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Dispatcher.cpp
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Dispatcher.cpp
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#include "Dispatcher.hpp"
// Includes
#include <stdexcept>
#include <iostream>
#include <thread>
#include <sstream>
#include <iomanip>
#include <random>
#include <thread>
#include <algorithm>
#include "precomp.hpp"
static std::string toHex(const uint8_t * const s, const size_t len) {
std::string b("0123456789abcdef");
std::string r;
for (size_t i = 0; i < len; ++i) {
const unsigned char h = s[i] / 16;
const unsigned char l = s[i] % 16;
r = r + b.substr(h, 1) + b.substr(l, 1);
}
return r;
}
static void printResult(cl_ulong4 seed, cl_ulong round, result r, cl_uchar score, const std::chrono::time_point<std::chrono::steady_clock> & timeStart, const Mode & mode) {
// Time delta
const auto seconds = std::chrono::duration_cast<std::chrono::seconds>(std::chrono::steady_clock::now() - timeStart).count();
// Format private key
cl_ulong carry = 0;
cl_ulong4 seedRes;
seedRes.s[0] = seed.s[0] + round; carry = seedRes.s[0] < round;
seedRes.s[1] = seed.s[1] + carry; carry = !seedRes.s[1];
seedRes.s[2] = seed.s[2] + carry; carry = !seedRes.s[2];
seedRes.s[3] = seed.s[3] + carry + r.foundId;
std::ostringstream ss;
ss << std::hex << std::setfill('0');
ss << std::setw(16) << seedRes.s[3] << std::setw(16) << seedRes.s[2] << std::setw(16) << seedRes.s[1] << std::setw(16) << seedRes.s[0];
const std::string strPrivate = ss.str();
// Format public key
std::string strPublic = toHex(r.foundHash, 20);
if(mode.name == "matching"){
strPublic = toString(r.foundHash);
}
// Print
const std::string strVT100ClearLine = "\33[2K\r";
std::cout << strVT100ClearLine << " Time: " << std::setw(5) << seconds << "s Score: " << std::setw(2) << (int) score << " Private: 0x" << strPrivate << ' ';
std::cout << mode.transformName();
std::cout << ": " << strPublic << std::endl;
}
unsigned int getKernelExecutionTimeMicros(cl_event & e) {
cl_ulong timeStart = 0, timeEnd = 0;
clWaitForEvents(1, &e);
clGetEventProfilingInfo(e, CL_PROFILING_COMMAND_START, sizeof(timeStart), &timeStart, NULL);
clGetEventProfilingInfo(e, CL_PROFILING_COMMAND_END, sizeof(timeEnd), &timeEnd, NULL);
return (timeEnd - timeStart) / 1000;
}
Dispatcher::OpenCLException::OpenCLException(const std::string s, const cl_int res) :
std::runtime_error( s + " (res = " + toString(res) + ")"),
m_res(res)
{
}
void Dispatcher::OpenCLException::OpenCLException::throwIfError(const std::string s, const cl_int res) {
if (res != CL_SUCCESS) {
throw OpenCLException(s, res);
}
}
cl_command_queue Dispatcher::Device::createQueue(cl_context & clContext, cl_device_id & clDeviceId) {
// nVidia CUDA Toolkit 10.1 only supports OpenCL 1.2 so we revert back to older functions for compatability
#ifdef PROFANITY_DEBUG
cl_command_queue_properties p = CL_QUEUE_PROFILING_ENABLE;
#else
cl_command_queue_properties p = NULL;
#endif
#ifdef CL_VERSION_2_0
const cl_command_queue ret = clCreateCommandQueueWithProperties(clContext, clDeviceId, &p, NULL);
#else
const cl_command_queue ret = clCreateCommandQueue(clContext, clDeviceId, p, NULL);
#endif
return ret == NULL ? throw std::runtime_error("failed to create command queue") : ret;
}
cl_kernel Dispatcher::Device::createKernel(cl_program & clProgram, const std::string s) {
cl_kernel ret = clCreateKernel(clProgram, s.c_str(), NULL);
return ret == NULL ? throw std::runtime_error("failed to create kernel \"" + s + "\"") : ret;
