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Numpy VS. Cupy
import time
import cupy
import numpy
cnt = 100
N = 10
shapes = ((N, N), (N, N, N, N), (N, N, N, N, N, N), (N, N, N, N, N, N, N))
def meas_cupy(func, operand):
stream = cupy.cuda.Stream.null
start = stream.record()
for i in range(cnt):
func(operand, xp=cupy)
end = stream.record()
end.synchronize()
elapsed = cupy.cuda.get_elapsed_time(start, end) / cnt
return elapsed
def meas_numpy(func, operand):
start = time.time()
for i in range(cnt):
func(operand, xp=numpy)
end = time.time()
elapsed = (end - start) * 1000 / cnt
return elapsed
def meas_numpy_cupy(func):
meas_results = []
print("Array size,Numpy,Cupy")
for shape in shapes:
A = cupy.random.rand(*shape)
a = A.get()
cupy_elapsed = meas_cupy(func, A)
numpy_elapsed = meas_numpy(func, a)
meas_results.append((a.size, numpy_elapsed, cupy_elapsed))
for result in meas_results:
print("{0},{1},{2}".format(*result))
def array_add(A, xp):
return A + A
print("Array add")
meas_numpy_cupy(array_add)
def array_sub(A, xp):
return A - A
print("Array sub")
meas_numpy_cupy(array_sub)
def array_sum(A, xp):
return A.sum()
print("Array sum")
meas_numpy_cupy(array_sum)
def array_argmax(A, xp):
return A.argmax()
print("Array argmax")
meas_numpy_cupy(array_argmax)
def array_sort(A, xp):
return xp.sort(A)
print("Array sort")
meas_numpy_cupy(array_sort)
shapes = ((N, N), (N, N, N, N), (N, N, N, N, N, N))
def array_tensordot(A, xp):
return xp.tensordot(A, A)
print("Array tensordot")
meas_numpy_cupy(array_tensordot)
def array_matmul(A, xp):
return xp.matmul(A, A)
print("Array matmul")
meas_numpy_cupy(array_matmul)
def array_einsum(A, xp):
return xp.einsum("...i, ...j->...ij", A, A)
print("Array einsum")
meas_numpy_cupy(array_einsum)
def array_moveaxis(A, xp):
return xp.moveaxis(A, 0, -1)
print("Array transpose")
meas_numpy_cupy(array_moveaxis)
def array_sin(A, xp):
return xp.sin(A)
print("Array sin")
meas_numpy_cupy(array_sin)
shapes = ((N, N), (N * N, N * N), (N * N * N, N * N * N))
def array_eigh(A, xp):
return xp.linalg.eigh(A)
print("Array eigenvalue")
meas_numpy_cupy(array_eigh)
def array_inv(A, xp):
return xp.linalg.inv(A)
print("Array inv")
meas_numpy_cupy(array_inv)
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