From 17ccf92dc647e675d08f2185e35211dd8097d62a Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Wed, 15 Sep 2021 11:34:03 -0400 Subject: [PATCH 01/30] add clang-format & ignore vscode folder --- .gitignore | 5 +- cmake/.clang-format | 9 + cmake/cuda_compute_capability.cpp | 91 ++++----- src/.clang-format | 9 + src/main.cpp | 308 ++++++++++++++++-------------- src/testing_helpers.hpp | 84 ++++---- stream_compaction/.clang-format | 9 + stream_compaction/common.cu | 57 +++--- stream_compaction/common.h | 223 ++++++++++----------- stream_compaction/cpu.cu | 82 ++++---- stream_compaction/cpu.h | 14 +- stream_compaction/efficient.cu | 62 +++--- stream_compaction/efficient.h | 12 +- stream_compaction/naive.cu | 34 ++-- stream_compaction/naive.h | 10 +- stream_compaction/thrust.cu | 36 ++-- stream_compaction/thrust.h | 10 +- 17 files changed, 556 insertions(+), 499 deletions(-) create mode 100644 cmake/.clang-format create mode 100644 src/.clang-format create mode 100644 stream_compaction/.clang-format diff --git a/.gitignore b/.gitignore index a59ec56..30af9f3 100644 --- a/.gitignore +++ b/.gitignore @@ -25,7 +25,8 @@ build .LSOverride # Icon must end with two \r -Icon +Icon + # Thumbnails ._* @@ -560,3 +561,5 @@ xcuserdata *.xccheckout *.moved-aside *.xcuserstate + +.vscode diff --git a/cmake/.clang-format b/cmake/.clang-format new file mode 100644 index 0000000..618314c --- /dev/null +++ b/cmake/.clang-format @@ -0,0 +1,9 @@ +--- +BasedOnStyle: Google +--- +Language: Cpp +AccessModifierOffset: -2 +AlignConsecutiveAssignments: true +AlignConsecutiveMacros: true +--- + diff --git a/cmake/cuda_compute_capability.cpp b/cmake/cuda_compute_capability.cpp index ef589a9..0af4901 100644 --- a/cmake/cuda_compute_capability.cpp +++ b/cmake/cuda_compute_capability.cpp @@ -1,58 +1,59 @@ /* -* Copyright (C) 2011 Florian Rathgeber, florian.rathgeber@gmail.com -* -* This code is licensed under the MIT License. See the FindCUDA.cmake script -* for the text of the license. -* -* Based on code by Christopher Bruns published on Stack Overflow (CC-BY): -* http://stackoverflow.com/questions/2285185 -*/ + * Copyright (C) 2011 Florian Rathgeber, florian.rathgeber@gmail.com + * + * This code is licensed under the MIT License. See the FindCUDA.cmake script + * for the text of the license. + * + * Based on code by Christopher Bruns published on Stack Overflow (CC-BY): + * http://stackoverflow.com/questions/2285185 + */ -#include #include +#include + #include #include int main() { - int deviceCount; - int gpuDeviceCount = 0; - struct cudaDeviceProp properties; + int deviceCount; + int gpuDeviceCount = 0; + struct cudaDeviceProp properties; - if (cudaGetDeviceCount(&deviceCount) != cudaSuccess) - { - printf("Couldn't get device count: %s\n", cudaGetErrorString(cudaGetLastError())); - return 1; - } + if (cudaGetDeviceCount(&deviceCount) != cudaSuccess) { + printf("Couldn't get device count: %s\n", + cudaGetErrorString(cudaGetLastError())); + return 1; + } - std::set computes; - typedef std::set::iterator iter; + std::set computes; + typedef std::set::iterator iter; - // machines with no GPUs can still report one emulation device - for (int device = 0; device < deviceCount; ++device) { - int major = 9999, minor = 9999; - cudaGetDeviceProperties(&properties, device); - if (properties.major != 9999) { // 9999 means emulation only - ++gpuDeviceCount; - major = properties.major; - minor = properties.minor; - if ((major == 2 && minor == 1)) { - // There is no --arch compute_21 flag for nvcc, so force minor to 0 - minor = 0; - } - computes.insert(10 * major + minor); - } - } - int i = 0; - for(iter it = computes.begin(); it != computes.end(); it++, i++) { - if(i > 0) { - printf(" "); - } - printf("%d", *it); + // machines with no GPUs can still report one emulation device + for (int device = 0; device < deviceCount; ++device) { + int major = 9999, minor = 9999; + cudaGetDeviceProperties(&properties, device); + if (properties.major != 9999) { // 9999 means emulation only + ++gpuDeviceCount; + major = properties.major; + minor = properties.minor; + if ((major == 2 && minor == 1)) { + // There is no --arch compute_21 flag for nvcc, so force minor to 0 + minor = 0; + } + computes.insert(10 * major + minor); } - /* don't just return the number of gpus, because other runtime cuda - errors can also yield non-zero return values */ - if (gpuDeviceCount <= 0 || computes.size() <= 0) { - return 1; // failure + } + int i = 0; + for (iter it = computes.begin(); it != computes.end(); it++, i++) { + if (i > 0) { + printf(" "); } - return 0; // success + printf("%d", *it); + } + /* don't just return the number of gpus, because other runtime cuda + errors can also yield non-zero return values */ + if (gpuDeviceCount <= 0 || computes.size() <= 0) { + return 1; // failure + } + return 0; // success } diff --git a/src/.clang-format b/src/.clang-format new file mode 100644 index 0000000..618314c --- /dev/null +++ b/src/.clang-format @@ -0,0 +1,9 @@ +--- +BasedOnStyle: Google +--- +Language: Cpp +AccessModifierOffset: -2 +AlignConsecutiveAssignments: true +AlignConsecutiveMacros: true +--- + diff --git a/src/main.cpp b/src/main.cpp index 896ac2b..cb88466 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -6,149 +6,177 @@ * @copyright University of Pennsylvania */ -#include #include -#include #include +#include #include + +#include + #include "testing_helpers.hpp" -const int SIZE = 1 << 8; // feel free to change the size of array -const int NPOT = SIZE - 3; // Non-Power-Of-Two -int *a = new int[SIZE]; -int *b = new int[SIZE]; -int *c = new int[SIZE]; - -int main(int argc, char* argv[]) { - // Scan tests - - printf("\n"); - printf("****************\n"); - printf("** SCAN TESTS **\n"); - printf("****************\n"); - - genArray(SIZE - 1, a, 50); // Leave a 0 at the end to test that edge case - a[SIZE - 1] = 0; - printArray(SIZE, a, true); - - // initialize b using StreamCompaction::CPU::scan you implement - // We use b for further comparison. Make sure your StreamCompaction::CPU::scan is correct. - // At first all cases passed because b && c are all zeroes. - zeroArray(SIZE, b); - printDesc("cpu scan, power-of-two"); - StreamCompaction::CPU::scan(SIZE, b, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(SIZE, b, true); - - zeroArray(SIZE, c); - printDesc("cpu scan, non-power-of-two"); - StreamCompaction::CPU::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(NPOT, b, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("naive scan, power-of-two"); - StreamCompaction::Naive::scan(SIZE, c, a); - printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - /* For bug-finding only: Array of 1s to help find bugs in stream compaction or scan - onesArray(SIZE, c); - printDesc("1s array for finding bugs"); - StreamCompaction::Naive::scan(SIZE, c, a); - printArray(SIZE, c, true); */ - - zeroArray(SIZE, c); - printDesc("naive scan, non-power-of-two"); - StreamCompaction::Naive::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient scan, power-of-two"); - StreamCompaction::Efficient::scan(SIZE, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient scan, non-power-of-two"); - StreamCompaction::Efficient::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(NPOT, c, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("thrust scan, power-of-two"); - StreamCompaction::Thrust::scan(SIZE, c, a); - printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - zeroArray(SIZE, c); - printDesc("thrust scan, non-power-of-two"); - StreamCompaction::Thrust::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(NPOT, c, true); - printCmpResult(NPOT, b, c); - - printf("\n"); - printf("*****************************\n"); - printf("** STREAM COMPACTION TESTS **\n"); - printf("*****************************\n"); - - // Compaction tests - - genArray(SIZE - 1, a, 4); // Leave a 0 at the end to test that edge case - a[SIZE - 1] = 0; - printArray(SIZE, a, true); - - int count, expectedCount, expectedNPOT; - - // initialize b using StreamCompaction::CPU::compactWithoutScan you implement - // We use b for further comparison. Make sure your StreamCompaction::CPU::compactWithoutScan is correct. - zeroArray(SIZE, b); - printDesc("cpu compact without scan, power-of-two"); - count = StreamCompaction::CPU::compactWithoutScan(SIZE, b, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - expectedCount = count; - printArray(count, b, true); - printCmpLenResult(count, expectedCount, b, b); - - zeroArray(SIZE, c); - printDesc("cpu compact without scan, non-power-of-two"); - count = StreamCompaction::CPU::compactWithoutScan(NPOT, c, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - expectedNPOT = count; - printArray(count, c, true); - printCmpLenResult(count, expectedNPOT, b, c); - - zeroArray(SIZE, c); - printDesc("cpu compact with scan"); - count = StreamCompaction::CPU::compactWithScan(SIZE, c, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(count, c, true); - printCmpLenResult(count, expectedCount, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient compact, power-of-two"); - count = StreamCompaction::Efficient::compact(SIZE, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(count, c, true); - printCmpLenResult(count, expectedCount, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient compact, non-power-of-two"); - count = StreamCompaction::Efficient::compact(NPOT, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(count, c, true); - printCmpLenResult(count, expectedNPOT, b, c); - - system("pause"); // stop Win32 console from closing on exit - delete[] a; - delete[] b; - delete[] c; +const int SIZE = 1 << 8; // feel free to change the size of array +const int NPOT = SIZE - 3; // Non-Power-Of-Two +int *a = new int[SIZE]; +int *b = new int[SIZE]; +int *c = new int[SIZE]; + +int main(int argc, char *argv[]) { + // Scan tests + + printf("\n"); + printf("****************\n"); + printf("** SCAN TESTS **\n"); + printf("****************\n"); + + genArray(SIZE - 1, a, 50); // Leave a 0 at the end to test that edge case + a[SIZE - 1] = 0; + printArray(SIZE, a, true); + + // initialize b using StreamCompaction::CPU::scan you implement + // We use b for further comparison. Make sure your StreamCompaction::CPU::scan + // is correct. At first all cases passed because b && c are all zeroes. + zeroArray(SIZE, b); + printDesc("cpu scan, power-of-two"); + StreamCompaction::CPU::scan(SIZE, b, a); + printElapsedTime( + StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), + "(std::chrono Measured)"); + printArray(SIZE, b, true); + + zeroArray(SIZE, c); + printDesc("cpu scan, non-power-of-two"); + StreamCompaction::CPU::scan(NPOT, c, a); + printElapsedTime( + StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), + "(std::chrono Measured)"); + printArray(NPOT, b, true); + printCmpResult(NPOT, b, c); + + zeroArray(SIZE, c); + printDesc("naive scan, power-of-two"); + StreamCompaction::Naive::scan(SIZE, c, a); + printElapsedTime( + StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), + "(CUDA Measured)"); + // printArray(SIZE, c, true); + printCmpResult(SIZE, b, c); + + /* For bug-finding only: Array of 1s to help find bugs in stream compaction or + scan onesArray(SIZE, c); printDesc("1s array for finding bugs"); + StreamCompaction::Naive::scan(SIZE, c, a); + printArray(SIZE, c, true); */ + + zeroArray(SIZE, c); + printDesc("naive scan, non-power-of-two"); + StreamCompaction::Naive::scan(NPOT, c, a); + printElapsedTime( + StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), + "(CUDA Measured)"); + // printArray(SIZE, c, true); + printCmpResult(NPOT, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient scan, power-of-two"); + StreamCompaction::Efficient::scan(SIZE, c, a); + printElapsedTime(StreamCompaction::Efficient::timer() + .getGpuElapsedTimeForPreviousOperation(), + "(CUDA Measured)"); + // printArray(SIZE, c, true); + printCmpResult(SIZE, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient scan, non-power-of-two"); + StreamCompaction::Efficient::scan(NPOT, c, a); + printElapsedTime(StreamCompaction::Efficient::timer() + .getGpuElapsedTimeForPreviousOperation(), + "(CUDA Measured)"); + // printArray(NPOT, c, true); + printCmpResult(NPOT, b, c); + + zeroArray(SIZE, c); + printDesc("thrust scan, power-of-two"); + StreamCompaction::Thrust::scan(SIZE, c, a); + printElapsedTime( + StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), + "(CUDA Measured)"); + // printArray(SIZE, c, true); + printCmpResult(SIZE, b, c); + + zeroArray(SIZE, c); + printDesc("thrust scan, non-power-of-two"); + StreamCompaction::Thrust::scan(NPOT, c, a); + printElapsedTime( + StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), + "(CUDA Measured)"); + // printArray(NPOT, c, true); + printCmpResult(NPOT, b, c); + + printf("\n"); + printf("*****************************\n"); + printf("** STREAM COMPACTION TESTS **\n"); + printf("*****************************\n"); + + // Compaction tests + + genArray(SIZE - 1, a, 4); // Leave a 0 at the end to test that edge case + a[SIZE - 1] = 0; + printArray(SIZE, a, true); + + int count, expectedCount, expectedNPOT; + + // initialize b using StreamCompaction::CPU::compactWithoutScan you implement + // We use b for further comparison. Make sure your + // StreamCompaction::CPU::compactWithoutScan is correct. + zeroArray(SIZE, b); + printDesc("cpu compact without scan, power-of-two"); + count = StreamCompaction::CPU::compactWithoutScan(SIZE, b, a); + printElapsedTime( + StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), + "(std::chrono Measured)"); + expectedCount = count; + printArray(count, b, true); + printCmpLenResult(count, expectedCount, b, b); + + zeroArray(SIZE, c); + printDesc("cpu compact without scan, non-power-of-two"); + count = StreamCompaction::CPU::compactWithoutScan(NPOT, c, a); + printElapsedTime( + StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), + "(std::chrono Measured)"); + expectedNPOT = count; + printArray(count, c, true); + printCmpLenResult(count, expectedNPOT, b, c); + + zeroArray(SIZE, c); + printDesc("cpu compact with scan"); + count = StreamCompaction::CPU::compactWithScan(SIZE, c, a); + printElapsedTime( + StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), + "(std::chrono Measured)"); + printArray(count, c, true); + printCmpLenResult(count, expectedCount, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient compact, power-of-two"); + count = StreamCompaction::Efficient::compact(SIZE, c, a); + printElapsedTime(StreamCompaction::Efficient::timer() + .getGpuElapsedTimeForPreviousOperation(), + "(CUDA Measured)"); + // printArray(count, c, true); + printCmpLenResult(count, expectedCount, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient compact, non-power-of-two"); + count = StreamCompaction::Efficient::compact(NPOT, c, a); + printElapsedTime(StreamCompaction::Efficient::timer() + .getGpuElapsedTimeForPreviousOperation(), + "(CUDA Measured)"); + // printArray(count, c, true); + printCmpLenResult(count, expectedNPOT, b, c); + + system("pause"); // stop Win32 console from closing on exit + delete[] a; + delete[] b; + delete[] c; } diff --git a/src/testing_helpers.hpp b/src/testing_helpers.hpp