pyopencl global_work_offset 内核参数

Question

我想使用来自 OpenCL API 函数 clEnqueueNDRangeKernel 的 global_work_offset 参数。我不知道如何在pyopencl API 中做到这一点。这是一个演示代码，我想在内核调用中添加 2 的偏移量，因此 get_global_id(0) 从 2 而不是 0 开始：

import pyopencl as cl 
import pyopencl.array 
import numpy as np

platform = cl.get_platforms()[0]
devices = platform.get_devices()[1] #gpu
context = cl.Context(devices=[devices])
queue =  cl.CommandQueue(context)

kernel = cl.Program(context, """
    __kernel void derp(global char* a) {
        a[get_global_id(0)] = 1;
    }""").build()

buffarr = cl.array.zeros(queue, 4, dtype=np.uint8)
kernel.derp(queue, (2,), None, buffarr.data)

np_data = buffarr.get()

# within this demo the buffer contains currently [1,1,0,0]
assert np.array_equal(np_data, [0,0,1,1])

如何更改代码以使断言不会失败？我不想在内核代码中添加额外的参数。

Answer 1

作为documentation，您可以将global_offset 作为命名参数传递。

内核的调用变成：

kernel.derp(queue, (4, 1), None, buffarr.data, global_offset=[2, 0])

有变化的程序：

import pyopencl as cl
import pyopencl.array
import numpy as np


platform = cl.get_platforms()[2]
print(platform)
devices = platform.get_devices()[0] #gpu
context = cl.Context(devices=[devices])
queue =  cl.CommandQueue(context)

kernel = cl.Program(context, """
    __kernel void derp(global char* a) {
        a[get_global_id(0)] = 1;
    }""").build()


buffarr = cl.array.zeros(queue, 4, dtype=np.uint8)

# (4, 1) ==> shape of the buffer
kernel.derp(queue, (4, 1), None, buffarr.data, global_offset=[2, 0])

np_data = buffarr.get()
print(np_data)
# within this demo the buffer contains currently [1,1,0,0]
assert np.array_equal(np_data, [0,0,1,1])
print("Ok")

执行后：

在设备 0 上

<pyopencl.Platform 'Intel(R) OpenCL' at 0x60bdc0>
[0 0 1 1]
Ok

在设备 1 上

<pyopencl.Platform 'Experimental OpenCL 2.0 CPU Only Platform' at 0xb60a20>
[0 0 1 1]
Ok

在设备 2 上

<pyopencl.Platform 'NVIDIA CUDA' at 0xff0440>
[0 0 1 1]
Ok

_{使用 python 2.7.11 [MSC v.1500 64 bit (AMD64)] 测试 - pyopencl (2015, 1)}