NumPy数组到vecs的PyOpenCL数组

Question

我有一个NumPy数组，其中包含数组：

import numpy as np
import pyopencl as cl
someArray = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])

现在，我想将此数组转换为vec4s的OpenCL数组，以便对其进行处理。 例如：

context = cl.create_some_context()
queue = cl.CommandQueue()
program = cl.Program("""
    __kernel void multiplyByTwo(__global const float32* someArrayAsOpenCLType, __global float32* result) {
        gid = get_global_id(0);
        vector = someArrayAsOpenCLType[gid];
        result[gid] = vector * 2;
    }
""").build()

someArrayAsOpenCLType = # something with someArray
result = # some other thing
program.multiplyByTwo(queue, someArray.shape, None, someArrayAsOpenCLType, result)

如何将someArray转换为someArrayAsOpenCLType？

Answer 1

someArray的数据存储在主机的内存中，并且这些数据必须复制到设备的缓冲存储器（ someArrayAsOpenCLType ）中。

内核在设备上执行，并将结果存储在设备缓冲区（预分配： resultAsOpenCLType ）中。

执行之后，程序可以将结果从设备的缓冲区中返回到主机内存中（例如： cl.enqueue_copy(queue, result, resultAsOpenCLType) ）。

遵循一个简单的示例（但也许还有其他方法可以做到这一点）：

import numpy as np
import pyopencl as cl

# Context
ctx = cl.create_some_context()
# Create queue
queue = cl.CommandQueue(ctx)

someArray = np.array([
    [1, 2, 3, 4],
    [5, 6, 7, 8]
]).astype(np.float32)

print ""
print("Input:")
print(someArray)
print("------------------------------------")

# Get mem flags
mf = cl.mem_flags

# Create a read-only buffer on device and copy 'someArray' from host to device
someArrayAsOpenCLType = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=someArray)

# Create a write-only buffer to get the result from device
resultAsOpenCLType = cl.Buffer(ctx, mf.WRITE_ONLY, someArray.nbytes)

# Creates a kernel in context
program = cl.Program(ctx, """
__kernel void multiplyByTwo(__global const float4 *someArrayAsOpenCLType, __global float4 *resultAsOpenCLType) {
        int gid = get_global_id(0);

        float4 vector = someArrayAsOpenCLType[gid];
        resultAsOpenCLType[gid] =  vector * (float) 2.0;
}
""").build()

# Execute
program.multiplyByTwo(queue, someArray.shape, None, someArrayAsOpenCLType, resultAsOpenCLType)

# Creates a buffer for the result (host memory)
result = np.empty_like(someArray)

# Copy the results from device to host
cl.enqueue_copy(queue, result, resultAsOpenCLType)

print("------------------------------------")
print("Output")
# Show the result
print (result)

执行后（带有选项0 ）：

Choose platform:
[0] <pyopencl.Platform 'Intel(R) OpenCL' at 0x858ea0>
[1] <pyopencl.Platform 'Experimental OpenCL 2.0 CPU Only Platform' at 0x872880>
[2] <pyopencl.Platform 'NVIDIA CUDA' at 0x894a80>
Choice [0]:
Set the environment variable PYOPENCL_CTX='' to avoid being asked again.

Input:
[[ 1.  2.  3.  4.]
 [ 5.  6.  7.  8.]]
------------------------------------
C:\Python27\lib\site-packages\pyopencl\__init__.py:59: CompilerWarning: Built kernel retrieved from cache. Original from-sour
ce build had warnings:
Build on <pyopencl.Device 'Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz' on 'Intel(R) OpenCL' at 0x86ca30> succeeded, but said:

Compilation started
Compilation done
Linking started
Linking done
Device build started
Device build done
Kernel <multiplyByTwo> was not vectorized
Done.
  warn(text, CompilerWarning)
C:\Python27\lib\site-packages\pyopencl\__init__.py:59: CompilerWarning: From-binary build succeeded, but resulted in non-empt
y logs:
Build on <pyopencl.Device 'Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz' on 'Intel(R) OpenCL' at 0x86ca30> succeeded, but said:

Device build started
Device build done
Reload Program Binary Object.
  warn(text, CompilerWarning)
------------------------------------
Output
[[  2.   4.   6.   8.]
 [ 10.  12.  14.  16.]]

英特尔网站上有关OpenCL的一些教程：

英特尔-OpenCL™教程