index 025e94a..bd560a4 100644 --- a/src/testing_helpers.hpp +++ b/src/testing_helpers.hpp @@ -1,76 +1,72 @@ #pragma once -#include #include +#include +#include #include #include -#include -template +template int cmpArrays(int n, T *a, T *b) { - for (int i = 0; i < n; i++) { - if (a[i] != b[i]) { - printf(" a[%d] = %d, b[%d] = %d\n", i, a[i], i, b[i]); - return 1; - } + for (int i = 0; i < n; i++) { + if (a[i] != b[i]) { + printf(" a[%d] = %d, b[%d] = %d\n", i, a[i], i, b[i]); + return 1; } - return 0; + } + return 0; } -void printDesc(const char *desc) { - printf("==== %s ====\n", desc); -} +void printDesc(const char *desc) { printf("==== %s ====\n", desc); } -template +template void printCmpResult(int n, T *a, T *b) { - printf(" %s \n", - cmpArrays(n, a, b) ? "FAIL VALUE" : "passed"); + printf(" %s \n", cmpArrays(n, a, b) ? "FAIL VALUE" : "passed"); } -template +template void printCmpLenResult(int n, int expN, T *a, T *b) { - if (n != expN) { - printf(" expected %d elements, got %d\n", expN, n); - } - printf(" %s \n", - (n == -1 || n != expN) ? "FAIL COUNT" : - cmpArrays(n, a, b) ? "FAIL VALUE" : "passed"); + if (n != expN) { + printf(" expected %d elements, got %d\n", expN, n); + } + printf(" %s \n", (n == -1 || n != expN) ? "FAIL COUNT" + : cmpArrays(n, a, b) ? "FAIL VALUE" + : "passed"); } void zeroArray(int n, int *a) { - for (int i = 0; i < n; i++) { - a[i] = 0; - } + for (int i = 0; i < n; i++) { + a[i] = 0; + } } void onesArray(int n, int *a) { - for (int i = 0; i < n; i++) { - a[i] = 1; - } + for (int i = 0; i < n; i++) { + a[i] = 1; + } } void genArray(int n, int *a, int maxval) { - srand(time(nullptr)); + srand(time(nullptr)); - for (int i = 0; i < n; i++) { - a[i] = rand() % maxval; - } + for (int i = 0; i < n; i++) { + a[i] = rand() % maxval; + } } void printArray(int n, int *a, bool abridged = false) { - printf(" [ "); - for (int i = 0; i < n; i++) { - if (abridged && i + 2 == 15 && n > 16) { - i = n - 2; - printf("... "); - } - printf("%3d ", a[i]); + printf(" [ "); + for (int i = 0; i < n; i++) { + if (abridged && i + 2 == 15 && n > 16) { + i = n - 2; + printf("... "); } - printf("]\n"); + printf("%3d ", a[i]); + } + printf("]\n"); } -template -void printElapsedTime(T time, std::string note = "") -{ - std::cout << " elapsed time: " << time << "ms " << note << std::endl; +template +void printElapsedTime(T time, std::string note = "") { + std::cout << " elapsed time: " << time << "ms " << note << std::endl; } diff --git a/stream_compaction/.clang-format b/stream_compaction/.clang-format new file mode 100644 index 0000000..618314c --- /dev/null +++ b/stream_compaction/.clang-format @@ -0,0 +1,9 @@ +--- +BasedOnStyle: Google +--- +Language: Cpp +AccessModifierOffset: -2 +AlignConsecutiveAssignments: true +AlignConsecutiveMacros: true +--- + diff --git a/stream_compaction/common.cu b/stream_compaction/common.cu index 2ed6d63..2acbef7 100644 --- a/stream_compaction/common.cu +++ b/stream_compaction/common.cu @@ -1,39 +1,38 @@ #include "common.h" void checkCUDAErrorFn(const char *msg, const char *file, int line) { - cudaError_t err = cudaGetLastError(); - if (cudaSuccess == err) { - return; - } + cudaError_t err = cudaGetLastError(); + if (cudaSuccess == err) { + return; + } - fprintf(stderr, "CUDA error"); - if (file) { - fprintf(stderr, " (%s:%d)", file, line); - } - fprintf(stderr, ": %s: %s\n", msg, cudaGetErrorString(err)); - exit(EXIT_FAILURE); + fprintf(stderr, "CUDA error"); + if (file) { + fprintf(stderr, " (%s:%d)", file, line); + } + fprintf(stderr, ": %s: %s\n", msg, cudaGetErrorString(err)); + exit(EXIT_FAILURE); } - namespace StreamCompaction { - namespace Common { - - /** - * Maps an array to an array of 0s and 1s for stream compaction. Elements - * which map to 0 will be removed, and elements which map to 1 will be kept. - */ - __global__ void kernMapToBoolean(int n, int *bools, const int *idata) { - // TODO - } +namespace Common { - /** - * Performs scatter on an array. That is, for each element in idata, - * if bools[idx] == 1, it copies idata[idx] to odata[indices[idx]]. - */ - __global__ void kernScatter(int n, int *odata, - const int *idata, const int *bools, const int *indices) { - // TODO - } +/** + * Maps an array to an array of 0s and 1s for stream compaction. Elements + * which map to 0 will be removed, and elements which map to 1 will be kept. + */ +__global__ void kernMapToBoolean(int n, int *bools, const int *idata) { + // TODO +} - } +/** + * Performs scatter on an array. That is, for each element in idata, + * if bools[idx] == 1, it copies idata[idx] to odata[indices[idx]]. + */ +__global__ void kernScatter(int n, int *odata, const int *idata, + const int *bools, const int *indices) { + // TODO } + +} // namespace Common +} // namespace StreamCompaction diff --git a/stream_compaction/common.h b/stream_compaction/common.h index d2c1fed..4a95d11 100644 --- a/stream_compaction/common.h +++ b/stream_compaction/common.h @@ -3,14 +3,15 @@ #include #include -#include -#include -#include #include #include +#include +#include +#include #include -#define FILENAME (strrchr(__FILE__, '/') ? strrchr(__FILE__, '/') + 1 : __FILE__) +#define FILENAME \ + (strrchr(__FILE__, '/') ? strrchr(__FILE__, '/') + 1 : __FILE__) #define checkCUDAError(msg) checkCUDAErrorFn(msg, FILENAME, __LINE__) /** @@ -19,114 +20,116 @@ void checkCUDAErrorFn(const char *msg, const char *file = NULL, int line = -1); inline int ilog2(int x) { - int lg = 0; - while (x >>= 1) { - ++lg; - } - return lg; + int lg = 0; + while (x >>= 1) { + ++lg; + } + return lg; } -inline int ilog2ceil(int x) { - return x == 1 ? 0 : ilog2(x - 1) + 1; -} +inline int ilog2ceil(int x) { return x == 1 ? 0 : ilog2(x - 1) + 1; } namespace StreamCompaction { - namespace Common { - __global__ void kernMapToBoolean(int n, int *bools, const int *idata); - - __global__ void kernScatter(int n, int *odata, - const int *idata, const int *bools, const int *indices); - - /** - * This class is used for timing the performance - * Uncopyable and unmovable - * - * Adapted from WindyDarian(https://github.com/WindyDarian) - */ - class PerformanceTimer - { - public: - PerformanceTimer() - { - cudaEventCreate(&event_start); - cudaEventCreate(&event_end); - } - - ~PerformanceTimer() - { - cudaEventDestroy(event_start); - cudaEventDestroy(event_end); - } - - void startCpuTimer() - { - if (cpu_timer_started) { throw std::runtime_error("CPU timer already started"); } - cpu_timer_started = true; - - time_start_cpu = std::chrono::high_resolution_clock::now(); - } - - void endCpuTimer() - { - time_end_cpu = std::chrono::high_resolution_clock::now(); - - if (!cpu_timer_started) { throw std::runtime_error("CPU timer not started"); } - - std::chrono::duration duro = time_end_cpu - time_start_cpu; - prev_elapsed_time_cpu_milliseconds = - static_cast(duro.count()); - - cpu_timer_started = false; - } - - void startGpuTimer() - { - if (gpu_timer_started) { throw std::runtime_error("GPU timer already started"); } - gpu_timer_started = true; - - cudaEventRecord(event_start); - } - - void endGpuTimer() - { - cudaEventRecord(event_end); - cudaEventSynchronize(event_end); - - if (!gpu_timer_started) { throw std::runtime_error("GPU timer not started"); } - - cudaEventElapsedTime(&prev_elapsed_time_gpu_milliseconds, event_start, event_end); - gpu_timer_started = false; - } - - float getCpuElapsedTimeForPreviousOperation() //noexcept //(damn I need VS 2015 - { - return prev_elapsed_time_cpu_milliseconds; - } - - float getGpuElapsedTimeForPreviousOperation() //noexcept - { - return prev_elapsed_time_gpu_milliseconds; - } - - // remove copy and move functions - PerformanceTimer(const PerformanceTimer&) = delete; - PerformanceTimer(PerformanceTimer&&) = delete; - PerformanceTimer& operator=(const PerformanceTimer&) = delete; - PerformanceTimer& operator=(PerformanceTimer&&) = delete; - - private: - cudaEvent_t event_start = nullptr; - cudaEvent_t event_end = nullptr; - - using time_point_t = std::chrono::high_resolution_clock::time_point; - time_point_t time_start_cpu; - time_point_t time_end_cpu; - - bool cpu_timer_started = false; - bool gpu_timer_started = false; - - float prev_elapsed_time_cpu_milliseconds = 0.f; - float prev_elapsed_time_gpu_milliseconds = 0.f; - }; +namespace Common { +__global__ void kernMapToBoolean(int n, int *bools, const int *idata); + +__global__ void kernScatter(int n, int *odata, const int *idata, + const int *bools, const int *indices); + +/** + * This class is used for timing the performance + * Uncopyable and unmovable + * + * Adapted from WindyDarian(https://github.com/WindyDarian) + */ +class PerformanceTimer { +public: + PerformanceTimer() { + cudaEventCreate(&event_start); + cudaEventCreate(&event_end); + } + + ~PerformanceTimer() { + cudaEventDestroy(event_start); + cudaEventDestroy(event_end); + } + + void startCpuTimer() { + if (cpu_timer_started) { + throw std::runtime_error("CPU timer already started"); } -} + cpu_timer_started = true; + + time_start_cpu = std::chrono::high_resolution_clock::now(); + } + + void endCpuTimer() { + time_end_cpu = std::chrono::high_resolution_clock::now(); + + if (!cpu_timer_started) { + throw std::runtime_error("CPU timer not started"); + } + + std::chrono::duration duro = + time_end_cpu - time_start_cpu; + prev_elapsed_time_cpu_milliseconds = + static_cast(duro.count()); + + cpu_timer_started = false; + } + + void startGpuTimer() { + if (gpu_timer_started) { + throw std::runtime_error("GPU timer already started"); + } + gpu_timer_started = true; + + cudaEventRecord(event_start); + } + + void endGpuTimer() { + cudaEventRecord(event_end); + cudaEventSynchronize(event_end); + + if (!gpu_timer_started) { + throw std::runtime_error("GPU timer not started"); + } + + cudaEventElapsedTime(&prev_elapsed_time_gpu_milliseconds, event_start, + event_end); + gpu_timer_started = false; + } + + float + getCpuElapsedTimeForPreviousOperation() // noexcept //(damn I need VS 2015 + { + return prev_elapsed_time_cpu_milliseconds; + } + + float getGpuElapsedTimeForPreviousOperation() // noexcept + { + return prev_elapsed_time_gpu_milliseconds; + } + + // remove copy and move functions + PerformanceTimer(const PerformanceTimer &) = delete; + PerformanceTimer(PerformanceTimer &&) = delete; + PerformanceTimer &operator=(const PerformanceTimer &) = delete; + PerformanceTimer &operator=(PerformanceTimer &&) = delete; + +private: + cudaEvent_t event_start = nullptr; + cudaEvent_t event_end = nullptr; + + using time_point_t = std::chrono::high_resolution_clock::time_point; + time_point_t time_start_cpu; + time_point_t time_end_cpu; + + bool cpu_timer_started = false; + bool gpu_timer_started = false; + + float prev_elapsed_time_cpu_milliseconds = 0.f; + float prev_elapsed_time_gpu_milliseconds = 0.f; +}; +} // namespace Common +} // namespace StreamCompaction diff --git a/stream_compaction/cpu.cu b/stream_compaction/cpu.cu index 719fa11..49df4f0 100644 --- a/stream_compaction/cpu.cu +++ b/stream_compaction/cpu.cu @@ -1,50 +1,50 @@ #include -#include "cpu.h" #include "common.h" +#include "cpu.h" namespace StreamCompaction { - namespace CPU { - using StreamCompaction::Common::PerformanceTimer; - PerformanceTimer& timer() - { - static PerformanceTimer timer; - return timer; - } +namespace CPU { +using StreamCompaction::Common::PerformanceTimer; +PerformanceTimer &timer() { + static PerformanceTimer timer; + return timer; +} - /** - * CPU scan (prefix sum). - * For performance analysis, this is supposed to be a simple for loop. - * (Optional) For better understanding before starting moving to GPU, you can simulate your GPU scan in this function first. - */ - void scan(int n, int *odata, const int *idata) { - timer().startCpuTimer(); - // TODO - timer().endCpuTimer(); - } +/** + * CPU scan (prefix sum). + * For performance analysis, this is supposed to be a simple for loop. + * (Optional) For better understanding before starting moving to GPU, you can + * simulate your GPU scan in this function first. + */ +void scan(int n, int *odata, const int *idata) { + timer().startCpuTimer(); + // TODO + timer().endCpuTimer(); +} - /** - * CPU stream compaction without using the scan function. - * - * @returns the number of elements remaining after compaction. - */ - int compactWithoutScan(int n, int *odata, const int *idata) { - timer().startCpuTimer(); - // TODO - timer().endCpuTimer(); - return -1; - } +/** + * CPU stream compaction without using the scan function. + * + * @returns the number of elements remaining after compaction. + */ +int compactWithoutScan(int n, int *odata, const int *idata) { + timer().startCpuTimer(); + // TODO + timer().endCpuTimer(); + return -1; +} - /** - * CPU stream compaction using scan and scatter, like the parallel version. - * - * @returns the number of elements remaining after compaction. - */ - int compactWithScan(int n, int *odata, const int *idata) { - timer().startCpuTimer(); - // TODO - timer().endCpuTimer(); - return -1; - } - } +/** + * CPU stream compaction using scan and scatter, like the parallel version. + * + * @returns the number of elements remaining after compaction. + */ +int compactWithScan(int n, int *odata, const int *idata) { + timer().startCpuTimer(); + // TODO + timer().endCpuTimer(); + return -1; } +} // namespace CPU +} // namespace StreamCompaction diff --git a/stream_compaction/cpu.h b/stream_compaction/cpu.h index 873c047..7258098 100644 --- a/stream_compaction/cpu.h +++ b/stream_compaction/cpu.h @@ -3,13 +3,13 @@ #include "common.h" namespace StreamCompaction { - namespace CPU { - StreamCompaction::Common::PerformanceTimer& timer(); +namespace CPU { +StreamCompaction::Common::PerformanceTimer &timer(); - void scan(int n, int *odata, const int *idata); +void scan(int n, int *odata, const int *idata); - int compactWithoutScan(int n, int *odata, const int *idata); +int compactWithoutScan(int n, int *odata, const int *idata); - int compactWithScan(int n, int *odata, const int *idata); - } -} +int compactWithScan(int n, int *odata, const int *idata); +} // namespace CPU +} // namespace StreamCompaction diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index 2db346e..bb16a6d 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -1,40 +1,40 @@ #include #include + #include "common.h" #include "efficient.h" namespace StreamCompaction { - namespace Efficient { - using StreamCompaction::Common::PerformanceTimer; - PerformanceTimer& timer() - { - static PerformanceTimer timer; - return timer; - } +namespace Efficient { +using StreamCompaction::Common::PerformanceTimer; +PerformanceTimer &timer() { + static PerformanceTimer timer; + return timer; +} - /** - * Performs prefix-sum (aka scan) on idata, storing the result into odata. - */ - void scan(int n, int *odata, const int *idata) { - timer().startGpuTimer(); - // TODO - timer().endGpuTimer(); - } +/** + * Performs prefix-sum (aka scan) on idata, storing the result into odata. + */ +void scan(int n, int *odata, const int *idata) { + timer().startGpuTimer(); + // TODO + timer().endGpuTimer(); +} - /** - * Performs stream compaction on idata, storing the result into odata. - * All zeroes are discarded. - * - * @param n The number of elements in idata. - * @param odata The array into which to store elements. - * @param idata The array of elements to compact. - * @returns The number of elements remaining after compaction. - */ - int compact(int n, int *odata, const int *idata) { - timer().startGpuTimer(); - // TODO - timer().endGpuTimer(); - return -1; - } - } +/** + * Performs stream compaction on idata, storing the result into odata. + * All zeroes are discarded. + * + * @param n The number of elements in idata. + * @param odata The array into which to store elements. + * @param idata The array of elements to compact. + * @returns The number of elements remaining after compaction. + */ +int compact(int n, int *odata, const int *idata) { + timer().startGpuTimer(); + // TODO + timer().endGpuTimer(); + return -1; } +} // namespace Efficient +} // namespace StreamCompaction diff --git a/stream_compaction/efficient.h b/stream_compaction/efficient.h index 803cb4f..0638439 100644 --- a/stream_compaction/efficient.h +++ b/stream_compaction/efficient.h @@ -3,11 +3,11 @@ #include "common.h" namespace StreamCompaction { - namespace Efficient { - StreamCompaction::Common::PerformanceTimer& timer(); +namespace Efficient { +StreamCompaction::Common::PerformanceTimer &timer(); - void scan(int n, int *odata, const int *idata); +void scan(int n, int *odata, const int *idata); - int compact(int n, int *odata, const int *idata); - } -} +int compact(int n, int *odata, const int *idata); +} // namespace Efficient +} // namespace StreamCompaction diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index 4308876..91b7a14 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -1,25 +1,25 @@ #include #include + #include "common.h" #include "naive.h" namespace StreamCompaction { - namespace Naive { - using StreamCompaction::Common::PerformanceTimer; - PerformanceTimer& timer() - { - static PerformanceTimer timer; - return timer; - } - // TODO: __global__ +namespace Naive { +using StreamCompaction::Common::PerformanceTimer; +PerformanceTimer &timer() { + static PerformanceTimer timer; + return timer; +} +// TODO: __global__ - /** - * Performs prefix-sum (aka scan) on idata, storing the result into odata. - */ - void scan(int n, int *odata, const int *idata) { - timer().startGpuTimer(); - // TODO - timer().endGpuTimer(); - } - } +/** + * Performs prefix-sum (aka scan) on idata, storing the result into odata. + */ +void scan(int n, int *odata, const int *idata) { + timer().startGpuTimer(); + // TODO + timer().endGpuTimer(); } +} // namespace Naive +} // namespace StreamCompaction diff --git a/stream_compaction/naive.h b/stream_compaction/naive.h index 37dcb06..9351fa9 100644 --- a/stream_compaction/naive.h +++ b/stream_compaction/naive.h @@ -3,9 +3,9 @@ #include "common.h" namespace StreamCompaction { - namespace Naive { - StreamCompaction::Common::PerformanceTimer& timer(); +namespace Naive { +StreamCompaction::Common::PerformanceTimer &timer(); - void scan(int n, int *odata, const int *idata); - } -} +void scan(int n, int *odata, const int *idata); +} // namespace Naive +} // namespace StreamCompaction diff --git a/stream_compaction/thrust.cu b/stream_compaction/thrust.cu index 1def45e..e726435 100644 --- a/stream_compaction/thrust.cu +++ b/stream_compaction/thrust.cu @@ -3,26 +3,26 @@ #include #include #include + #include "common.h" #include "thrust.h" namespace StreamCompaction { - namespace Thrust { - using StreamCompaction::Common::PerformanceTimer; - PerformanceTimer& timer() - { - static PerformanceTimer timer; - return timer; - } - /** - * Performs prefix-sum (aka scan) on idata, storing the result into odata. - */ - void scan(int n, int *odata, const int *idata) { - timer().startGpuTimer(); - // TODO use `thrust::exclusive_scan` - // example: for device_vectors dv_in and dv_out: - // thrust::exclusive_scan(dv_in.begin(), dv_in.end(), dv_out.begin()); - timer().endGpuTimer(); - } - } +namespace Thrust { +using StreamCompaction::Common::PerformanceTimer; +PerformanceTimer &timer() { + static PerformanceTimer timer; + return timer; +} +/** + * Performs prefix-sum (aka scan) on idata, storing the result into odata. + */ +void scan(int n, int *odata, const int *idata) { + timer().startGpuTimer(); + // TODO use `thrust::exclusive_scan` + // example: for device_vectors dv_in and dv_out: + // thrust::exclusive_scan(dv_in.begin(), dv_in.end(), dv_out.begin()); + timer().endGpuTimer(); } +} // namespace Thrust +} // namespace StreamCompaction diff --git a/stream_compaction/thrust.h b/stream_compaction/thrust.h index fe98206..697a202 100644 --- a/stream_compaction/thrust.h +++ b/stream_compaction/thrust.h @@ -3,9 +3,9 @@ #include "common.h" namespace StreamCompaction { - namespace Thrust { - StreamCompaction::Common::PerformanceTimer& timer(); +namespace Thrust { +StreamCompaction::Common::PerformanceTimer &timer(); - void scan(int n, int *odata, const int *idata); - } -} +void scan(int n, int *odata, const int *idata); +} // namespace Thrust +} // namespace StreamCompaction From 466bb30f92041d1391160057ee1a93417eb485b9 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Wed, 15 Sep 2021 15:31:37 -0400 Subject: [PATCH 02/30] add _WIN32 guard around `system("pause");` --- src/main.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/main.cpp b/src/main.cpp index cb88466..dae47a9 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -175,7 +175,10 @@ int main(int argc, char *argv[]) { // printArray(count, c, true); printCmpLenResult(count, expectedNPOT, b, c); +#if defined(_WIN32) system("pause"); // stop Win32 console from closing on exit +#endif + delete[] a; delete[] b; delete[] c; From be9077ec51d96fc1bb572d0a9078b3d8fc77690f Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Wed, 15 Sep 2021 23:38:16 -0400 Subject: [PATCH 03/30] finish CPU scan & stream compaction --- stream_compaction/cpu.cu | 47 +++++++++++++++++++++++++++++++++++----- 1 file changed, 42 insertions(+), 5 deletions(-) diff --git a/stream_compaction/cpu.cu b/stream_compaction/cpu.cu index 49df4f0..5c8d6e6 100644 --- a/stream_compaction/cpu.cu +++ b/stream_compaction/cpu.cu @@ -11,6 +11,18 @@ PerformanceTimer &timer() { return timer; } +/** + * CPU scan core function. + * This function runs without starting CPU timer. + */ +void scan_core(int n, int *odata, const int *idata) { + int sum = 0; + for (int i = 0; i < n; ++i) { + odata[i] = sum; + sum += idata[i]; + } +} + /** * CPU scan (prefix sum). * For performance analysis, this is supposed to be a simple for loop. @@ -19,7 +31,7 @@ PerformanceTimer &timer() { */ void scan(int n, int *odata, const int *idata) { timer().startCpuTimer(); - // TODO + scan_core(n, odata, idata); timer().endCpuTimer(); } @@ -30,9 +42,14 @@ void scan(int n, int *odata, const int *idata) { */ int compactWithoutScan(int n, int *odata, const int *idata) { timer().startCpuTimer(); - // TODO + int outarray_len = 0; + for (int i = 0; i < n; ++i) { + if (idata[i] != 0) { + odata[outarray_len++] = idata[i]; + } + } timer().endCpuTimer(); - return -1; + return outarray_len; } /** @@ -42,9 +59,29 @@ int compactWithoutScan(int n, int *odata, const int *idata) { */ int compactWithScan(int n, int *odata, const int *idata) { timer().startCpuTimer(); - // TODO + + // create bool array b + int *b = (int *)malloc(n * sizeof(int)); + std::memset(b, 0, n * sizeof(int)); + for (int i = 0; i < n; ++i) { + if (idata[i] != 0) b[i] = 1; + } + + // exclusive scan bool array + int *scan_b = (int *)malloc(n * sizeof(int)); + scan_core(n, scan_b, b); + int outarray_len = b[n - 1] + scan_b[n - 1]; + + // copy selected array into out array + for (int i = 0; i < n; ++i) { + if (b[i]) odata[scan_b[i]] = idata[i]; + } + + free(b); + free(scan_b); + timer().endCpuTimer(); - return -1; + return outarray_len; } } // namespace CPU } // namespace StreamCompaction From 7c58e96c60dca15aa14e2c4ee451b1dd2f0a742b Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sat, 18 Sep 2021 03:39:49 -0400 Subject: [PATCH 04/30] WIP on naive parallel scan implementation --- stream_compaction/naive.cu | 50 ++++++++++++++++++++++++++++++++++++-- 1 file changed, 48 insertions(+), 2 deletions(-) diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index 91b7a14..6ad2309 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -11,15 +11,61 @@ PerformanceTimer &timer() { static PerformanceTimer timer; return timer; } -// TODO: __global__ + +template +__device__ void inline swap(T &a, T &b) { + T c(a); + a = b; + b = c; +} + +__global__ void kernScanExclusiveNaive(int n, int *idata, int *odata) { + int id = blockDim.x * blockIdx.x + threadIdx.x; + int tx = threadIdx.x; + int bdim = blockDim.x; + int log2n = ilog2ceil((n < bdim) ? n : bdim); + + if (id < n) { + for (int d = 1; d <= log2n; ++d) { + if (tx >= (1 << (d - 1))) { + odata[id] = idata[id - (1 << (d - 1))] + idata[id]; + } + __syncthreads(); + idata[id] = odata[id]; + __syncthreads(); + } + + if (tx > 0) { + odata[id] = idata[id - 1]; + } else { + odata[id] = 0; + } + } +} /** * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ void scan(int n, int *odata, const int *idata) { + if (n <= 0) return; + + int *dev_idata, *dev_odata; + cudaMalloc((void **)&dev_idata, n * sizeof(int)); + cudaMalloc((void **)&dev_odata, n * sizeof(int)); + checkCUDAError("cudaMalloc failed for dev_idata and dev_odata !"); + + cudaMemcpy(dev_idata, idata, n * sizeof(int), cudaMemcpyHostToDevice); + checkCUDAError("cudaMemcpy failed for idata --> dev_idata!"); + timer().startGpuTimer(); - // TODO + dim3 dimGrid{1}, dimBlock{512}; + kernScanExclusiveNaive<<>>(n, dev_idata, dev_odata); timer().endGpuTimer(); + + cudaMemcpy(odata, dev_odata, n * sizeof(int), cudaMemcpyDeviceToHost); + + cudaFree(dev_idata); + cudaFree(dev_odata); } } // namespace Naive } // namespace StreamCompaction From 39ffd98037f2f296e769dab40e34fbf34f174992 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sat, 18 Sep 2021 03:40:13 -0400 Subject: [PATCH 05/30] add __host__ and __device__ specifier to ilog2ceil --- stream_compaction/common.h | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/stream_compaction/common.h b/stream_compaction/common.h index 4a95d11..413d995 100644 --- a/stream_compaction/common.h +++ b/stream_compaction/common.h @@ -19,7 +19,7 @@ */ void checkCUDAErrorFn(const char *msg, const char *file = NULL, int line = -1); -inline int ilog2(int x) { +inline __device__ __host__ int ilog2(int x) { int lg = 0; while (x >>= 1) { ++lg; @@ -27,7 +27,9 @@ inline int ilog2(int x) { return lg; } -inline int ilog2ceil(int x) { return x == 1 ? 0 : ilog2(x - 1) + 1; } +inline __device__ __host__ int ilog2ceil(int x) { + return x == 1 ? 0 : ilog2(x - 1) + 1; +} namespace StreamCompaction { namespace Common { From b553536297ac827dd9bf733d0ce748d42812014f Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sat, 18 Sep 2021 03:54:13 -0400 Subject: [PATCH 06/30] finish block-wise parallel scan kernel function --- stream_compaction/naive.cu | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index 6ad2309..25ddadf 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -27,11 +27,13 @@ __global__ void kernScanExclusiveNaive(int n, int *idata, int *odata) { if (id < n) { for (int d = 1; d <= log2n; ++d) { + odata[id] = idata[id]; + __syncthreads(); if (tx >= (1 << (d - 1))) { odata[id] = idata[id - (1 << (d - 1))] + idata[id]; } __syncthreads(); - idata[id] = odata[id]; + swap(idata, odata); __syncthreads(); } From 75e69001ac8f03d0b1444e2260a113e056aa3c77 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sat, 18 Sep 2021 03:54:25 -0400 Subject: [PATCH 07/30] enable printing of naive scan --- src/main.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/main.cpp b/src/main.cpp index dae47a9..50ff24d 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -59,7 +59,7 @@ int main(int argc, char *argv[]) { printElapsedTime( StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - // printArray(SIZE, c, true); + printArray(SIZE, c, true); printCmpResult(SIZE, b, c); /* For bug-finding only: Array of 1s to help find bugs in stream compaction or @@ -73,7 +73,7 @@ int main(int argc, char *argv[]) { printElapsedTime( StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - // printArray(SIZE, c, true); + printArray(SIZE, c, true); printCmpResult(NPOT, b, c); zeroArray(SIZE, c); From 43cf87f3207ab83cca2d159257aaac99a1bf1691 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sat, 18 Sep 2021 04:25:23 -0400 Subject: [PATCH 08/30] define global block_size; add cudaDeviceSync() --- stream_compaction/common.cu | 2 ++ stream_compaction/common.h | 3 +++ stream_compaction/naive.cu | 6 +++++- 3 files changed, 10 insertions(+), 1 deletion(-) diff --git a/stream_compaction/common.cu b/stream_compaction/common.cu index 2acbef7..0e44622 100644 --- a/stream_compaction/common.cu +++ b/stream_compaction/common.cu @@ -17,6 +17,8 @@ void checkCUDAErrorFn(const char *msg, const char *file, int line) { namespace StreamCompaction { namespace Common { +const unsigned int block_size = 256; + /** * Maps an array to an array of 0s and 1s for stream compaction. Elements * which map to 0 will be removed, and elements which map to 1 will be kept. diff --git a/stream_compaction/common.h b/stream_compaction/common.h index 413d995..00f4840 100644 --- a/stream_compaction/common.h +++ b/stream_compaction/common.h @@ -33,6 +33,9 @@ inline __device__ __host__ int ilog2ceil(int x) { namespace StreamCompaction { namespace Common { + +extern const unsigned int block_size; + __global__ void kernMapToBoolean(int n, int *bools, const int *idata); __global__ void kernScatter(int n, int *odata, const int *idata, diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index 25ddadf..fa65d3d 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -51,6 +51,9 @@ __global__ void kernScanExclusiveNaive(int n, int *idata, int *odata) { void scan(int n, int *odata, const int *idata) { if (n <= 0) return; + const unsigned int grid_size = + (n + Common::block_size - 1) / Common::block_size; + int *dev_idata, *dev_odata; cudaMalloc((void **)&dev_idata, n * sizeof(int)); cudaMalloc((void **)&dev_odata, n * sizeof(int)); @@ -60,10 +63,11 @@ void scan(int n, int *odata, const int *idata) { checkCUDAError("cudaMemcpy failed for idata --> dev_idata!"); timer().startGpuTimer(); - dim3 dimGrid{1}, dimBlock{512}; + dim3 dimGrid{grid_size}, dimBlock{Common::block_size}; kernScanExclusiveNaive<<>>(n, dev_idata, dev_odata); timer().endGpuTimer(); + cudaDeviceSynchronize(); cudaMemcpy(odata, dev_odata, n * sizeof(int), cudaMemcpyDeviceToHost); cudaFree(dev_idata); From d0d3de0270494333a45730cad626d08848992265 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sat, 18 Sep 2021 23:10:12 -0400 Subject: [PATCH 09/30] add handling of arbitrary length input on naive scan --- stream_compaction/common.cu | 32 +++++++++++++++++++++++++++ stream_compaction/common.h | 9 ++++++++ stream_compaction/naive.cu | 44 ++++++++++++++++++++++++++----------- 3 files changed, 72 insertions(+), 13 deletions(-) diff --git a/stream_compaction/common.cu b/stream_compaction/common.cu index 0e44622..0282b87 100644 --- a/stream_compaction/common.cu +++ b/stream_compaction/common.cu @@ -19,6 +19,38 @@ namespace Common { const unsigned int block_size = 256; +__global__ void kernExtractLastElementPerBlock(int n, int *odata, + const int *idata) { + int bid = blockIdx.x; + int tid = threadIdx.x; + int id = bid * blockDim.x + tid; + if (id < n) { + if (tid == blockDim.x - 1 || id == n - 1) { + odata[bid] = idata[id]; + } + } +} + +__global__ void kernAddOffsetPerBlock(int n, int *odata, + const int *block_offset, + const int *idata) { + int bid = blockIdx.x; + int id = bid * blockDim.x + threadIdx.x; + if (id < n) { + odata[id] = idata[id] + block_offset[bid]; + } +} + +__global__ void kernShiftToExclusive(int n, int *odata, const int *idata) { + int id = blockIdx.x * blockDim.x + threadIdx.x; + if (id < n) { + if (id == 0) + odata[id] = 0; + else + odata[id] = idata[id - 1]; + } +} + /** * Maps an array to an array of 0s and 1s for stream compaction. Elements * which map to 0 will be removed, and elements which map to 1 will be kept. diff --git a/stream_compaction/common.h b/stream_compaction/common.h index 00f4840..331fd4d 100644 --- a/stream_compaction/common.h +++ b/stream_compaction/common.h @@ -36,6 +36,15 @@ namespace Common { extern const unsigned int block_size; +__global__ void kernExtractLastElementPerBlock(int n, int *odata, + const int *idata); + +__global__ void kernAddOffsetPerBlock(int n, int *odata, + const int *block_offset, + const int *idata); + +__global__ void kernShiftToExclusive(int n, int *odata, const int *idata); + __global__ void kernMapToBoolean(int n, int *bools, const int *idata); __global__ void kernScatter(int n, int *odata, const int *idata, diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index fa65d3d..865d09f 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -19,7 +19,12 @@ __device__ void inline swap(T &a, T &b) { b = c; } -__global__ void kernScanExclusiveNaive(int n, int *idata, int *odata) { +/** + * Naive parallel scan algorithm + * Input must be stored in `data`. + * Output is stored both in `data` and `buffer`. + */ +__global__ void kernScanInclusiveNaive(int n, int *data, int *buffer) { int id = blockDim.x * blockIdx.x + threadIdx.x; int tx = threadIdx.x; int bdim = blockDim.x; @@ -27,21 +32,15 @@ __global__ void kernScanExclusiveNaive(int n, int *idata, int *odata) { if (id < n) { for (int d = 1; d <= log2n; ++d) { - odata[id] = idata[id]; + buffer[id] = data[id]; __syncthreads(); if (tx >= (1 << (d - 1))) { - odata[id] = idata[id - (1 << (d - 1))] + idata[id]; + buffer[id] = data[id - (1 << (d - 1))] + data[id]; } __syncthreads(); - swap(idata, odata); + data[id] = buffer[id]; __syncthreads(); } - - if (tx > 0) { - odata[id] = idata[id - 1]; - } else { - odata[id] = 0; - } } } @@ -54,18 +53,37 @@ void scan(int n, int *odata, const int *idata) { const unsigned int grid_size = (n + Common::block_size - 1) / Common::block_size; - int *dev_idata, *dev_odata; + int *dev_idata, *dev_odata, *buffer; cudaMalloc((void **)&dev_idata, n * sizeof(int)); cudaMalloc((void **)&dev_odata, n * sizeof(int)); - checkCUDAError("cudaMalloc failed for dev_idata and dev_odata !"); + cudaMalloc((void **)&buffer, n * sizeof(int)); + checkCUDAError("cudaMalloc failed for dev_idata, dev_odata, buffer!"); + + int *dev_offset_inclusive, *dev_offset_exclusive; + cudaMalloc((void **)&dev_offset_inclusive, grid_size * sizeof(int)); + cudaMalloc((void **)&dev_offset_exclusive, grid_size * sizeof(int)); + checkCUDAError( + "cudaMalloc failed for dev_offset_inclusive, dev_offset_exclusive!"); cudaMemcpy(dev_idata, idata, n * sizeof(int), cudaMemcpyHostToDevice); checkCUDAError("cudaMemcpy failed for idata --> dev_idata!"); + /******* KERNEL INVOCATIONS *******/ timer().startGpuTimer(); dim3 dimGrid{grid_size}, dimBlock{Common::block_size}; - kernScanExclusiveNaive<<>>(n, dev_idata, dev_odata); + kernScanInclusiveNaive<<>>(n, dev_idata, buffer); + Common::kernExtractLastElementPerBlock<<>>( + n, dev_offset_inclusive, dev_idata); + kernScanInclusiveNaive<<<1, dimBlock>>>( + grid_size, dev_offset_inclusive, + dev_offset_exclusive); // dev_offset_exclusive only serves as buffer here + Common::kernShiftToExclusive<<<1, dimBlock>>>(grid_size, dev_offset_exclusive, + dev_offset_inclusive); + Common::kernAddOffsetPerBlock<<>>( + n, dev_idata, dev_offset_exclusive, buffer); + Common::kernShiftToExclusive<<>>(n, dev_odata, dev_idata); timer().endGpuTimer(); + /**********************************/ cudaDeviceSynchronize(); cudaMemcpy(odata, dev_odata, n * sizeof(int), cudaMemcpyDeviceToHost); From 3ba2ba3777e907130ad154730705fb534236c21f Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sat, 18 Sep 2021 23:12:53 -0400 Subject: [PATCH 10/30] add docs to arbitrary length array handling progress --- stream_compaction/naive.cu | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index 865d09f..c07808d 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -69,9 +69,13 @@ void scan(int n, int *odata, const int *idata) { checkCUDAError("cudaMemcpy failed for idata --> dev_idata!"); /******* KERNEL INVOCATIONS *******/ - timer().startGpuTimer(); dim3 dimGrid{grid_size}, dimBlock{Common::block_size}; + timer().startGpuTimer(); kernScanInclusiveNaive<<>>(n, dev_idata, buffer); + + // NOTE. We assume the number of blocks we use is always less than block size, + // so we only have to perform scan on block offsets only once. + // TODO: Implement recursive scan feature Common::kernExtractLastElementPerBlock<<>>( n, dev_offset_inclusive, dev_idata); kernScanInclusiveNaive<<<1, dimBlock>>>( @@ -81,6 +85,7 @@ void scan(int n, int *odata, const int *idata) { dev_offset_inclusive); Common::kernAddOffsetPerBlock<<>>( n, dev_idata, dev_offset_exclusive, buffer); + Common::kernShiftToExclusive<<>>(n, dev_odata, dev_idata); timer().endGpuTimer(); /**********************************/ From c56536fa3c08d3f8f595ed0d5fcfcfc87034b598 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sun, 19 Sep 2021 03:00:46 -0400 Subject: [PATCH 11/30] finish initial version of work-efficient scan --- src/main.cpp | 4 +- stream_compaction/efficient.cu | 80 +++++++++++++++++++++++++++++++++- 2 files changed, 81 insertions(+), 3 deletions(-) diff --git a/src/main.cpp b/src/main.cpp index 50ff24d..c099ae7 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -82,7 +82,7 @@ int main(int argc, char *argv[]) { printElapsedTime(StreamCompaction::Efficient::timer() .getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - // printArray(SIZE, c, true); + printArray(SIZE, c, true); printCmpResult(SIZE, b, c); zeroArray(SIZE, c); @@ -91,7 +91,7 @@ int main(int argc, char *argv[]) { printElapsedTime(StreamCompaction::Efficient::timer() .getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - // printArray(NPOT, c, true); + printArray(NPOT, c, true); printCmpResult(NPOT, b, c); zeroArray(SIZE, c); diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index bb16a6d..7cdec25 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -12,13 +12,91 @@ PerformanceTimer &timer() { return timer; } +__global__ void kernScanInclusive(int n, int *odata, int *idata) { + int tid = threadIdx.x; + int bdim = blockDim.x; + int id = blockIdx.x * bdim + tid; + int log2n = ilog2ceil((n < bdim) ? n : bdim); + if (id < n) { + // upsweep + for (int d = 0; d < log2n; ++d) { + if (id % (1 << (d + 1)) == 0) { + idata[id + (1 << (d + 1)) - 1] += idata[id + (1 << d) - 1]; + } + __syncthreads(); + } + + // last thread remembers and sets reduction sum after downsweep + int reduction_sum = 0; + if (tid == bdim - 1 || id == n - 1) { + reduction_sum = idata[id]; + idata[id] = 0; + } + __syncthreads(); + + // downsweep + for (int d = log2n - 1; d >= 0; --d) { + if (id % (1 << (d + 1)) == 0) { + int temp = idata[id + (1 << d) - 1]; + idata[id + (1 << d) - 1] = idata[id + (1 << (d + 1)) - 1]; + idata[id + (1 << (d + 1)) - 1] += temp; + } + __syncthreads(); + } + + // turn exclusive scan into inclusive scan + if (tid == bdim - 1 || id == n - 1) { + odata[id] = reduction_sum; + } else { + odata[id] = idata[id + 1]; + } + } +} + /** * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ void scan(int n, int *odata, const int *idata) { + const unsigned int grid_size = + (n + Common::block_size - 1) / Common::block_size; + const int n_pad = 1 << (ilog2ceil(n)); + + // allocate input/output device data + int *dev_idata, *dev_odata; + cudaMalloc((void **)&dev_idata, n_pad * sizeof(int)); + cudaMalloc((void **)&dev_odata, n_pad * sizeof(int)); + checkCUDAError("cudaMalloc dev_idata, dev_odata failed!"); + + cudaMemset(dev_idata, 0, n_pad * sizeof(int)); + checkCUDAError("cudaMemset dev_idata to 0 failed!"); + cudaMemcpy(dev_idata, idata, n * sizeof(int), cudaMemcpyHostToDevice); + checkCUDAError("cudaMemcpy dev_idata from idata failed!"); + + // allocate block reduction device data + int *dev_block_offset_inclusive, *dev_block_offset_exclusive; + cudaMalloc((void **)&dev_block_offset_inclusive, grid_size * sizeof(int)); + cudaMalloc((void **)&dev_block_offset_exclusive, grid_size * sizeof(int)); + + /******* KERNEL INVOCATION *******/ + dim3 dimGrid{grid_size}, dimBlock{Common::block_size}; timer().startGpuTimer(); - // TODO + kernScanInclusive<<>>(n_pad, dev_odata, dev_idata); + Common::kernExtractLastElementPerBlock<<>>( + n_pad, dev_block_offset_exclusive, dev_odata); + kernScanInclusive<<<1, dimBlock>>>(grid_size, dev_block_offset_inclusive, + dev_block_offset_exclusive); + Common::kernShiftToExclusive<<<1, dimBlock>>>( + grid_size, dev_block_offset_exclusive, dev_block_offset_inclusive); + Common::kernAddOffsetPerBlock<<>>( + n_pad, dev_idata, dev_block_offset_exclusive, dev_odata); + Common::kernShiftToExclusive<<>>(n_pad, dev_odata, + dev_idata); timer().endGpuTimer(); + /*********************************/ + + cudaDeviceSynchronize(); + cudaMemcpy(odata, dev_odata, n * sizeof(int), cudaMemcpyDeviceToHost); + checkCUDAError("cudaMemcpy odata from dev_idata failed!"); } /** From 18326dd855be1583061fbbf14dce9e362fbd79fc Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sun, 19 Sep 2021 03:04:33 -0400 Subject: [PATCH 12/30] increase block size to 1024 --- src/main.cpp | 2 +- stream_compaction/common.cu | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/main.cpp b/src/main.cpp index c099ae7..8ce6149 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -15,7 +15,7 @@ #include "testing_helpers.hpp" -const int SIZE = 1 << 8; // feel free to change the size of array +const int SIZE = 1 << 20; // feel free to change the size of array const int NPOT = SIZE - 3; // Non-Power-Of-Two int *a = new int[SIZE]; int *b = new int[SIZE]; diff --git a/stream_compaction/common.cu b/stream_compaction/common.cu index 0282b87..b9df4fe 100644 --- a/stream_compaction/common.cu +++ b/stream_compaction/common.cu @@ -17,7 +17,7 @@ void checkCUDAErrorFn(const char *msg, const char *file, int line) { namespace StreamCompaction { namespace Common { -const unsigned int block_size = 256; +const unsigned int block_size = 1024; __global__ void kernExtractLastElementPerBlock(int n, int *odata, const int *idata) { From 6b34b0052f934f8e3832d2f6dcb1cc1dd54bdc4f Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sun, 19 Sep 2021 13:23:26 -0400 Subject: [PATCH 13/30] finish first version of work-efficient stream compaction --- src/main.cpp | 4 +- stream_compaction/common.cu | 10 ++++- stream_compaction/efficient.cu | 68 +++++++++++++++++++++++++++++++++- 3 files changed, 76 insertions(+), 6 deletions(-) diff --git a/src/main.cpp b/src/main.cpp index 8ce6149..fac48dc 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -163,7 +163,7 @@ int main(int argc, char *argv[]) { printElapsedTime(StreamCompaction::Efficient::timer() .getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - // printArray(count, c, true); + printArray(count, c, true); printCmpLenResult(count, expectedCount, b, c); zeroArray(SIZE, c); @@ -172,7 +172,7 @@ int main(int argc, char *argv[]) { printElapsedTime(StreamCompaction::Efficient::timer() .getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - // printArray(count, c, true); + printArray(count, c, true); printCmpLenResult(count, expectedNPOT, b, c); #if defined(_WIN32) diff --git a/stream_compaction/common.cu b/stream_compaction/common.cu index b9df4fe..b625fca 100644 --- a/stream_compaction/common.cu +++ b/stream_compaction/common.cu @@ -56,7 +56,10 @@ __global__ void kernShiftToExclusive(int n, int *odata, const int *idata) { * which map to 0 will be removed, and elements which map to 1 will be kept. */ __global__ void kernMapToBoolean(int n, int *bools, const int *idata) { - // TODO + int id = blockIdx.x * blockDim.x + threadIdx.x; + if (id < n) { + bools[id] = (idata[id] == 0) ? 0 : 1; + } } /** @@ -65,7 +68,10 @@ __global__ void kernMapToBoolean(int n, int *bools, const int *idata) { */ __global__ void kernScatter(int n, int *odata, const int *idata, const int *bools, const int *indices) { - // TODO + int id = blockDim.x * blockIdx.x + threadIdx.x; + if (id < n) { + if (bools[id] == 1) odata[indices[id]] = idata[id]; + } } } // namespace Common diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index 7cdec25..0f9fa63 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -109,10 +109,74 @@ void scan(int n, int *odata, const int *idata) { * @returns The number of elements remaining after compaction. */ int compact(int n, int *odata, const int *idata) { + const unsigned int grid_size = + (n + Common::block_size - 1) / Common::block_size; + const int n_pad = 1 << (ilog2ceil(n)); + + // Device data allocation + int *dev_idata, *dev_odata; + cudaMalloc((void **)&dev_idata, n_pad * sizeof(int)); + cudaMalloc((void **)&dev_odata, n_pad * sizeof(int)); + checkCUDAError("cudaMalloc dev_idata, dev_odata failed!"); + cudaMemset(dev_idata, 0, n_pad * sizeof(int)); + checkCUDAError("cudaMemset dev_idata to 0 failed!"); + + cudaMemcpy(dev_idata, idata, n * sizeof(int), cudaMemcpyHostToDevice); + checkCUDAError("cudaMemcpy dev_idata from idata failed!"); + + // helper data allocation + int *dev_indices, *dev_bools, *dev_buffer; + cudaMalloc((void **)&dev_indices, n_pad * sizeof(int)); + cudaMalloc((void **)&dev_bools, n_pad * sizeof(int)); + cudaMalloc((void **)&dev_buffer, n_pad * sizeof(int)); + checkCUDAError("cudaMalloc dev_indices, dev_bools, dev_buffer failed!"); + + int *dev_offset_inclusive, *dev_offset_exclusive; + cudaMalloc((void **)&dev_offset_exclusive, grid_size * sizeof(int)); + cudaMalloc((void **)&dev_offset_inclusive, grid_size * sizeof(int)); + checkCUDAError( + "cudaMalloc dev_offset_inclusive, dev_offset_exclusive failed!"); + + /******* KERNEL INVOCATION *******/ + dim3 dimGrid{grid_size}, dimBlock{Common::block_size}; timer().startGpuTimer(); - // TODO + Common::kernMapToBoolean<<>>(n_pad, dev_bools, dev_idata); + cudaDeviceSynchronize(); + cudaMemcpy(dev_buffer, dev_bools, n_pad * sizeof(int), + cudaMemcpyDeviceToDevice); + kernScanInclusive<<>>(n_pad, dev_odata, dev_buffer); + Common::kernExtractLastElementPerBlock<<>>( + n_pad, dev_offset_exclusive, dev_odata); + kernScanInclusive<<<1, dimBlock>>>(grid_size, dev_offset_inclusive, + dev_offset_exclusive); + Common::kernShiftToExclusive<<<1, dimBlock>>>(grid_size, dev_offset_exclusive, + dev_offset_inclusive); + Common::kernAddOffsetPerBlock<<>>( + n_pad, dev_buffer, dev_offset_exclusive, dev_odata); + Common::kernShiftToExclusive<<>>(n_pad, dev_indices, + dev_buffer); + Common::kernScatter<<>>(n_pad, dev_odata, dev_idata, + dev_bools, dev_indices); timer().endGpuTimer(); - return -1; + /*********************************/ + + // transfer output data to CPU & analyze + cudaDeviceSynchronize(); + cudaMemcpy(odata, dev_odata, n * sizeof(int), cudaMemcpyDeviceToHost); + checkCUDAError("cudaMemcpy odata from dev_odata failed!"); + + // calculate num. of elements after compaction + int *indices = (int *)malloc(n * sizeof(int)); + int *bools = (int *)malloc(n * sizeof(int)); + cudaMemcpy(indices, dev_indices, n * sizeof(int), cudaMemcpyDeviceToHost); + cudaMemcpy(bools, dev_bools, n * sizeof(int), cudaMemcpyDeviceToHost); + checkCUDAError( + "cudaMemcpy indices from dev_indices, bools from dev_bools failed!"); + int compact_len = indices[n - 1] + bools[n - 1]; + free(indices); + free(bools); + + return compact_len; } } // namespace Efficient } // namespace StreamCompaction From 5069185f021645fd2ddcb6f19dc30f608bdaf877 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sun, 19 Sep 2021 15:17:37 -0400 Subject: [PATCH 14/30] free all CUDA data arrays --- stream_compaction/efficient.cu | 8 ++++++++ stream_compaction/naive.cu | 16 ++++++++++------ 2 files changed, 18 insertions(+), 6 deletions(-) diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index 0f9fa63..13a7552 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -176,6 +176,14 @@ int compact(int n, int *odata, const int *idata) { free(indices); free(bools); + cudaFree(dev_idata); + cudaFree(dev_odata); + cudaFree(dev_indices); + cudaFree(dev_bools); + cudaFree(dev_buffer); + cudaFree(dev_offset_exclusive); + cudaFree(dev_offset_inclusive); + return compact_len; } } // namespace Efficient diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index c07808d..e802315 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -53,11 +53,11 @@ void scan(int n, int *odata, const int *idata) { const unsigned int grid_size = (n + Common::block_size - 1) / Common::block_size; - int *dev_idata, *dev_odata, *buffer; + int *dev_idata, *dev_odata, *dev_buffer; cudaMalloc((void **)&dev_idata, n * sizeof(int)); cudaMalloc((void **)&dev_odata, n * sizeof(int)); - cudaMalloc((void **)&buffer, n * sizeof(int)); - checkCUDAError("cudaMalloc failed for dev_idata, dev_odata, buffer!"); + cudaMalloc((void **)&dev_buffer, n * sizeof(int)); + checkCUDAError("cudaMalloc failed for dev_idata, dev_odata, dev_buffer!"); int *dev_offset_inclusive, *dev_offset_exclusive; cudaMalloc((void **)&dev_offset_inclusive, grid_size * sizeof(int)); @@ -71,7 +71,7 @@ void scan(int n, int *odata, const int *idata) { /******* KERNEL INVOCATIONS *******/ dim3 dimGrid{grid_size}, dimBlock{Common::block_size}; timer().startGpuTimer(); - kernScanInclusiveNaive<<>>(n, dev_idata, buffer); + kernScanInclusiveNaive<<>>(n, dev_idata, dev_buffer); // NOTE. We assume the number of blocks we use is always less than block size, // so we only have to perform scan on block offsets only once. @@ -80,11 +80,12 @@ void scan(int n, int *odata, const int *idata) { n, dev_offset_inclusive, dev_idata); kernScanInclusiveNaive<<<1, dimBlock>>>( grid_size, dev_offset_inclusive, - dev_offset_exclusive); // dev_offset_exclusive only serves as buffer here + dev_offset_exclusive); // dev_offset_exclusive only serves as buffer + // here Common::kernShiftToExclusive<<<1, dimBlock>>>(grid_size, dev_offset_exclusive, dev_offset_inclusive); Common::kernAddOffsetPerBlock<<>>( - n, dev_idata, dev_offset_exclusive, buffer); + n, dev_idata, dev_offset_exclusive, dev_buffer); Common::kernShiftToExclusive<<>>(n, dev_odata, dev_idata); timer().endGpuTimer(); @@ -95,6 +96,9 @@ void scan(int n, int *odata, const int *idata) { cudaFree(dev_idata); cudaFree(dev_odata); + cudaFree(dev_buffer); + cudaFree(dev_offset_exclusive); + cudaFree(dev_offset_inclusive); } } // namespace Naive } // namespace StreamCompaction From d3372e5d622eb3f0d4831d91a7e5e3724a9808d0 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sun, 19 Sep 2021 16:59:54 -0400 Subject: [PATCH 15/30] finish recursive block scan in Naive scan --- stream_compaction/naive.cu | 98 ++++++++++++++++++++++---------------- 1 file changed, 58 insertions(+), 40 deletions(-) diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index e802315..d467e18 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -24,7 +24,7 @@ __device__ void inline swap(T &a, T &b) { * Input must be stored in `data`. * Output is stored both in `data` and `buffer`. */ -__global__ void kernScanInclusiveNaive(int n, int *data, int *buffer) { +__global__ void kernScanInclusive(int n, int *data, int *buffer) { int id = blockDim.x * blockIdx.x + threadIdx.x; int tx = threadIdx.x; int bdim = blockDim.x; @@ -50,55 +50,73 @@ __global__ void kernScanInclusiveNaive(int n, int *data, int *buffer) { void scan(int n, int *odata, const int *idata) { if (n <= 0) return; - const unsigned int grid_size = - (n + Common::block_size - 1) / Common::block_size; + int num_scans = 1; + int len = n; + while ((len + Common::block_size - 1) / Common::block_size > 1) { + ++num_scans; + len = (len + Common::block_size - 1) / Common::block_size; + } - int *dev_idata, *dev_odata, *dev_buffer; - cudaMalloc((void **)&dev_idata, n * sizeof(int)); - cudaMalloc((void **)&dev_odata, n * sizeof(int)); - cudaMalloc((void **)&dev_buffer, n * sizeof(int)); - checkCUDAError("cudaMalloc failed for dev_idata, dev_odata, dev_buffer!"); + int **dev_idata = (int **)malloc(num_scans * sizeof(int *)); + int **dev_odata = (int **)malloc(num_scans * sizeof(int *)); + int **dev_buffer = (int **)malloc(num_scans * sizeof(int *)); + int *array_sizes = (int *)malloc(num_scans * sizeof(int)); + int *grid_sizes = (int *)malloc(num_scans * sizeof(int)); - int *dev_offset_inclusive, *dev_offset_exclusive; - cudaMalloc((void **)&dev_offset_inclusive, grid_size * sizeof(int)); - cudaMalloc((void **)&dev_offset_exclusive, grid_size * sizeof(int)); - checkCUDAError( - "cudaMalloc failed for dev_offset_inclusive, dev_offset_exclusive!"); + len = n; + for (int i = 0; i < num_scans; ++i) { + cudaMalloc((void **)&dev_idata[i], len * sizeof(int)); + cudaMalloc((void **)&dev_odata[i], len * sizeof(int)); + cudaMalloc((void **)&dev_buffer[i], len * sizeof(int)); + checkCUDAError("cudaMalloc failed for dev_idata, dev_odata, dev_buffer!"); + array_sizes[i] = len; + len = (len + Common::block_size - 1) / Common::block_size; + grid_sizes[i] = len; + } - cudaMemcpy(dev_idata, idata, n * sizeof(int), cudaMemcpyHostToDevice); - checkCUDAError("cudaMemcpy failed for idata --> dev_idata!"); + cudaMemcpy(dev_idata[0], idata, n * sizeof(int), cudaMemcpyHostToDevice); + checkCUDAError("cudaMemcpy failed for idata --> dev_idata[0]!"); /******* KERNEL INVOCATIONS *******/ - dim3 dimGrid{grid_size}, dimBlock{Common::block_size}; + dim3 dimBlock{Common::block_size}; timer().startGpuTimer(); - kernScanInclusiveNaive<<>>(n, dev_idata, dev_buffer); - - // NOTE. We assume the number of blocks we use is always less than block size, - // so we only have to perform scan on block offsets only once. - // TODO: Implement recursive scan feature - Common::kernExtractLastElementPerBlock<<>>( - n, dev_offset_inclusive, dev_idata); - kernScanInclusiveNaive<<<1, dimBlock>>>( - grid_size, dev_offset_inclusive, - dev_offset_exclusive); // dev_offset_exclusive only serves as buffer - // here - Common::kernShiftToExclusive<<<1, dimBlock>>>(grid_size, dev_offset_exclusive, - dev_offset_inclusive); - Common::kernAddOffsetPerBlock<<>>( - n, dev_idata, dev_offset_exclusive, dev_buffer); - - Common::kernShiftToExclusive<<>>(n, dev_odata, dev_idata); + for (int i = 0; i < num_scans; ++i) { + dim3 dimGrid{(unsigned int)grid_sizes[i]}; + kernScanInclusive<<>>(array_sizes[i], dev_idata[i], + dev_buffer[i]); + if (i < num_scans - 1) { + Common::kernExtractLastElementPerBlock<<>>( + array_sizes[i], dev_idata[i + 1], dev_idata[i]); + } + } + for (int i = num_scans - 1; i >= 0; --i) { + dim3 dimGrid{(unsigned int)grid_sizes[i]}; + Common::kernShiftToExclusive<<>>( + array_sizes[i], dev_odata[i], dev_buffer[i]); + if (i >= 1) { + dim3 next_dimGrid{(unsigned int)grid_sizes[i - 1]}; + Common::kernAddOffsetPerBlock<<>>( + array_sizes[i - 1], dev_buffer[i - 1], dev_odata[i], + dev_idata[i - 1]); + } + } + cudaDeviceSynchronize(); timer().endGpuTimer(); /**********************************/ - cudaDeviceSynchronize(); - cudaMemcpy(odata, dev_odata, n * sizeof(int), cudaMemcpyDeviceToHost); + cudaMemcpy(odata, dev_odata[0], n * sizeof(int), cudaMemcpyDeviceToHost); - cudaFree(dev_idata); - cudaFree(dev_odata); - cudaFree(dev_buffer); - cudaFree(dev_offset_exclusive); - cudaFree(dev_offset_inclusive); + // Free all memory allocations + for (int i = 0; i < num_scans; ++i) { + cudaFree(dev_idata[i]); + cudaFree(dev_odata[i]); + cudaFree(dev_buffer[i]); + } + free(grid_sizes); + free(array_sizes); + free(dev_idata); + free(dev_odata); + free(dev_buffer); } } // namespace Naive } // namespace StreamCompaction From d7205a075734cb2ea789b5d820b73d2aa75ab36b Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sun, 19 Sep 2021 17:11:46 -0400 Subject: [PATCH 16/30] rewrite work-efficient scan as recursive scan on blocks --- stream_compaction/efficient.cu | 96 ++++++++++++++++++++++------------ 1 file changed, 64 insertions(+), 32 deletions(-) diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index 13a7552..c585796 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -57,46 +57,78 @@ __global__ void kernScanInclusive(int n, int *odata, int *idata) { * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ void scan(int n, int *odata, const int *idata) { - const unsigned int grid_size = - (n + Common::block_size - 1) / Common::block_size; + if (n <= 0) return; const int n_pad = 1 << (ilog2ceil(n)); - // allocate input/output device data - int *dev_idata, *dev_odata; - cudaMalloc((void **)&dev_idata, n_pad * sizeof(int)); - cudaMalloc((void **)&dev_odata, n_pad * sizeof(int)); - checkCUDAError("cudaMalloc dev_idata, dev_odata failed!"); + int num_scans = 1; + int len = n_pad; + while ((len + Common::block_size - 1) / Common::block_size > 1) { + ++num_scans; + len = (len + Common::block_size - 1) / Common::block_size; + } - cudaMemset(dev_idata, 0, n_pad * sizeof(int)); - checkCUDAError("cudaMemset dev_idata to 0 failed!"); - cudaMemcpy(dev_idata, idata, n * sizeof(int), cudaMemcpyHostToDevice); - checkCUDAError("cudaMemcpy dev_idata from idata failed!"); + int **dev_idata = (int **)malloc(num_scans * sizeof(int *)); + int **dev_odata = (int **)malloc(num_scans * sizeof(int *)); + int **dev_buffer = (int **)malloc(num_scans * sizeof(int *)); + int *array_sizes = (int *)malloc(num_scans * sizeof(int)); + int *grid_sizes = (int *)malloc(num_scans * sizeof(int)); + + len = n_pad; + for (int i = 0; i < num_scans; ++i) { + cudaMalloc((void **)&dev_idata[i], len * sizeof(int)); + cudaMalloc((void **)&dev_odata[i], len * sizeof(int)); + cudaMalloc((void **)&dev_buffer[i], len * sizeof(int)); + checkCUDAError("cudaMalloc failed for dev_idata, dev_odata, dev_buffer!"); + array_sizes[i] = len; + len = (len + Common::block_size - 1) / Common::block_size; + grid_sizes[i] = len; + } - // allocate block reduction device data - int *dev_block_offset_inclusive, *dev_block_offset_exclusive; - cudaMalloc((void **)&dev_block_offset_inclusive, grid_size * sizeof(int)); - cudaMalloc((void **)&dev_block_offset_exclusive, grid_size * sizeof(int)); + cudaMemcpy(dev_idata[0], idata, n * sizeof(int), cudaMemcpyHostToDevice); + checkCUDAError("cudaMemcpy failed for idata --> dev_idata[0]!"); - /******* KERNEL INVOCATION *******/ - dim3 dimGrid{grid_size}, dimBlock{Common::block_size}; + /******* KERNEL INVOCATIONS *******/ + dim3 dimBlock{Common::block_size}; timer().startGpuTimer(); - kernScanInclusive<<>>(n_pad, dev_odata, dev_idata); - Common::kernExtractLastElementPerBlock<<>>( - n_pad, dev_block_offset_exclusive, dev_odata); - kernScanInclusive<<<1, dimBlock>>>(grid_size, dev_block_offset_inclusive, - dev_block_offset_exclusive); - Common::kernShiftToExclusive<<<1, dimBlock>>>( - grid_size, dev_block_offset_exclusive, dev_block_offset_inclusive); - Common::kernAddOffsetPerBlock<<>>( - n_pad, dev_idata, dev_block_offset_exclusive, dev_odata); - Common::kernShiftToExclusive<<>>(n_pad, dev_odata, - dev_idata); + for (int i = 0; i < num_scans; ++i) { + dim3 dimGrid{(unsigned int)grid_sizes[i]}; + kernScanInclusive<<>>(array_sizes[i], dev_buffer[i], + dev_idata[i]); + cudaMemcpy(dev_idata[i], dev_buffer[i], array_sizes[i] * sizeof(int), + cudaMemcpyDeviceToDevice); + if (i < num_scans - 1) { + Common::kernExtractLastElementPerBlock<<>>( + array_sizes[i], dev_idata[i + 1], dev_idata[i]); + } + } + for (int i = num_scans - 1; i >= 0; --i) { + dim3 dimGrid{(unsigned int)grid_sizes[i]}; + Common::kernShiftToExclusive<<>>( + array_sizes[i], dev_odata[i], dev_buffer[i]); + if (i >= 1) { + dim3 next_dimGrid{(unsigned int)grid_sizes[i - 1]}; + Common::kernAddOffsetPerBlock<<>>( + array_sizes[i - 1], dev_buffer[i - 1], dev_odata[i], + dev_idata[i - 1]); + } + } + cudaDeviceSynchronize(); timer().endGpuTimer(); - /*********************************/ + /**********************************/ - cudaDeviceSynchronize(); - cudaMemcpy(odata, dev_odata, n * sizeof(int), cudaMemcpyDeviceToHost); - checkCUDAError("cudaMemcpy odata from dev_idata failed!"); + cudaMemcpy(odata, dev_odata[0], n * sizeof(int), cudaMemcpyDeviceToHost); + + // Free all memory allocations + for (int i = 0; i < num_scans; ++i) { + cudaFree(dev_idata[i]); + cudaFree(dev_odata[i]); + cudaFree(dev_buffer[i]); + } + free(grid_sizes); + free(array_sizes); + free(dev_idata); + free(dev_odata); + free(dev_buffer); } /** From 941e9f672b678b5085f70403cb868d43dc45855d Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sun, 19 Sep 2021 17:31:14 -0400 Subject: [PATCH 17/30] finish recursive block scan in work-efficient stream compaction --- stream_compaction/efficient.cu | 118 +++++++++++++++++++++------------ 1 file changed, 77 insertions(+), 41 deletions(-) diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index c585796..02abe3a 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -12,6 +12,11 @@ PerformanceTimer &timer() { return timer; } +/** + * Work-Efficient Inclusive Scan + * Input: idata + * Output: odata --- inclusive scan, idata --- exclusive scan + */ __global__ void kernScanInclusive(int n, int *odata, int *idata) { int tid = threadIdx.x; int bdim = blockDim.x; @@ -141,80 +146,111 @@ void scan(int n, int *odata, const int *idata) { * @returns The number of elements remaining after compaction. */ int compact(int n, int *odata, const int *idata) { - const unsigned int grid_size = - (n + Common::block_size - 1) / Common::block_size; + if (n <= 0) return n; const int n_pad = 1 << (ilog2ceil(n)); - // Device data allocation - int *dev_idata, *dev_odata; + int num_scans = 1; + int len = n_pad; + while ((len + Common::block_size - 1) / Common::block_size > 1) { + ++num_scans; + len = (len + Common::block_size - 1) / Common::block_size; + } + + // input data device allocation + int *dev_idata, *dev_odata, *dev_bools; cudaMalloc((void **)&dev_idata, n_pad * sizeof(int)); cudaMalloc((void **)&dev_odata, n_pad * sizeof(int)); - checkCUDAError("cudaMalloc dev_idata, dev_odata failed!"); + cudaMalloc((void **)&dev_bools, n_pad * sizeof(int)); + checkCUDAError("cudaMalloc dev_idata, dev_odata, dev_bools failed!"); cudaMemset(dev_idata, 0, n_pad * sizeof(int)); checkCUDAError("cudaMemset dev_idata to 0 failed!"); - cudaMemcpy(dev_idata, idata, n * sizeof(int), cudaMemcpyHostToDevice); - checkCUDAError("cudaMemcpy dev_idata from idata failed!"); + checkCUDAError("cudaMemcpy failed for idata --> dev_idata!"); - // helper data allocation - int *dev_indices, *dev_bools, *dev_buffer; - cudaMalloc((void **)&dev_indices, n_pad * sizeof(int)); - cudaMalloc((void **)&dev_bools, n_pad * sizeof(int)); - cudaMalloc((void **)&dev_buffer, n_pad * sizeof(int)); - checkCUDAError("cudaMalloc dev_indices, dev_bools, dev_buffer failed!"); + int **dev_iIndices = (int **)malloc(num_scans * sizeof(int *)); + int **dev_oIndices = (int **)malloc(num_scans * sizeof(int *)); + int **dev_buffer = (int **)malloc(num_scans * sizeof(int *)); + int *array_sizes = (int *)malloc(num_scans * sizeof(int)); + int *grid_sizes = (int *)malloc(num_scans * sizeof(int)); - int *dev_offset_inclusive, *dev_offset_exclusive; - cudaMalloc((void **)&dev_offset_exclusive, grid_size * sizeof(int)); - cudaMalloc((void **)&dev_offset_inclusive, grid_size * sizeof(int)); - checkCUDAError( - "cudaMalloc dev_offset_inclusive, dev_offset_exclusive failed!"); + len = n_pad; + for (int i = 0; i < num_scans; ++i) { + cudaMalloc((void **)&dev_iIndices[i], len * sizeof(int)); + cudaMalloc((void **)&dev_oIndices[i], len * sizeof(int)); + cudaMalloc((void **)&dev_buffer[i], len * sizeof(int)); + checkCUDAError( + "cudaMalloc failed for dev_iIndices, dev_oIndices, dev_buffer!"); + array_sizes[i] = len; + len = (len + Common::block_size - 1) / Common::block_size; + grid_sizes[i] = len; + } /******* KERNEL INVOCATION *******/ - dim3 dimGrid{grid_size}, dimBlock{Common::block_size}; + dim3 dimGrid{(unsigned int)grid_sizes[0]}, dimBlock{Common::block_size}; timer().startGpuTimer(); + Common::kernMapToBoolean<<>>(n_pad, dev_bools, dev_idata); - cudaDeviceSynchronize(); - cudaMemcpy(dev_buffer, dev_bools, n_pad * sizeof(int), + cudaMemcpy(dev_iIndices[0], dev_bools, n_pad * sizeof(int), cudaMemcpyDeviceToDevice); - kernScanInclusive<<>>(n_pad, dev_odata, dev_buffer); - Common::kernExtractLastElementPerBlock<<>>( - n_pad, dev_offset_exclusive, dev_odata); - kernScanInclusive<<<1, dimBlock>>>(grid_size, dev_offset_inclusive, - dev_offset_exclusive); - Common::kernShiftToExclusive<<<1, dimBlock>>>(grid_size, dev_offset_exclusive, - dev_offset_inclusive); - Common::kernAddOffsetPerBlock<<>>( - n_pad, dev_buffer, dev_offset_exclusive, dev_odata); - Common::kernShiftToExclusive<<>>(n_pad, dev_indices, - dev_buffer); + for (int i = 0; i < num_scans; ++i) { + dim3 dimGrid{(unsigned int)grid_sizes[i]}; + kernScanInclusive<<>>(array_sizes[i], dev_buffer[i], + dev_iIndices[i]); + cudaMemcpy(dev_iIndices[i], dev_buffer[i], array_sizes[i] * sizeof(int), + cudaMemcpyDeviceToDevice); + if (i < num_scans - 1) { + Common::kernExtractLastElementPerBlock<<>>( + array_sizes[i], dev_iIndices[i + 1], dev_iIndices[i]); + } + } + for (int i = num_scans - 1; i >= 0; --i) { + dim3 dimGrid{(unsigned int)grid_sizes[i]}; + Common::kernShiftToExclusive<<>>( + array_sizes[i], dev_oIndices[i], dev_buffer[i]); + if (i >= 1) { + dim3 next_dimGrid{(unsigned int)grid_sizes[i - 1]}; + Common::kernAddOffsetPerBlock<<>>( + array_sizes[i - 1], dev_buffer[i - 1], dev_oIndices[i], + dev_iIndices[i - 1]); + } + } Common::kernScatter<<>>(n_pad, dev_odata, dev_idata, - dev_bools, dev_indices); + dev_bools, dev_oIndices[0]); + + cudaDeviceSynchronize(); timer().endGpuTimer(); /*********************************/ // transfer output data to CPU & analyze - cudaDeviceSynchronize(); cudaMemcpy(odata, dev_odata, n * sizeof(int), cudaMemcpyDeviceToHost); checkCUDAError("cudaMemcpy odata from dev_odata failed!"); // calculate num. of elements after compaction int *indices = (int *)malloc(n * sizeof(int)); int *bools = (int *)malloc(n * sizeof(int)); - cudaMemcpy(indices, dev_indices, n * sizeof(int), cudaMemcpyDeviceToHost); + cudaMemcpy(indices, dev_oIndices[0], n * sizeof(int), cudaMemcpyDeviceToHost); cudaMemcpy(bools, dev_bools, n * sizeof(int), cudaMemcpyDeviceToHost); checkCUDAError( - "cudaMemcpy indices from dev_indices, bools from dev_bools failed!"); + "cudaMemcpy indices from dev_oIndices, bools from dev_bools failed!"); int compact_len = indices[n - 1] + bools[n - 1]; + + // Free all memory allocations free(indices); free(bools); + for (int i = 0; i < num_scans; ++i) { + cudaFree(dev_iIndices[i]); + cudaFree(dev_oIndices[i]); + cudaFree(dev_buffer[i]); + } + cudaFree(dev_bools); cudaFree(dev_idata); cudaFree(dev_odata); - cudaFree(dev_indices); - cudaFree(dev_bools); - cudaFree(dev_buffer); - cudaFree(dev_offset_exclusive); - cudaFree(dev_offset_inclusive); + free(grid_sizes); + free(array_sizes); + free(dev_iIndices); + free(dev_oIndices); + free(dev_buffer); return compact_len; } From dbd6cf350ee9697354f221c64c7f98342538cdeb Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sun, 19 Sep 2021 18:12:05 -0400 Subject: [PATCH 18/30] implement thrust scan --- src/main.cpp | 4 ++-- stream_compaction/thrust.cu | 9 +++++++-- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/src/main.cpp b/src/main.cpp index fac48dc..4da08a3 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -100,7 +100,7 @@ int main(int argc, char *argv[]) { printElapsedTime( StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - // printArray(SIZE, c, true); + printArray(SIZE, c, true); printCmpResult(SIZE, b, c); zeroArray(SIZE, c); @@ -109,7 +109,7 @@ int main(int argc, char *argv[]) { printElapsedTime( StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - // printArray(NPOT, c, true); + printArray(NPOT, c, true); printCmpResult(NPOT, b, c); printf("\n"); diff --git a/stream_compaction/thrust.cu b/stream_compaction/thrust.cu index e726435..5cce77d 100644 --- a/stream_compaction/thrust.cu +++ b/stream_compaction/thrust.cu @@ -18,11 +18,16 @@ PerformanceTimer &timer() { * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ void scan(int n, int *odata, const int *idata) { - timer().startGpuTimer(); - // TODO use `thrust::exclusive_scan` // example: for device_vectors dv_in and dv_out: // thrust::exclusive_scan(dv_in.begin(), dv_in.end(), dv_out.begin()); + thrust::device_vector dv_in{idata, idata + n}; + thrust::device_vector dv_out{odata, odata + n}; + + timer().startGpuTimer(); + thrust::exclusive_scan(dv_in.begin(), dv_in.end(), dv_out.begin()); timer().endGpuTimer(); + + thrust::copy(dv_out.begin(), dv_out.end(), odata); } } // namespace Thrust } // namespace StreamCompaction From 64cddb607e21b0f53ee349cf4e7dd890c87003fc Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sun, 19 Sep 2021 19:20:08 -0400 Subject: [PATCH 19/30] add sample log - config: block size = 2014, array size = 2^27 --- profiling/sample_log_b-1024_N-27.txt | 60 ++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 profiling/sample_log_b-1024_N-27.txt diff --git a/profiling/sample_log_b-1024_N-27.txt b/profiling/sample_log_b-1024_N-27.txt new file mode 100644 index 0000000..4c82ccb --- /dev/null +++ b/profiling/sample_log_b-1024_N-27.txt @@ -0,0 +1,60 @@ +**************** +** SCAN TESTS ** +**************** + [ 5 33 25 22 48 26 23 19 36 32 2 17 45 ... 22 0 ] +==== cpu scan, power-of-two ==== + elapsed time: 79.9027ms (std::chrono Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515866 -1006515844 ] +==== cpu scan, non-power-of-two ==== + elapsed time: 81.4093ms (std::chrono Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515949 -1006515918 ] + passed +==== naive scan, power-of-two ==== + elapsed time: 31.3315ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515866 -1006515844 ] + passed +==== naive scan, non-power-of-two ==== + elapsed time: 24.8398ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... 0 0 ] + passed +==== work-efficient scan, power-of-two ==== + elapsed time: 37.6307ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515866 -1006515844 ] + passed +==== work-efficient scan, non-power-of-two ==== + elapsed time: 37.6407ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515949 -1006515918 ] + passed +==== thrust scan, power-of-two ==== + elapsed time: 3.16525ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515866 -1006515844 ] + passed +==== thrust scan, non-power-of-two ==== + elapsed time: 3.12653ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515949 -1006515918 ] + passed + +***************************** +** STREAM COMPACTION TESTS ** +***************************** + [ 0 3 1 1 1 3 0 1 2 1 1 1 2 ... 3 0 ] +==== cpu compact without scan, power-of-two ==== + elapsed time: 250.09ms (std::chrono Measured) + [ 3 1 1 1 3 1 2 1 1 1 2 2 2 ... 3 3 ] + passed +==== cpu compact without scan, non-power-of-two ==== + elapsed time: 247.095ms (std::chrono Measured) + [ 3 1 1 1 3 1 2 1 1 1 2 2 2 ... 2 3 ] + passed +==== cpu compact with scan ==== + elapsed time: 886.643ms (std::chrono Measured) + [ 3 1 1 1 3 1 2 1 1 1 2 2 2 ... 3 3 ] + passed +==== work-efficient compact, power-of-two ==== + elapsed time: 58.4025ms (CUDA Measured) + [ 3 1 1 1 3 1 2 1 1 1 2 2 2 ... 3 3 ] + passed +==== work-efficient compact, non-power-of-two ==== + elapsed time: 48.5331ms (CUDA Measured) + [ 3 1 1 1 3 1 2 1 1 1 2 2 2 ... 2 3 ] + passed From 5294179d8cc3c8493f5d5bfc0902c5b20e900021 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Sun, 19 Sep 2021 21:44:48 -0400 Subject: [PATCH 20/30] add include "device_launch_parameters.h" --- stream_compaction/common.h | 1 + 1 file changed, 1 insertion(+) diff --git a/stream_compaction/common.h b/stream_compaction/common.h index 331fd4d..a3fdcd7 100644 --- a/stream_compaction/common.h +++ b/stream_compaction/common.h @@ -2,6 +2,7 @@ #include #include +#include #include #include From 0488b13a94f13523eb3591f5e55b83f7bc636090 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Mon, 20 Sep 2021 21:58:20 -0400 Subject: [PATCH 21/30] Finish introduction of README --- README.md | 39 ++++++++++++++++++++++++++----- img/downsweep.png | Bin 0 -> 255893 bytes img/naive_scan.png | Bin 0 -> 454174 bytes img/scan_inclusive_exclusive.png | Bin 0 -> 180563 bytes img/stream_compaction.png | Bin 0 -> 132122 bytes img/upsweep.png | Bin 0 -> 155806 bytes 6 files changed, 33 insertions(+), 6 deletions(-) create mode 100644 img/downsweep.png create mode 100644 img/naive_scan.png create mode 100644 img/scan_inclusive_exclusive.png create mode 100644 img/stream_compaction.png create mode 100644 img/upsweep.png diff --git a/README.md b/README.md index 0e38ddb..00e1b63 100644 --- a/README.md +++ b/README.md @@ -3,12 +3,39 @@ CUDA Stream Compaction **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2** -* (TODO) YOUR NAME HERE - * (TODO) [LinkedIn](), [personal website](), [twitter](), etc. -* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab) +* Zhihao Ruan (ruanzh@seas.upenn.edu) + * [LinkedIn](https://www.linkedin.com/in/zhihao-ruan-29b29a13a/), [personal website](https://zhihaoruan.xyz/) +* Tested on: Ubuntu 20.04 LTS, Ryzen 3700X @ 2.22GHz 48GB, RTX 2060 Super @ 7976MB -### (TODO: Your README) +## Introduction: Stream Compaction +Stream compaction, essentially, is a technique that aims at removing elements from a list (aka. stream) that are not satisfied according to some criteria. For example, if we have a stream of integers `[1 2 3 2 1 5 23 4 0 0 3 4 2 0 3 8 0]` and we wish to remove "all elements that are 0" (aka. the *criteria*), we would get the remaining compact list `[1 2 3 2 1 5 23 4 3 4 2 3 8]`. -Include analysis, etc. (Remember, this is public, so don't put -anything here that you don't want to share with the world.) +Stream compaction is widely used in rendering & ray tracing. Although it seems straightforward to implement stream compaction in the first place, it is actually non-trivial to implement it on GPU with some parallel algorithms so that its performance can be boosted. This project would discuss the method for parallel stream compaction, and its underlying necessary component — parallel scan algorithm. + +**For more detailed description of the project, please refer to the [project instruction.](INSTRUCTION.md)** + +## Parallel Scan +Parallel scan, aka. parallel prefix sum, is a task of generating a list of numbers in which each index is the sum of all elements that comes before this index. There are two types of parallel scan: *exclusive* scan and *inclusive scan*, where the former inserts 0 at the beginning of output and discards the total sum at the end of list, while the latter keeps the total sum at the end of list and does not introduce 0 at the beginning. + +![](img/scan_inclusive_exclusive.png) + +### Naive Parallel Scan +A naive algorithm of implementing parallel scan is shown as follows. For each iteration, part of the thread adds up two elements in the list, producing the final result after several iterations. + +![](img/naive_scan.png) + +### Work Efficient Parallel Scan +There is also a much more efficient version of parallel scan, which involves 1) a list reduction, 2) a down-sweep. The list reduction can also be called as the "up-sweep" procedure, producing a total sum of the list with all partial sums in the middle. The down-sweep procedure exactly compensates those missing parts for the middle elements and completes the entire parallel scan. + +![](img/upsweep.png) + +![](img/downsweep.png) + +## Parallel Stream Compaction +After solving the problem of parallel scan, we can now get to the real algorithm for parallel stream compaction. Essentially, an effective stream compaction procedure consists of the following: +1. Generate a boolean array marking the validity of each element. For elements to remove, mark as "0"; otherwise mark as "1". +2. Compute exclusive parallel scan on the boolean array. +3. Scatter the desired elements into the output array. 