}
cl_ulong4 Dispatcher::Device::createSeed() {
#ifdef PROFANITY_DEBUG
cl_ulong4 r;
r.s[0] = 1;
r.s[1] = 1;
r.s[2] = 1;
r.s[3] = 1;
return r;
#else
// Randomize private keys
std::random_device rd;
std::mt19937_64 eng(rd());
std::uniform_int_distribution<cl_ulong> distr;
cl_ulong4 r;
r.s[0] = distr(eng);
r.s[1] = distr(eng);
r.s[2] = distr(eng);
r.s[3] = distr(eng);
return r;
#endif
}
Dispatcher::Device::Device(Dispatcher & parent, cl_context & clContext, cl_program & clProgram, cl_device_id clDeviceId, const size_t worksizeLocal, const size_t size, const size_t index, const Mode & mode) :
m_parent(parent),
m_index(index),
m_clDeviceId(clDeviceId),
m_worksizeLocal(worksizeLocal),
m_clScoreMax(0),
m_clQueue(createQueue(clContext, clDeviceId) ),
m_kernelInit( createKernel(clProgram, "profanity_init") ),
m_kernelInverse(createKernel(clProgram, "profanity_inverse")),
m_kernelIterate(createKernel(clProgram, "profanity_iterate")),
m_kernelTransform( mode.transformKernel() == "" ? NULL : createKernel(clProgram, mode.transformKernel())),
m_kernelScore(createKernel(clProgram, mode.kernel)),
m_memPrecomp(clContext, m_clQueue, CL_MEM_READ_ONLY | CL_MEM_HOST_WRITE_ONLY, sizeof(g_precomp), g_precomp),
m_memPointsDeltaX(clContext, m_clQueue, CL_MEM_READ_WRITE | CL_MEM_HOST_NO_ACCESS, size, true),
m_memInversedNegativeDoubleGy(clContext, m_clQueue, CL_MEM_READ_WRITE | CL_MEM_HOST_NO_ACCESS, size, true),
m_memPrevLambda(clContext, m_clQueue, CL_MEM_READ_WRITE | CL_MEM_HOST_NO_ACCESS, size, true),
m_memResult(clContext, m_clQueue, CL_MEM_READ_WRITE | CL_MEM_HOST_READ_ONLY, PROFANITY_MAX_SCORE + 1),
m_memData1(clContext, m_clQueue, CL_MEM_READ_ONLY | CL_MEM_HOST_WRITE_ONLY, 20 * 100),
m_memData2(clContext, m_clQueue, CL_MEM_READ_ONLY | CL_MEM_HOST_WRITE_ONLY, 20 * 100),
m_clSeed(createSeed()),
m_round(0),
m_speed(PROFANITY_SPEEDSAMPLES),
m_sizeInitialized(0),
m_eventFinished(NULL)
{
}
Dispatcher::Device::~Device() {
}
Dispatcher::Dispatcher(cl_context & clContext, cl_program & clProgram, const Mode mode, const size_t worksizeMax, const size_t inverseSize, const size_t inverseMultiple, const cl_uchar clScoreQuit)
: m_clContext(clContext), m_clProgram(clProgram), m_mode(mode), m_worksizeMax(worksizeMax), m_inverseSize(inverseSize), m_size(inverseSize*inverseMultiple), m_clScoreMax(mode.score), m_clScoreQuit(clScoreQuit), m_eventFinished(NULL), m_countPrint(0) {
}
Dispatcher::~Dispatcher() {
}
void Dispatcher::addDevice(cl_device_id clDeviceId, const size_t worksizeLocal, const size_t index) {
Device * pDevice = new Device(*this, m_clContext, m_clProgram, clDeviceId, worksizeLocal, m_size, index, m_mode);
m_vDevices.push_back(pDevice);
}
void Dispatcher::run() {
m_eventFinished = clCreateUserEvent(m_clContext, NULL);
timeStart = std::chrono::steady_clock::now();
init();
const auto timeInitialization = std::chrono::duration_cast<std::chrono::seconds>(std::chrono::steady_clock::now() - timeStart).count();
std::cout << "Initialization time: " << timeInitialization << " seconds" << std::endl;
m_quit = false;
m_countRunning = m_vDevices.size();
std::cout << "Running..." << std::endl;
std::cout << " Always verify that a private key generated by this program corresponds to the" << std::endl;
std::cout << " public key printed by importing it to a wallet of your choice. This program" << std::endl;
std::cout << " like any software might contain bugs and it does by design cut corners to" << std::endl;
