-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathgtc_fall_numba_v4.py
259 lines (196 loc) · 7.03 KB
/
gtc_fall_numba_v4.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
# 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 4
# 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
_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)(_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)(_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)