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HcmV?d00001 From 8befcb21b5fd0d95eea2d1795c9147551328852b Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Mon, 20 Sep 2021 22:06:26 -0400 Subject: [PATCH 22/30] Add project highlights --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 00e1b63..0b055e9 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,12 @@ CUDA Stream Compaction * [LinkedIn](https://www.linkedin.com/in/zhihao-ruan-29b29a13a/), [personal website](https://zhihaoruan.xyz/) * Tested on: Ubuntu 20.04 LTS, Ryzen 3700X @ 2.22GHz 48GB, RTX 2060 Super @ 7976MB +## Highlights +This project implements: +- a naive parallel scan algorithm compatible with arbitrary sized input arrays; +- a work-efficient parallel scan algorithm compatible with arbitrary sized input arrays; +- a stream compaction algorithm built upon the work-efficient parallel scan compatible with arbitrary sized input arrays. + ## Introduction: Stream Compaction Stream compaction, essentially, is a technique that aims at removing elements from a list (aka. stream) that are not satisfied according to some criteria. For example, if we have a stream of integers `[1 2 3 2 1 5 23 4 0 0 3 4 2 0 3 8 0]` and we wish to remove "all elements that are 0" (aka. the *criteria*), we would get the remaining compact list `[1 2 3 2 1 5 23 4 3 4 2 3 8]`. From a7d8e8da3e2f377f78a73409fbd12f524ecc2cf2 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Mon, 20 Sep 2021 22:10:45 -0400 Subject: [PATCH 23/30] add sample output in readme --- README.md | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) diff --git a/README.md b/README.md index 0b055e9..cc71c3c 100644 --- a/README.md +++ b/README.md @@ -13,6 +13,72 @@ This project implements: - a work-efficient parallel scan algorithm compatible with arbitrary sized input arrays; - a stream compaction algorithm built upon the work-efficient parallel scan compatible with arbitrary sized input arrays. +The GPU steam compaction algorithm is demonstrated to be over 4x faster than the CPU version. + +A sample of test output on `block_size` = 1024, `array_size` = 2^20: +``` +**************** +** SCAN TESTS ** +**************** + [ 5 33 25 22 48 26 23 19 36 32 2 17 45 ... 22 0 ] +==== cpu scan, power-of-two ==== + elapsed time: 79.9027ms (std::chrono Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515866 -1006515844 ] +==== cpu scan, non-power-of-two ==== + elapsed time: 81.4093ms (std::chrono Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515949 -1006515918 ] + passed +==== naive scan, power-of-two ==== + elapsed time: 31.3315ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515866 -1006515844 ] + passed +==== naive scan, non-power-of-two ==== + elapsed time: 24.8398ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... 0 0 ] + passed +==== work-efficient scan, power-of-two ==== + elapsed time: 37.6307ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515866 -1006515844 ] + passed +==== work-efficient scan, non-power-of-two ==== + elapsed time: 37.6407ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515949 -1006515918 ] + passed +==== thrust scan, power-of-two ==== + elapsed time: 3.16525ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515866 -1006515844 ] + passed +==== thrust scan, non-power-of-two ==== + elapsed time: 3.12653ms (CUDA Measured) + [ 0 5 38 63 85 133 159 182 201 237 269 271 288 ... -1006515949 -1006515918 ] + passed + +***************************** +** STREAM COMPACTION TESTS ** +***************************** + [ 0 3 1 1 1 3 0 1 2 1 1 1 2 ... 3 0 ] +==== cpu compact without scan, power-of-two ==== + elapsed time: 250.09ms (std::chrono Measured) + [ 3 1 1 1 3 1 2 1 1 1 2 2 2 ... 3 3 ] + passed +==== cpu compact without scan, non-power-of-two ==== + elapsed time: 247.095ms (std::chrono Measured) + [ 3 1 1 1 3 1 2 1 1 1 2 2 2 ... 2 3 ] + passed +==== cpu compact with scan ==== + elapsed time: 886.643ms (std::chrono Measured) + [ 3 1 1 1 3 1 2 1 1 1 2 2 2 ... 3 3 ] + passed +==== work-efficient compact, power-of-two ==== + elapsed time: 58.4025ms (CUDA Measured) + [ 3 1 1 1 3 1 2 1 1 1 2 2 2 ... 3 3 ] + passed +==== work-efficient compact, non-power-of-two ==== + elapsed time: 48.5331ms (CUDA Measured) + [ 3 1 1 1 3 1 2 1 1 1 2 2 2 ... 2 3 ] + passed +``` + ## Introduction: Stream Compaction Stream compaction, essentially, is a technique that aims at removing elements from a list (aka. stream) that are not satisfied according to some criteria. For example, if we have a stream of integers `[1 2 3 2 1 5 23 4 0 0 3 4 2 0 3 8 0]` and we wish to remove "all elements that are 0" (aka. the *criteria*), we would get the remaining compact list `[1 2 3 2 1 5 23 4 3 4 2 3 8]`. From cc22684840e8412106040987baf45fb13a2b6c8b Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Mon, 20 Sep 2021 22:12:03 -0400 Subject: [PATCH 24/30] fix sample output array size error --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cc71c3c..7acf8ba 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ This project implements: The GPU steam compaction algorithm is demonstrated to be over 4x faster than the CPU version. -A sample of test output on `block_size` = 1024, `array_size` = 2^20: +A sample of test output on `block_size` = 1024, `array_size` = 2^27: ``` **************** ** SCAN TESTS ** From 1617aa9f9dcd378ad35d82bc681acde34d284798 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Mon, 20 Sep 2021 22:24:45 -0400 Subject: [PATCH 25/30] Update README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7acf8ba..c0bf7e4 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ This project implements: The GPU steam compaction algorithm is demonstrated to be over 4x faster than the CPU version. -A sample of test output on `block_size` = 1024, `array_size` = 2^27: +A sample of test output on `block_size` = 1024, `array_size` = 2^27 **(max array possible on local GPU)**: ``` **************** ** SCAN TESTS ** From 7a23d9b773e0ba7e1ae9b595d224eca72f9bed4b Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Mon, 20 Sep 2021 22:28:28 -0400 Subject: [PATCH 26/30] add separate block size for naive & efficient --- stream_compaction/common.cu | 3 ++- stream_compaction/common.h | 3 ++- stream_compaction/efficient.cu | 26 ++++++++++++++------------ stream_compaction/naive.cu | 13 +++++++------ 4 files changed, 25 insertions(+), 20 deletions(-) diff --git a/stream_compaction/common.cu b/stream_compaction/common.cu index b625fca..c03d50f 100644 --- a/stream_compaction/common.cu +++ b/stream_compaction/common.cu @@ -17,7 +17,8 @@ void checkCUDAErrorFn(const char *msg, const char *file, int line) { namespace StreamCompaction { namespace Common { -const unsigned int block_size = 1024; +const unsigned int block_size_efficient = 1024; +const unsigned int block_size_naive = 1024; __global__ void kernExtractLastElementPerBlock(int n, int *odata, const int *idata) { diff --git a/stream_compaction/common.h b/stream_compaction/common.h index a3fdcd7..22597fa 100644 --- a/stream_compaction/common.h +++ b/stream_compaction/common.h @@ -35,7 +35,8 @@ inline __device__ __host__ int ilog2ceil(int x) { namespace StreamCompaction { namespace Common { -extern const unsigned int block_size; +extern const unsigned int block_size_efficient; +extern const unsigned int block_size_naive; __global__ void kernExtractLastElementPerBlock(int n, int *odata, const int *idata); diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index 02abe3a..3a6fe5e 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -65,11 +65,12 @@ void scan(int n, int *odata, const int *idata) { if (n <= 0) return; const int n_pad = 1 << (ilog2ceil(n)); - int num_scans = 1; - int len = n_pad; - while ((len + Common::block_size - 1) / Common::block_size > 1) { + const unsigned int block_size = Common::block_size_efficient; + int num_scans = 1; + int len = n_pad; + while ((len + block_size - 1) / block_size > 1) { ++num_scans; - len = (len + Common::block_size - 1) / Common::block_size; + len = (len + block_size - 1) / block_size; } int **dev_idata = (int **)malloc(num_scans * sizeof(int *)); @@ -85,7 +86,7 @@ void scan(int n, int *odata, const int *idata) { cudaMalloc((void **)&dev_buffer[i], len * sizeof(int)); checkCUDAError("cudaMalloc failed for dev_idata, dev_odata, dev_buffer!"); array_sizes[i] = len; - len = (len + Common::block_size - 1) / Common::block_size; + len = (len + block_size - 1) / block_size; grid_sizes[i] = len; } @@ -93,7 +94,7 @@ void scan(int n, int *odata, const int *idata) { checkCUDAError("cudaMemcpy failed for idata --> dev_idata[0]!"); /******* KERNEL INVOCATIONS *******/ - dim3 dimBlock{Common::block_size}; + dim3 dimBlock{block_size}; timer().startGpuTimer(); for (int i = 0; i < num_scans; ++i) { dim3 dimGrid{(unsigned int)grid_sizes[i]}; @@ -149,11 +150,12 @@ int compact(int n, int *odata, const int *idata) { if (n <= 0) return n; const int n_pad = 1 << (ilog2ceil(n)); - int num_scans = 1; - int len = n_pad; - while ((len + Common::block_size - 1) / Common::block_size > 1) { + const unsigned int block_size = Common::block_size_efficient; + int num_scans = 1; + int len = n_pad; + while ((len + block_size - 1) / block_size > 1) { ++num_scans; - len = (len + Common::block_size - 1) / Common::block_size; + len = (len + block_size - 1) / block_size; } // input data device allocation @@ -181,12 +183,12 @@ int compact(int n, int *odata, const int *idata) { checkCUDAError( "cudaMalloc failed for dev_iIndices, dev_oIndices, dev_buffer!"); array_sizes[i] = len; - len = (len + Common::block_size - 1) / Common::block_size; + len = (len + block_size - 1) / block_size; grid_sizes[i] = len; } /******* KERNEL INVOCATION *******/ - dim3 dimGrid{(unsigned int)grid_sizes[0]}, dimBlock{Common::block_size}; + dim3 dimGrid{(unsigned int)grid_sizes[0]}, dimBlock{block_size}; timer().startGpuTimer(); Common::kernMapToBoolean<<>>(n_pad, dev_bools, dev_idata); diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index d467e18..34a1609 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -50,11 +50,12 @@ __global__ void kernScanInclusive(int n, int *data, int *buffer) { void scan(int n, int *odata, const int *idata) { if (n <= 0) return; - int num_scans = 1; - int len = n; - while ((len + Common::block_size - 1) / Common::block_size > 1) { + const unsigned int block_size = Common::block_size_naive; + int num_scans = 1; + int len = n; + while ((len + block_size - 1) / block_size > 1) { ++num_scans; - len = (len + Common::block_size - 1) / Common::block_size; + len = (len + block_size - 1) / block_size; } int **dev_idata = (int **)malloc(num_scans * sizeof(int *)); @@ -70,7 +71,7 @@ void scan(int n, int *odata, const int *idata) { cudaMalloc((void **)&dev_buffer[i], len * sizeof(int)); checkCUDAError("cudaMalloc failed for dev_idata, dev_odata, dev_buffer!"); array_sizes[i] = len; - len = (len + Common::block_size - 1) / Common::block_size; + len = (len + block_size - 1) / block_size; grid_sizes[i] = len; } @@ -78,7 +79,7 @@ void scan(int n, int *odata, const int *idata) { checkCUDAError("cudaMemcpy failed for idata --> dev_idata[0]!"); /******* KERNEL INVOCATIONS *******/ - dim3 dimBlock{Common::block_size}; + dim3 dimBlock{block_size}; timer().startGpuTimer(); for (int i = 0; i < num_scans; ++i) { dim3 dimGrid{(unsigned int)grid_sizes[i]}; From 7a8964e38f115ee434345c024e76980f7414be07 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Mon, 20 Sep 2021 23:22:16 -0400 Subject: [PATCH 27/30] calibrated optimal block size for both modes - use srand(0) on local desktop --- stream_compaction/common.cu | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/stream_compaction/common.cu b/stream_compaction/common.cu index c03d50f..d9e0a5d 100644 --- a/stream_compaction/common.cu +++ b/stream_compaction/common.cu @@ -17,8 +17,8 @@ void checkCUDAErrorFn(const char *msg, const char *file, int line) { namespace StreamCompaction { namespace Common { -const unsigned int block_size_efficient = 1024; -const unsigned int block_size_naive = 1024; +const unsigned int block_size_efficient = 128; +const unsigned int block_size_naive = 256; __global__ void kernExtractLastElementPerBlock(int n, int *odata, const int *idata) { From 84d3ee64959a806c8b59cc55a031c759b974c124 Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Tue, 21 Sep 2021 01:01:33 -0400 Subject: [PATCH 28/30] add profiling result & data --- profiling/.gitignore | 441 +++++++++++++++++++++++++++++++++++++ profiling/.style.yapf | 2 + profiling/img/Figure_1.png | Bin 0 -> 57563 bytes profiling/img/Figure_2.png | Bin 0 -> 57668 bytes profiling/img/Figure_3.png | Bin 0 -> 43912 bytes profiling/img/Figure_4.png | Bin 0 -> 42311 bytes profiling/profile.csv | 9 + profiling/profile_raw.txt | 112 ++++++++++ profiling/visualize.py | 168 ++++++++++++++ 9 files changed, 732 insertions(+) create mode 100644 profiling/.gitignore create mode 100644 profiling/.style.yapf create mode 100644 profiling/img/Figure_1.png create mode 100644 profiling/img/Figure_2.png create mode 100644 profiling/img/Figure_3.png create mode 100644 profiling/img/Figure_4.png create mode 100644 profiling/profile.csv create mode 100644 profiling/profile_raw.txt create mode 100644 profiling/visualize.py diff --git a/profiling/.gitignore b/profiling/.gitignore new file mode 100644 index 0000000..c5adf1a --- /dev/null +++ b/profiling/.gitignore @@ -0,0 +1,441 @@ + +# Created by https://www.toptal.com/developers/gitignore/api/python,latex +# Edit at https://www.toptal.com/developers/gitignore?templates=python,latex + +### LaTeX ### +## Core latex/pdflatex auxiliary files: +*.aux +*.lof +*.log +*.lot +*.fls +*.out +*.toc +*.fmt +*.fot +*.cb +*.cb2 +.*.lb + +## Intermediate documents: +*.dvi +*.xdv +*-converted-to.* +# these rules might exclude image files for figures etc. +# *.ps +# *.eps +# *.pdf + +## Generated if empty string is given at "Please type another file name for output:" +.pdf + +## Bibliography auxiliary files (bibtex/biblatex/biber): +*.bbl +*.bcf +*.blg +*-blx.aux +*-blx.bib +*.run.xml + +## Build tool auxiliary files: +*.fdb_latexmk +*.synctex +*.synctex(busy) +*.synctex.gz +*.synctex.gz(busy) +*.pdfsync + +## Build tool directories for auxiliary files +# latexrun +latex.out/ + +## Auxiliary and intermediate files from other packages: +# algorithms +*.alg +*.loa + +# achemso +acs-*.bib + +# amsthm +*.thm + +# beamer +*.nav +*.pre +*.snm +*.vrb + +# changes +*.soc + +# comment +*.cut + +# cprotect +*.cpt + +# elsarticle (documentclass of Elsevier journals) +*.spl + +# endnotes +*.ent + +# fixme +*.lox + +# feynmf/feynmp +*.mf +*.mp +*.t[1-9] +*.t[1-9][0-9] +*.tfm + +#(r)(e)ledmac/(r)(e)ledpar +*.end +*.?end +*.[1-9] +*.[1-9][0-9] +*.[1-9][0-9][0-9] +*.[1-9]R +*.[1-9][0-9]R +*.[1-9][0-9][0-9]R +*.eledsec[1-9] +*.eledsec[1-9]R +*.eledsec[1-9][0-9] +*.eledsec[1-9][0-9]R +*.eledsec[1-9][0-9][0-9] +*.eledsec[1-9][0-9][0-9]R + +# glossaries +*.acn +*.acr +*.glg +*.glo +*.gls +*.glsdefs +*.lzo +*.lzs + +# uncomment this for glossaries-extra (will ignore makeindex's style files!) +# *.ist + +# gnuplottex +*-gnuplottex-* + +# gregoriotex +*.gaux +*.glog +*.gtex + +# htlatex +*.4ct +*.4tc +*.idv +*.lg +*.trc +*.xref + +# hyperref +*.brf + +# knitr +*-concordance.tex +# TODO Uncomment the next line if you use knitr and want to ignore its generated tikz files +# *.tikz +*-tikzDictionary + +# listings +*.lol + +# luatexja-ruby +*.ltjruby + +# makeidx +*.idx +*.ilg +*.ind + +# minitoc +*.maf +*.mlf +*.mlt +*.mtc[0-9]* +*.slf[0-9]* +*.slt[0-9]* +*.stc[0-9]* + +# minted +_minted* +*.pyg + +# morewrites +*.mw + +# newpax +*.newpax + +# nomencl +*.nlg +*.nlo +*.nls + +# pax +*.pax + +# pdfpcnotes +*.pdfpc + +# sagetex +*.sagetex.sage +*.sagetex.py +*.sagetex.scmd + +# scrwfile +*.wrt + +# sympy +*.sout +*.sympy +sympy-plots-for-*.tex/ + +# pdfcomment +*.upa +*.upb + +# pythontex +*.pytxcode +pythontex-files-*/ + +# tcolorbox +*.listing + +# thmtools +*.loe + +# TikZ & PGF +*.dpth +*.md5 +*.auxlock + +# todonotes +*.tdo + +# vhistory +*.hst +*.ver + +# easy-todo +*.lod + +# xcolor +*.xcp + +# xmpincl +*.xmpi + +# xindy +*.xdy + +# xypic precompiled matrices and outlines +*.xyc +*.xyd + +# endfloat +*.ttt +*.fff + +# Latexian +TSWLatexianTemp* + +## Editors: +# WinEdt +*.bak +*.sav + +# Texpad +.texpadtmp + +# LyX +*.lyx~ + +# Kile +*.backup + +# gummi +.