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gtc_fall_numba_v5.py
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gtc_fall_numba_v5.py
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# Copyright (c) 2019-2020, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cupy as cp
import numpy as np
import sys
from cupy import prof
from math import sin, cos, atan2
from numba import cuda, void, int32, float32, float64, boolean
from scipy import signal
# Numba: Version 5
# Implementation a user level cache from version 2.
# Seperates 32 bit and 64 bit versions to
# reduce register pressure from version 3.
# Allows --use_fast_math flag to kernel compile from version 4.
# Add max registers parameters
_kernel_cache = {}
def _numba_lombscargle_32(x, y, freqs, pgram, y_dot):
dtype = float32
F = int32(cuda.grid(1))
strideF = int32(cuda.gridsize(1))
if not y_dot[0]:
yD = dtype(1.0)
else:
yD = dtype(2.0 / y_dot[0])
for i in range(F, int32(freqs.shape[0]), strideF):
# Copy data to registers
freq = dtype(freqs[i])
xc = dtype(0.0)
xs = dtype(0.0)
cc = dtype(0.0)
ss = dtype(0.0)
cs = dtype(0.0)
for j in range(int32(x.shape[0])):
c = dtype(cos(dtype(freq * x[j])))
s = dtype(sin(dtype(freq * x[j])))
xc += dtype(y[j] * c)
xs += dtype(y[j] * s)
cc += dtype(c * c)
ss += dtype(s * s)
cs += dtype(c * s)
tau = dtype(atan2(dtype(2.0 * cs), dtype(cc - ss)) / dtype(2.0 * freq))
c_tau = dtype(cos(freq * tau))
s_tau = dtype(sin(freq * tau))
c_tau2 = dtype(c_tau * c_tau)
s_tau2 = dtype(s_tau * s_tau)
cs_tau = dtype(2.0 * dtype(c_tau * s_tau))
pgram[i] = dtype(
0.5
* dtype(
dtype(
dtype(dtype(c_tau * xc) + dtype(s_tau * xs)) ** 2
/ dtype(dtype(c_tau2 * cc) + dtype(cs_tau * cs) + dtype(s_tau2 * ss))
)
+ dtype(
dtype(dtype(c_tau * xs) - dtype(s_tau * xc)) ** 2
/ dtype(dtype(c_tau2 * ss) - dtype(cs_tau * cs) + dtype(s_tau2 * cc))
)
)
) * yD
def _numba_lombscargle_64(x, y, freqs, pgram, y_dot):
dtype = float64
F = int32(cuda.grid(1))
strideF = int32(cuda.gridsize(1))
if not y_dot[0]:
yD = dtype(1.0)
else:
yD = dtype(2.0 / y_dot[0])
for i in range(F, int32(freqs.shape[0]), strideF):
# Copy data to registers
freq = dtype(freqs[i])
xc = dtype(0.0)
xs = dtype(0.0)
cc = dtype(0.0)
ss = dtype(0.0)
cs = dtype(0.0)
for j in range(int32(x.shape[0])):
c = dtype(cos(dtype(freq * x[j])))
s = dtype(sin(dtype(freq * x[j])))
xc += dtype(y[j] * c)
xs += dtype(y[j] * s)
cc += dtype(c * c)
ss += dtype(s * s)
cs += dtype(c * s)
tau = dtype(atan2(dtype(2.0 * cs), dtype(cc - ss)) / dtype(2.0 * freq))
c_tau = dtype(cos(freq * tau))
s_tau = dtype(sin(freq * tau))
c_tau2 = dtype(c_tau * c_tau)
s_tau2 = dtype(s_tau * s_tau)
cs_tau = dtype(2.0 * dtype(c_tau * s_tau))
pgram[i] = dtype(
0.5
* dtype(
dtype(
dtype(dtype(c_tau * xc) + dtype(s_tau * xs)) ** 2
/ dtype(dtype(c_tau2 * cc) + dtype(cs_tau * cs) + dtype(s_tau2 * ss))
)
+ dtype(
dtype(dtype(c_tau * xs) - dtype(s_tau * xc)) ** 2
/ dtype(dtype(c_tau2 * ss) - dtype(cs_tau * cs) + dtype(s_tau2 * cc))
)
)
) * yD
def _numba_lombscargle_signature(ty):
return void(
ty[::1], ty[::1], ty[::1], ty[::1], ty[::1], # x # y # freqs # pgram # y_dot
)
def _lombscargle(x, y, freqs, pgram, y_dot):
if (pgram.dtype == 'float32'):
numba_type = float32
elif (pgram.dtype == 'float64'):
numba_type = float64
if (str(numba_type)) in _kernel_cache:
kernel = _kernel_cache[(str(numba_type))]
else:
sig = _numba_lombscargle_signature(numba_type)
if (pgram.dtype == 'float32'):
kernel = _kernel_cache[(str(numba_type))] = cuda.jit(sig, fastmath=True, max_registers=32)(_numba_lombscargle_32)
print("Registers", kernel._func.get().attrs.regs)
elif (pgram.dtype == 'float64'):
kernel = _kernel_cache[(str(numba_type))] = cuda.jit(sig, fastmath=True, max_registers=64)(_numba_lombscargle_64)
print("Registers", kernel._func.get().attrs.regs)
device_id = cp.cuda.Device()
numSM = device_id.attributes["MultiProcessorCount"]
threadsperblock = (128, )
blockspergrid = (numSM * 20,)
kernel[blockspergrid, threadsperblock](x, y, freqs, pgram, y_dot)
cuda.synchronize()
def lombscargle(
x,
y,
freqs,
precenter=False,
normalize=False,
):
pgram = cuda.device_array_like(freqs)
assert x.ndim == 1
assert y.ndim == 1
assert freqs.ndim == 1
# Check input sizes
if x.shape[0] != y.shape[0]:
raise ValueError("Input arrays do not have the same size.")
y_dot = cuda.device_array(shape=(1,), dtype=y.dtype)
if normalize:
cp.dot(y, y, out=y_dot)
if precenter:
y_in = y - y.mean()
else:
y_in = y
_lombscargle(x, y_in, freqs, pgram, y_dot)
return pgram
if __name__ == "__main__":
dtype = sys.argv[1]
loops = int(sys.argv[2])
A = 2.0
w = 1.0
phi = 0.5 * np.pi
frac_points = 0.9 # Fraction of points to select
in_samps = 2 ** 10
out_samps = 2 ** 20
np.random.seed(1234)
r = np.random.rand(in_samps)
x = np.linspace(0.01, 10 * np.pi, in_samps)
x = x[r >= frac_points]
y = A * np.cos(w * x + phi)
f = np.linspace(0.01, 10, out_samps)
# Use float32 if b32 passed
if dtype == 'float32':
x = x.astype(np.float32)
y = y.astype(np.float32)
f = f.astype(np.float32)
d_x = cuda.to_device(x)
d_y = cuda.to_device(y)
d_f = cuda.to_device(f)
# Run baseline with scipy.signal.lombscargle
with prof.time_range("scipy_lombscargle", 0):
cpu_lombscargle = signal.lombscargle(x, y, f)
# Run Numba version
with prof.time_range("numba_lombscargle", 1):
gpu_lombscargle = lombscargle(d_x, d_y, d_f)
# Copy result to host
gpu_lombscargle = gpu_lombscargle.copy_to_host()
# Compare results
np.testing.assert_allclose(cpu_lombscargle, gpu_lombscargle, 1e-3)
# Run multiple passes to get average
for _ in range(loops):
with prof.time_range("numba_lombscargle_loop", 2):
gpu_lombscargle = lombscargle(d_x, d_y, d_f)