std::cout << " improve overall performance." << std::endl;
std::cout << std::endl;
for (auto it = m_vDevices.begin(); it != m_vDevices.end(); ++it) {
dispatch(*(*it));
}
clWaitForEvents(1, &m_eventFinished);
clReleaseEvent(m_eventFinished);
m_eventFinished = NULL;
}
void Dispatcher::init() {
std::cout << "Initializing devices..." << std::endl;
std::cout << " This should take less than a minute. The number of objects initialized on each" << std::endl;
std::cout << " device is equal to inverse-size * inverse-multiple. To lower" << std::endl;
std::cout << " initialization time (and memory footprint) I suggest lowering the" << std::endl;
std::cout << " inverse-multiple first. You can do this via the -I switch. Do note that" << std::endl;
std::cout << " this might negatively impact your performance." << std::endl;
std::cout << std::endl;
const auto deviceCount = m_vDevices.size();
m_sizeInitTotal = m_size * deviceCount;
m_sizeInitDone = 0;
cl_event * const pInitEvents = new cl_event[deviceCount];
for (size_t i = 0; i < deviceCount; ++i) {
pInitEvents[i] = clCreateUserEvent(m_clContext, NULL);
m_vDevices[i]->m_eventFinished = pInitEvents[i];
initBegin(*m_vDevices[i]);
}
clWaitForEvents(deviceCount, pInitEvents);
for (size_t i = 0; i < deviceCount; ++i) {
m_vDevices[i]->m_eventFinished = NULL;
clReleaseEvent(pInitEvents[i]);
}
delete[] pInitEvents;
std::cout << std::endl;
}
void Dispatcher::initBegin(Device & d) {
// Set mode data
for (auto i = 0; i < 20 * 100; ++i) {
d.m_memData1[i] = m_mode.data1[i];
d.m_memData2[i] = m_mode.data2[i];
}
// Write precompute table and mode data
d.m_memPrecomp.write(true);
d.m_memData1.write(true);
d.m_memData2.write(true);
// Kernel arguments - profanity_begin
d.m_memPrecomp.setKernelArg(d.m_kernelInit, 0);
d.m_memPointsDeltaX.setKernelArg(d.m_kernelInit, 1);
d.m_memPrevLambda.setKernelArg(d.m_kernelInit, 2);
d.m_memResult.setKernelArg(d.m_kernelInit, 3);
CLMemory<cl_ulong4>::setKernelArg(d.m_kernelInit, 4, d.m_clSeed);
// Kernel arguments - profanity_inverse
d.m_memPointsDeltaX.setKernelArg(d.m_kernelInverse, 0);
d.m_memInversedNegativeDoubleGy.setKernelArg(d.m_kernelInverse, 1);
// Kernel arguments - profanity_iterate
d.m_memPointsDeltaX.setKernelArg(d.m_kernelIterate, 0);
d.m_memInversedNegativeDoubleGy.setKernelArg(d.m_kernelIterate, 1);
d.m_memPrevLambda.setKernelArg(d.m_kernelIterate, 2);
// Kernel arguments - profanity_transform_*
if(d.m_kernelTransform) {
d.m_memInversedNegativeDoubleGy.setKernelArg(d.m_kernelTransform, 0);
}
// Kernel arguments - profanity_score_*
d.m_memInversedNegativeDoubleGy.setKernelArg(d.m_kernelScore, 0);
d.m_memResult.setKernelArg(d.m_kernelScore, 1);
d.m_memData1.setKernelArg(d.m_kernelScore, 2);
d.m_memData2.setKernelArg(d.m_kernelScore, 3);
CLMemory<cl_uchar>::setKernelArg(d.m_kernelScore, 4, d.m_clScoreMax); // Updated in handleResult()
// Seed device
initContinue(d);
}
void Dispatcher::initContinue(Device & d) {
size_t sizeLeft = m_size - d.m_sizeInitialized;
const size_t sizeInitLimit = m_size / 20;
// Print progress
const size_t percentDone = m_sizeInitDone * 100 / m_sizeInitTotal;
std::cout << " " << percentDone << "%\r" << std::flush;
if (sizeLeft) {
cl_event event;
const size_t sizeRun = std::min(sizeInitLimit, std::min(sizeLeft, m_worksizeMax));
const auto resEnqueue = clEnqueueNDRangeKernel(d.m_clQueue, d.m_kernelInit, 1, &d.m_sizeInitialized, &sizeRun, NULL, 0, NULL, &event);
OpenCLException::throwIfError("kernel queueing failed during initilization", resEnqueue);