*.swp + +# KBibTeX +*~[0-9]* + +# TeXnicCenter +*.tps + +# auto folder when using emacs and auctex +./auto/* +*.el + +# expex forward references with \gathertags +*-tags.tex + +# standalone packages +*.sta + +# Makeindex log files +*.lpz + +# xwatermark package +*.xwm + +# REVTeX puts footnotes in the bibliography by default, unless the nofootinbib +# option is specified. Footnotes are the stored in a file with suffix Notes.bib. +# Uncomment the next line to have this generated file ignored. +#*Notes.bib + +### LaTeX Patch ### +# LIPIcs / OASIcs +*.vtc + +# glossaries +*.glstex + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# End of https://www.toptal.com/developers/gitignore/api/python,latex diff --git a/profiling/.style.yapf b/profiling/.style.yapf new file mode 100644 index 0000000..fdd0723 --- /dev/null +++ b/profiling/.style.yapf @@ -0,0 +1,2 @@ +[style] +based_on_style = yapf diff --git a/profiling/img/Figure_1.png b/profiling/img/Figure_1.png new file mode 100644 index 0000000000000000000000000000000000000000..db1731fb12d969f2e039afa2c003685fd35382b6 GIT binary patch literal 57563 zcmeFZbx>FB*Eb5HsDL2SQYr#UcZW(TASgL!}P*5kY zpu^wLP~H}V|M1$0DcQZSG_Z5jw$VpXwW@%?;taHmj-^SM1(t@3dgNcpdmXV#E 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Nr6uJhQp9xc{x1tOc2)oY literal 0 HcmV?d00001 diff --git a/profiling/profile.csv b/profiling/profile.csv new file mode 100644 index 0000000..b770825 --- /dev/null +++ b/profiling/profile.csv @@ -0,0 +1,9 @@ +Array Size,CPU Scan (power of two),CPU Scan (non power of two),Naive Scan (power of two),Naive Scan (non power of two),Work Efficient Scan (power of two),Work Efficient Scan (non power of two),Thrust Scan (power of two),Thrust Scan (non power of two),CPU Stream Compaction without Scan (power of two),CPU Stream Compaction without Scan (non power of two),CPU Stream Compaction with Scan,Work Efficient Stream Compaction (power of two),Work Efficient Stream Compaction (non power of two) +2,7e-05,2e-05,0.028416,0.011296,0.016384,0.01616,0.022272,0.018464,5e-05,2e-05,0.000291,0.02304,0.022688 +4,9.1e-05,5e-05,0.021952,0.012672,0.016544,0.016192,0.022816,0.018112,8e-05,6e-05,0.000681,0.0232,0.0224 +8,0.00034,0.0001,0.019808,0.013184,0.030048,0.027808,0.021184,0.01776,0.000651,0.000471,0.001903,0.03424,0.033408 +10,0.000651,0.000421,0.029184,0.021184,0.027456,0.026464,0.022432,0.018464,0.002185,0.001853,0.00522,0.034048,0.033472 +15,0.017503,0.01548,0.027328,0.026848,0.03984,0.03904,0.023104,0.020384,0.062198,0.058741,0.183698,0.047296,0.045472 +20,0.653047,0.636355,0.214848,0.214144,0.269568,0.272928,0.302208,0.182688,1.85969,1.8767,6.14675,0.368096,0.454112 +25,26.3133,20.3762,5.80374,7.06064,6.83888,6.2832,1.02957,1.02858,61.6844,62.6342,235.577,9.63056,9.95472 +27,81.1722,83.0083,23.0215,18.5987,24.2505,24.2091,3.15331,3.1527,230.532,239.606,902.052,39.9164,34.9404 diff --git a/profiling/profile_raw.txt b/profiling/profile_raw.txt new file mode 100644 index 0000000..2dcd65d --- /dev/null +++ b/profiling/profile_raw.txt @@ -0,0 +1,112 @@ +2 +7e-05 +2e-05 +0.028416 +0.011296 +0.016384 +0.01616 +0.022272 +0.018464 +5e-05 +2e-05 +0.000291 +0.02304 +0.022688 +4 +9.1e-05 +5e-05 +0.021952 +0.012672 +0.016544 +0.016192 +0.022816 +0.018112 +8e-05 +6e-05 +0.000681 +0.0232 +0.0224 +8 +0.00034 +0.0001 +0.019808 +0.013184 +0.030048 +0.027808 +0.021184 +0.01776 +0.000651 +0.000471 +0.001903 +0.03424 +0.033408 +10 +0.000651 +0.000421 +0.029184 +0.021184 +0.027456 +0.026464 +0.022432 +0.018464 +0.002185 +0.001853 +0.00522 +0.034048 +0.033472 +15 +0.017503 +0.01548 +0.027328 +0.026848 +0.03984 +0.03904 +0.023104 +0.020384 +0.062198 +0.058741 +0.183698 +0.047296 +0.045472 +20 +0.653047 +0.636355 +0.214848 +0.214144 +0.269568 +0.272928 +0.302208 +0.182688 +1.85969 +1.8767 +6.14675 +0.368096 +0.454112 +25 +26.3133 +20.3762 +5.80374 +7.06064 +6.83888 +6.2832 +1.02957 +1.02858 +61.6844 +62.6342 +235.577 +9.63056 +9.95472 +27 +81.1722 +83.0083 +23.0215 +18.5987 +24.2505 +24.2091 +3.15331 +3.1527 +230.532 +239.606 +902.052 +39.9164 +34.9404 diff --git a/profiling/visualize.py b/profiling/visualize.py new file mode 100644 index 0000000..12ba5bf --- /dev/null +++ b/profiling/visualize.py @@ -0,0 +1,168 @@ +import csv +from matplotlib import pyplot as plt +import numpy as np +from numpy.core.defchararray import array + + +def process_profile_raw(): + file = open('profile_raw.txt') + + # initialize arrays + array_size = [] # in log2 scale + cpu_scan_power_of_two = [] + cpu_scan_non_power_of_two = [] + naive_scan_power_of_two = [] + naive_scan_non_power_of_two = [] + work_efficient_scan_power_of_two = [] + work_efficient_scan_non_power_of_two = [] + thrust_scan_power_of_two = [] + thrust_scan_non_power_of_two = [] + cpu_compact_power_of_two = [] + cpu_compact_non_power_of_two = [] + cpu_compact_scan = [] + work_efficient_compact_power_of_two = [] + work_efficient_compact_non_power_of_two = [] + + while True: + line = file.readline() + if not line: + break + array_size.append(int(line)) + cpu_scan_power_of_two.append(float(file.readline())) + cpu_scan_non_power_of_two.append(float(file.readline())) + naive_scan_power_of_two.append(float(file.readline())) + naive_scan_non_power_of_two.append(float(file.readline())) + work_efficient_scan_power_of_two.append(float(file.readline())) + work_efficient_scan_non_power_of_two.append(float(file.readline())) + thrust_scan_power_of_two.append(float(file.readline())) + thrust_scan_non_power_of_two.append(float(file.readline())) + cpu_compact_power_of_two.append(float(file.readline())) + cpu_compact_non_power_of_two.append(float(file.readline())) + cpu_compact_scan.append(float(file.readline())) + work_efficient_compact_power_of_two.append(float(file.readline())) + work_efficient_compact_non_power_of_two.append(float(file.readline())) + + # write to CSV for better data management + with open("profile.csv", 'w') as csv_file: + csv_writer = csv.writer(csv_file) + csv_writer.writerow([ + 'Array Size', 'CPU Scan (power of two)', 'CPU Scan (non power of two)', + 'Naive Scan (power of two)', 'Naive Scan (non power of two)', + 'Work Efficient Scan (power of two)', + 'Work Efficient Scan (non power of two)', 'Thrust Scan (power of two)', + 'Thrust Scan (non power of two)', + 'CPU Stream Compaction without Scan (power of two)', + 'CPU Stream Compaction without Scan (non power of two)', + 'CPU Stream Compaction with Scan', + 'Work Efficient Stream Compaction (power of two)', + 'Work Efficient Stream Compaction (non power of two)' + ]) + csv_writer.writerows( + zip(array_size, cpu_scan_power_of_two, cpu_scan_non_power_of_two, + naive_scan_power_of_two, naive_scan_non_power_of_two, + work_efficient_scan_power_of_two, + work_efficient_scan_non_power_of_two, thrust_scan_power_of_two, + thrust_scan_non_power_of_two, cpu_compact_power_of_two, + cpu_compact_non_power_of_two, cpu_compact_scan, + work_efficient_compact_power_of_two, + work_efficient_compact_non_power_of_two)) + + return (array_size, cpu_scan_power_of_two, cpu_scan_non_power_of_two, + naive_scan_power_of_two, naive_scan_non_power_of_two, + work_efficient_scan_power_of_two, + work_efficient_scan_non_power_of_two, thrust_scan_power_of_two, + thrust_scan_non_power_of_two, cpu_compact_power_of_two, + cpu_compact_non_power_of_two, cpu_compact_scan, + work_efficient_compact_power_of_two, + work_efficient_compact_non_power_of_two) + + +def main(): + # varying block size + efficient_block_size = [1024, 512, 256, 128, 64, 32] # pick: 128 + efficient_time = [0.714144, 0.78016, 1.33078, 0.275328, 0.48048, 0.402848] + + naive_block_size = [1024, 512, 256, 128, 64, 32] # pick: 256 + naive_time = [0.276192, 0.24032, 0.21904, 0.28976, 0.228416, 0.33616] + + (array_size, cpu_scan_power_of_two, cpu_scan_non_power_of_two, + naive_scan_power_of_two, naive_scan_non_power_of_two, + work_efficient_scan_power_of_two, work_efficient_scan_non_power_of_two, + thrust_scan_power_of_two, thrust_scan_non_power_of_two, + cpu_compact_power_of_two, cpu_compact_non_power_of_two, cpu_compact_scan, + work_efficient_compact_power_of_two, + work_efficient_compact_non_power_of_two) = process_profile_raw() + + # visualization + # power-of-two + plt.figure() + plt.plot( + np.array(array_size[4:]), np.log10(np.array(cpu_scan_power_of_two[4:])), + '.-') + plt.plot( + np.array(array_size[4:]), np.log10(np.array(naive_scan_power_of_two[4:])), + '.-') + plt.plot( + np.array(array_size[4:]), + np.log10(np.array(work_efficient_scan_power_of_two[4:])), '.-') + plt.plot( + np.array(array_size[4:]), + np.log10(np.array(thrust_scan_power_of_two[4:])), '.-') + plt.legend(['CPU', 'Naive', 'Work Efficient', 'Thrust']) + plt.xticks(array_size[4:]) + plt.xlabel('Array Size [Log2 Scale]') + plt.ylabel('Time [ms]') + plt.title('Scan Runtime vs. Array Size (power-of-two)') + + # non-power-of-two + plt.figure() + plt.plot( + np.array(array_size[4:]), + np.log10(np.array(cpu_scan_non_power_of_two[4:])), '.-') + plt.plot( + np.array(array_size[4:]), + np.log10(np.array(naive_scan_non_power_of_two[4:])), '.-') + plt.plot( + np.array(array_size[4:]), + np.log10(np.array(work_efficient_scan_non_power_of_two[4:])), '.-') + plt.plot( + np.array(array_size[4:]), + np.log10(np.array(thrust_scan_non_power_of_two[4:])), '.-') + plt.legend(['CPU', 'Naive', 'Work Efficient', 'Thrust']) + plt.xticks(array_size[4:]) + plt.xlabel('Array Size [Log2 Scale]') + plt.ylabel('Time [ms]') + plt.title('Scan Runtime vs. Array Size (non-power-of-two)') + + # compact, power-of-two + plt.figure() + plt.plot( + np.array(array_size[4:]), + np.log10(np.array(cpu_compact_power_of_two[4:])), '.-') + plt.plot( + np.array(array_size[4:]), + np.log10(np.array(work_efficient_compact_power_of_two[4:])), '.-') + plt.legend(['CPU', 'Work Efficient']) + plt.xticks(array_size[4:]) + plt.xlabel('Array Size [Log2 Scale]') + plt.ylabel('Time [ms]') + plt.title('Compaction Runtime vs. Array Size (power-of-two)') + + # compact, non-power-of-two + plt.figure() + plt.plot( + np.array(array_size[4:]), + np.log10(np.array(cpu_compact_non_power_of_two[4:])), '.-') + plt.plot( + np.array(array_size[4:]), + np.log10(np.array(work_efficient_compact_non_power_of_two[4:])), '.-') + plt.legend(['CPU', 'Work Efficient']) + plt.xticks(array_size[4:]) + plt.xlabel('Array Size [Log2 Scale]') + plt.ylabel('Time [ms]') + plt.title('Compaction Runtime vs. Array Size (non-power-of-two)') + plt.show() + + +if __name__ == '__main__': + main() From 8f5d566d6cee85d4b402a205498be03a11282ecc Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Tue, 21 Sep 2021 01:11:33 -0400 Subject: [PATCH 29/30] relabel y axis with correct unit --- profiling/img/Figure_1.png | Bin 57563 -> 58448 bytes profiling/img/Figure_2.png | Bin 57668 -> 60242 bytes profiling/img/Figure_3.png | Bin 43912 -> 44738 bytes profiling/img/Figure_4.png | Bin 42311 -> 45173 bytes profiling/visualize.py | 8 ++++---- 5 files changed, 4 insertions(+), 4 deletions(-) diff --git a/profiling/img/Figure_1.png b/profiling/img/Figure_1.png index 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Nr6uJhQp9xc{x1tOc2)oY diff --git a/profiling/visualize.py b/profiling/visualize.py index 12ba5bf..a00906d 100644 --- a/profiling/visualize.py +++ b/profiling/visualize.py @@ -111,7 +111,7 @@ def main(): plt.legend(['CPU', 'Naive', 'Work Efficient', 'Thrust']) plt.xticks(array_size[4:]) plt.xlabel('Array Size [Log2 Scale]') - plt.ylabel('Time [ms]') + plt.ylabel('Time [Log10 ms]') plt.title('Scan Runtime vs. Array Size (power-of-two)') # non-power-of-two @@ -131,7 +131,7 @@ def main(): plt.legend(['CPU', 'Naive', 'Work Efficient', 'Thrust']) plt.xticks(array_size[4:]) plt.xlabel('Array Size [Log2 Scale]') - plt.ylabel('Time [ms]') + plt.ylabel('Time [Log10 ms]') plt.title('Scan Runtime vs. Array Size (non-power-of-two)') # compact, power-of-two @@ -145,7 +145,7 @@ def main(): plt.legend(['CPU', 'Work Efficient']) plt.xticks(array_size[4:]) plt.xlabel('Array Size [Log2 Scale]') - plt.ylabel('Time [ms]') + plt.ylabel('Time [Log10 ms]') plt.title('Compaction Runtime vs. Array Size (power-of-two)') # compact, non-power-of-two @@ -159,7 +159,7 @@ def main(): plt.legend(['CPU', 'Work Efficient']) plt.xticks(array_size[4:]) plt.xlabel('Array Size [Log2 Scale]') - plt.ylabel('Time [ms]') + plt.ylabel('Time [Log10 ms]') plt.title('Compaction Runtime vs. Array Size (non-power-of-two)') plt.show() From 1419db418bfff03c1d5d23101ba720266a22715f Mon Sep 17 00:00:00 2001 From: Zhihao Ruan Date: Tue, 21 Sep 2021 01:20:33 -0400 Subject: [PATCH 30/30] add performance analysis in README --- README.md | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/README.md b/README.md index c0bf7e4..b33afca 100644 --- a/README.md +++ b/README.md @@ -111,3 +111,35 @@ After solving the problem of parallel scan, we can now get to the real algorithm ![](img/stream_compaction.png) +## Performance Analysis +**All the tests are conducted with random input array with `srand(0)` on local desktop.** + +I roughly found the optimal block size for naive scan algorithm to be 256 and work-efficient scan algorithm to be 128. With these numbers tuned, I ran the program against multiple sizes of input arrays to evaluate the performance. + +After careful evaluation, the current performance bottlenecks should be lying in: +1. Warp divergence and `__syncthreads()`. For both naive scan and work-efficient scan, the threads are utilized in an interleaved pattern, which leads to huge amount of warp divergence. +2. Global memory accesses are not coalesced. This is due to the same reason with (1), where we access global memory in an interleaved fashion. + +Further improvements to the kernel functions includes re-index active threads to minimize warp divergence, as well as breaking work-efficient scan kernel into two small kernels (up-sweep and down-sweep) to eliminate the effect of `__syncthreads()` and warp divergence. + +### Parallel Scan, Array Size Power-of-Two +In this diagram we can see that for large input data, CPU scan takes the most amount of time to run. For naive scan algorithm and work-efficient algorithm, both of them work similarly. When the data size is small, all four methods run roughly the same amount of time. Thrust outperforms all other three methods on large input data. + +![](profiling/img/Figure_1.png) + +### Parallel Scan, Array Size Non-Power-of-Two +In this diagram we can see that the four methods have roughly the same behaviors as in [array size of power of two.](#parallel-scan-array-size-power-of-two) + +![](profiling/img/Figure_2.png) + +### Stream Compaction, Array Size Power-of-Two +We can see that when data size is small, CPU compaction has roughly the same performance as work-efficient compaction. However, as the data size increases, GPU compaction outperforms CPU compaction. + +![](profiling/img/Figure_3.png) + +### Stream Compaction, Array Size Non-Power-of-Two +This diagram shows similar behaviors as in [array size of power of two.](#stream-compaction-array-size-power-of-two) + +![](profiling/img/Figure_4.png) + +