Easy OpenCL with Python

OpenCL与python联合工作：与CUDA的前景分析

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)))

从零开始学习OpenCL开发（一）架构

在Android上使用OpenCL调用GPU加速