NUMBA -如何使用"cuda“目标在@guvectorize中生成随机数？
NUMBA -如何使用"cuda“目标在@guvectorize中生成随机数？
提问于 2018-02-17 19:09:50
在这个(哑巴)例子中，我试图通过计算落入单位圆的(0，1) x (0，1)中随机选择的点的数量来计算π。
@guvectorize(['void(float64[:], int32, float64[:])'], '(n),()->(n)', target='cuda')
def guvec_compute_pi(arr, iters, res):
n = arr.shape[0]
for t in range(n):
inside = 0
for i in range(iters):
x = np.random.random()
y = np.random.random()
if x ** 2 + y ** 2 <= 1.0:
inside += 1
res[t] = 4.0 * inside / iters此异常在编译过程中弹出：
numba.errors.UntypedAttributeError: Failed at nopython (nopython frontend)
Unknown attribute 'random' of type Module(<module 'numpy.random' from '...'>)
File "scratch.py", line 34
[1] During: typing of get attribute at /.../scratch.py (34)我天真地认为使用RNG描述的here可以解决这个问题。我修改后的代码如下所示：
@guvectorize(['void(float64[:], int32, float64[:])'], '(n),()->(n)', target='cuda')
def guvec_compute_pi(arr, iters, res):
n = arr.shape[0]
rng = create_xoroshiro128p_states(n, seed=1)
for t in range(n):
inside = 0
for i in range(iters):
x = xoroshiro128p_uniform_float64(rng, t)
y = xoroshiro128p_uniform_float64(rng, t)
if x ** 2 + y ** 2 <= 1.0:
inside += 1
res[t] = 4.0 * inside / iters然而，一个类似的错误将会弹出：
numba.errors.TypingError: Failed at nopython (nopython frontend)
Untyped global name 'create_xoroshiro128p_states': cannot determine Numba type of <class 'function'>
File "scratch.py", line 28当我尝试更改为target='parallel'时，无论是否使用nopython=True，使用numpy.random.random的原始代码都工作得很好。target='cuda'出现问题的原因是什么?有没有办法在@guvectorize-d块中获取随机数？
回答 1
Stack Overflow用户
回答已采纳
发布于 2018-07-04 03:24:59
函数create_xoroshiro128p_states打算在CPU上运行，如Numba文档中的此示例所示，重复如下：
from __future__ import print_function, absolute_import
from numba import cuda
from numba.cuda.random import create_xoroshiro128p_states,
xoroshiro128p_uniform_float32
import numpy as np
@cuda.jit
def compute_pi(rng_states, iterations, out):
"""Find the maximum value in values and store in result[0]"""
thread_id = cuda.grid(1)
# Compute pi by drawing random (x, y) points and finding what
# fraction lie inside a unit circle
inside = 0
for i in range(iterations):
x = xoroshiro128p_uniform_float32(rng_states, thread_id)
y = xoroshiro128p_uniform_float32(rng_states, thread_id)
if x**2 + y**2 <= 1.0:
inside += 1
out[thread_id] = 4.0 * inside / iterations
threads_per_block = 64
blocks = 24
rng_states = create_xoroshiro128p_states(threads_per_block * blocks, seed=1)
out = np.zeros(threads_per_block * blocks, dtype=np.float32)
compute_pi[blocks, threads_per_block](rng_states, 10000, out)
print('pi:', out.mean())它生成一个随机初始化数据数组，使GPU上的随机数生成独立于多个线程。这些数据最终到达了设备端，这有点令人困惑。但是它允许你将随机状态数据传递给你的GPU内核。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/48840654
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