// See: https://www.khronos.org/registry/OpenCL/sdk/1.2/docs/man/xhtml/clSetEventCallback.html
// If an application needs to wait for completion of a routine from the above list in a callback, please use the non-blocking form of the function, and
// assign a completion callback to it to do the remainder of your work. Note that when a callback (or other code) enqueues commands to a command-queue,
// the commands are not required to begin execution until the queue is flushed. In standard usage, blocking enqueue calls serve this role by implicitly
// flushing the queue. Since blocking calls are not permitted in callbacks, those callbacks that enqueue commands on a command queue should either call
// clFlush on the queue before returning or arrange for clFlush to be called later on another thread.
clFlush(d.m_clQueue);
std::lock_guard<std::mutex> lock(m_mutex);
d.m_sizeInitialized += sizeRun;
m_sizeInitDone += sizeRun;
const auto resCallback = clSetEventCallback(event, CL_COMPLETE, staticCallback, &d);
OpenCLException::throwIfError("failed to set custom callback during initialization", resCallback);
} else {
// Printing one whole string at once helps in avoiding garbled output when executed in parallell
const std::string strOutput = " GPU" + toString(d.m_index) + " initialized";
std::cout << strOutput << std::endl;
clSetUserEventStatus(d.m_eventFinished, CL_COMPLETE);
}
}
void Dispatcher::enqueueKernel(cl_command_queue & clQueue, cl_kernel & clKernel, size_t worksizeGlobal, const size_t worksizeLocal, cl_event * pEvent = NULL) {
const size_t worksizeMax = m_worksizeMax;
size_t worksizeOffset = 0;
while (worksizeGlobal) {
const size_t worksizeRun = std::min(worksizeGlobal, worksizeMax);
const size_t * const pWorksizeLocal = (worksizeLocal == 0 ? NULL : &worksizeLocal);
const auto res = clEnqueueNDRangeKernel(clQueue, clKernel, 1, &worksizeOffset, &worksizeRun, pWorksizeLocal, 0, NULL, pEvent);
OpenCLException::throwIfError("kernel queueing failed", res);
worksizeGlobal -= worksizeRun;
worksizeOffset += worksizeRun;
}
}
void Dispatcher::enqueueKernelDevice(Device & d, cl_kernel & clKernel, size_t worksizeGlobal, cl_event * pEvent = NULL) {
try {
enqueueKernel(d.m_clQueue, clKernel, worksizeGlobal, d.m_worksizeLocal, pEvent);
} catch ( OpenCLException & e ) {
// If local work size is invalid, abandon it and let implementation decide
if ((e.m_res == CL_INVALID_WORK_GROUP_SIZE || e.m_res == CL_INVALID_WORK_ITEM_SIZE) && d.m_worksizeLocal != 0) {
std::cout << std::endl << "warning: local work size abandoned on GPU" << d.m_index << std::endl;
d.m_worksizeLocal = 0;
enqueueKernel(d.m_clQueue, clKernel, worksizeGlobal, d.m_worksizeLocal, pEvent);
}
else {
throw;
}
}
}
void Dispatcher::dispatch(Device & d) {
cl_event event;
d.m_memResult.read(false, &event);
#ifdef PROFANITY_DEBUG
cl_event eventInverse;
cl_event eventIterate;
enqueueKernelDevice(d, d.m_kernelInverse, m_size / m_inverseSize, &eventInverse);
enqueueKernelDevice(d, d.m_kernelIterate, m_size, &eventIterate);
#else
enqueueKernelDevice(d, d.m_kernelInverse, m_size / m_inverseSize);
enqueueKernelDevice(d, d.m_kernelIterate, m_size);
#endif
if (d.m_kernelTransform) {
enqueueKernelDevice(d, d.m_kernelTransform, m_size);
}
enqueueKernelDevice(d, d.m_kernelScore, m_size);
clFlush(d.m_clQueue);
#ifdef PROFANITY_DEBUG
// We're actually not allowed to call clFinish here because this function is ultimately asynchronously called by OpenCL.
