Question

我正在尝试使用pyopencl在Python中实现dot_persist_kernel（）显示here，并且我一直在挤压许多错误。但是，我偶然发现了一个我无法解决的问题：

self.program = cl.Program(self.ctx, code).build()
# code is a string with the code from the link given

a = cl_array.to_device(self.queue, np.random.rand(2**20).astype(np.float32))
b = cl_array.to_device(self.queue, np.random.rand(2**20).astype(np.float32))
c = 0.

mf = cl.mem_flags
c_buf = cl.Buffer(self.ctx, mf.WRITE_ONLY, 4)

MAX_COMPUTE_UNITS = cl.get_platforms()[0].get_devices()[0].max_compute_units
WORK_GROUPS_PER_CU = MAX_COMPUTE_UNITS * 4
ELEMENTS_PER_GROUP = a.size / WORK_GROUPS_PER_CU
ELEMENTS_PER_WORK_ITEM = ELEMENTS_PER_GROUP / 256

self.program.DotProduct(self.queue, a.shape, a.shape,
    a.data, b.data, c_buf, 
    np.uint32(ELEMENTS_PER_GROUP), 
    np.uint32(ELEMENTS_PER_WORK_ITEM), 
    np.uint32(1028 * MAX_COMPUTE_UNITS))

假设一个大小为2 ^ 26的数组，常量的值为：

MAX_COMPUTE_UNITS = 32 // from get_device()[0].max_compute_units
WORK_GROUPS_PER_CU = 128 // MAX_COMPUTE_UNITS * 4
ELEMENTS_PER_GROUP = 524288 // 2^19
ELEMENTS_PER_WORK_ITEM = 2048 // 2^11

内核标题如下：

#define LOCAL_GROUP_XDIM 256
// Kernel for part 1 of dot product, version 3.
__kernel __attribute__((reqd_work_group_size(LOCAL_GROUP_XDIM, 1, 1)))
void dot_persist_kernel(
    __global const double * x, // input vector
    __global const double * y, // input vector
    __global double * r, // result vector
    uint n_per_group, // elements processed per group
    uint n_per_work_item, // elements processed per work item
    uint n // input vector size
)

它给出的错误是：

Traceback (most recent call last):
  File "GPUCompute.py", line 102, in <module>
    gpu = GPUCompute()
  File "GPUCompute.py", line 87, in __init__
    np.uint32(1028 * MAX_COMPUTE_UNITS))
  File "C:\Miniconda2\lib\site-packages\pyopencl\__init__.py", line 512, in kernel_call
    global_offset, wait_for, g_times_l=g_times_l)
pyopencl.LogicError: clEnqueueNDRangeKernel failed: invalid work item size

我尝试过多次改变数字，但无济于事。想法？

Answer 1

之前的实施有一些问题，但是这个问题正在发挥作用：

WORK_GROUPS = cl.get_platforms()[0].get_devices()[0].max_compute_units * 4
ELEMENTS_PER_GROUP = np_a.size / WORK_GROUPS
LOCAL_GROUP_XDIM = 256
ELEMENTS_PER_WORK_ITEM = ELEMENTS_PER_GROUP / LOCAL_GROUP_XDIM

self.program = cl.Program(self.ctx, kernel).build()
self.program.DotProduct(
    self.queue, np_a.shape, (LOCAL_GROUP_XDIM,),    # kernel information
    cl_a, cl_b, cl_c,                   # data
    np.uint32(ELEMENTS_PER_GROUP),      # elements processed per group
    np.uint32(ELEMENTS_PER_WORK_ITEM),  # elements processed per work item
    np.uint32(np_a.size)                # input vector size
)

这是一些事情的高潮，但最大的因素是传递给DotProduct（）的第二个和第三个参数应该是元组 - 而不是像我想的那样整数。：）

pyopencl.LogicError：clEnqueueNDRangeKernel失败：工作项大小无效

1 个答案: