How to measure your dataflow using cupy & numpy 2018/04/18 SAKURA Internet, Inc. Research Center SR / Naoto MATSUMOTO

1. How to measure your dataflow
using cupy & numpy
2018/04/18 SAKURA Internet, Inc. Research Center SR / Naoto MATSUMOTO
(C) Copyright 1996-2018 SAKURA Internet Inc

2. How to measure your dataflow using cupy & numpy
2
R = randint(0,100,600000000)
R = randint(0,100,600000000)
a = cp.array(R, dtype=np.uint8) 2.27 sec
a = np.array(R, dtype=np.uint8) 0.46 sec
cp.sort(a)
np.sort(a)
numpy
cupy
SOURCE: SAKURA Internet Research Center. (04/2018) Project Sprig.
import time
import cupy as cp
import numpy as np
from numpy.random import *
start = time.time()
R = randint(0,100,600000000)
end = time.time()
print ( end - start )
start = time.time()
a = np.array(R, dtype=np.uint8)
end = time.time()
print ( end - start )
start = time.time()
np.sort(a)
end = time.time()
print ( end - start )
import time
import cupy as cp
import numpy as np
from numpy.random import *
start = time.time()
R = randint(0,100,600000000)
end = time.time()
print ( end - start )
start = time.time()
a = cp.array(R, dtype=cp.uint8)
end = time.time()
print ( end - start )
start = time.time()
cp.sort(a)
end = time.time()
print ( end - start )
5.36 sec 15.1sec
5.36 sec 0.54 sec