// However, this happens to work on my computer and it's not really intended for release, just something to aid me in
// optimizations.
clFinish(d.m_clQueue);
std::cout << "Timing: profanity_inverse = " << getKernelExecutionTimeMicros(eventInverse) << "us, profanity_iterate = " << getKernelExecutionTimeMicros(eventIterate) << "us" << std::endl;
#endif
const auto res = clSetEventCallback(event, CL_COMPLETE, staticCallback, &d);
OpenCLException::throwIfError("failed to set custom callback", res);
}
void Dispatcher::handleResult(Device & d) {
for (auto i = PROFANITY_MAX_SCORE; i > m_clScoreMax; --i) {
result & r = d.m_memResult[i];
if (r.found > 0 && i >= d.m_clScoreMax) {
d.m_clScoreMax = i;
CLMemory<cl_uchar>::setKernelArg(d.m_kernelScore, 4, d.m_clScoreMax);
std::lock_guard<std::mutex> lock(m_mutex);
if (i >= m_clScoreMax) {
m_clScoreMax = i;
if (m_clScoreQuit && i >= m_clScoreQuit) {
m_quit = true;
}
printResult(d.m_clSeed, d.m_round, r, i, timeStart, m_mode);
}
break;
}
}
}
void Dispatcher::onEvent(cl_event event, cl_int status, Device & d) {
if (status != CL_COMPLETE) {
std::cout << "Dispatcher::onEvent - Got bad status: " << status << std::endl;
}
else if (d.m_eventFinished != NULL) {
initContinue(d);
} else {
++d.m_round;
handleResult(d);
bool bDispatch = true;
{
std::lock_guard<std::mutex> lock(m_mutex);
d.m_speed.sample(m_size);
printSpeed();
if( m_quit ) {
bDispatch = false;
if(--m_countRunning == 0) {
clSetUserEventStatus(m_eventFinished, CL_COMPLETE);
}
}
}
if (bDispatch) {
dispatch(d);
}
}
}
// This is run when m_mutex is held.
void Dispatcher::printSpeed() {
++m_countPrint;
if( m_countPrint > m_vDevices.size() ) {
std::string strGPUs;
double speedTotal = 0;
unsigned int i = 0;
for (auto & e : m_vDevices) {
const auto curSpeed = e->m_speed.getSpeed();
speedTotal += curSpeed;
strGPUs += " GPU" + toString(e->m_index) + ": " + formatSpeed(curSpeed);
++i;
}
const std::string strVT100ClearLine = "\33[2K\r";
std::cerr << strVT100ClearLine << "Total: " << formatSpeed(speedTotal) << " -" << strGPUs << '\r' << std::flush;
m_countPrint = 0;
}
}
void CL_CALLBACK Dispatcher::staticCallback(cl_event event, cl_int event_command_exec_status, void * user_data) {
Device * const pDevice = static_cast<Device *>(user_data);
pDevice->m_parent.onEvent(event, event_command_exec_status, *pDevice);
clReleaseEvent(event);
}
std::string Dispatcher::formatSpeed(double f) {
const std::string S = " KMGT";
unsigned int index = 0;
while (f > 1000.0f && index < S.size()) {
f /= 1000.0f;
++index;
}
std::ostringstream ss;
ss << std::fixed << std::setprecision(3) << (double)f << " " << S[index] << "H/s";
return ss.str();
}