Dnia 2011-01-18, wto o godzinie 09:46 -0500, Martin Laprise pisze: > Hi, I just made some experiments with the CURAND wrappers. It seem to work > very nicely except for a little detail that I can't figure out. The > initialization of the generator and the actual random number generation seem > very fast. But for what ever reason, PyCUDA take a long time to "recover" > after the number generation. This pause is significantly longer than the > actual computation and the delay increase with N. Here is an example: >
curand kernels are called asynchronously.
This means that PyCUDA returns immediately after
initiating the call, and does not wait for result.
This allows hardware or drive to better manage
order of execution, and to run many kernels concurrently
on modern hardware (2.x capabilities).
After changing your code to force PyCUDA to wait I got
following results:
import numpy as np
import pycuda.autoinit
import pycuda.gpuarray
from pycuda.curandom import PseudoRandomNumberGenerator,
QuasiRandomNumberGenerator
import cProfile
import time as clock
cuda_stream = pycuda.driver.Stream()
def curand_prof():
N = 100000000
t1 = clock.time()
# GPU
rr = PseudoRandomNumberGenerator(0,
np.random.random(128).astype(np.int32))
data = pycuda.gpuarray.empty([N], np.float32)
rr.fill_normal_float(data.gpudata, N, stream=cuda_stream)
cuda_stream.synchronize()
t2 = clock.time()
print "Bench 1: " + str(t2-t1) + " sec"
if __name__ == "__main__":
t4 = clock.time()
curand_prof()
t5 = clock.time()
print "Bench 2: " + str(t5-t4) + " sec"
Bench 1: 1.15405488014 sec
Bench 2: 1.15947508812 sec
It seems consistent with your results - I was running on GTX 460
with Fermi. Your GTX 260 is Tesla, so 256 threads are used;
Fermi uses 1024 threads, which uses 4 times less time to compute
random numbers.
Best regards, thanks for noticing this, and thanks for testing
CURAND wrapper.
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Tomasz Rybak <[email protected]> GPG/PGP key ID: 2AD5 9860
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