Easy OpenCL with Python
OpenCL与python联合工作:与CUDA的前景分析
http://www.opengpu.org/forum.php?mod=viewthread&tid=16571
如果你对python熟,可以用 PyOpenCL, 兼顾 host 端的简洁与 device 端的高效。 kernel 函数可以写在单独的 *.cl 文件里, 一句 python 命令就可以 load + build: prg_src = open( 'kernel_test1.cl', 'r').read() prg = cl.Program(ctx, prg_src).build() #!/usr/bin/env python import numpy as np import pyopencl as cl a_np = np.random.rand(50000).astype(np.float32) b_np = np.random.rand(50000).astype(np.float32) ctx = cl.create_some_context() queue = cl.CommandQueue(ctx) mf = cl.mem_flags a_g = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=a_np) b_g = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=b_np) prg = cl.Program(ctx, """ __kernel void sum(__global const float *a_g, __global const float *b_g, __global float *res_g) { int gid = get_global_id(0); res_g[gid] = a_g[gid] + b_g[gid]; } """).build() res_g = cl.Buffer(ctx, mf.WRITE_ONLY, a_np.nbytes) prg.sum(queue, a_np.shape, None, a_g, b_g, res_g) res_np = np.empty_like(a_np) cl.enqueue_copy(queue, res_np, res_g) # Check on CPU with Numpy: print(res_np - (a_np + b_np)) print(np.linalg.norm(res_np - (a_np + b_np)))
GPGPU OpenCL/CUDA 高性能编程的10大注意事项
http://www.cnblogs.com/xudong-bupt/p/3630952.html
从零开始学习OpenCL开发(一)架构
http://blog.csdn.net/leonwei/article/details/8880012
在Android上使用OpenCL调用GPU加速