Centre Blaise Pascal » Bench4GPU

#!/usr/bin/env python3

import numpy as np

import pyopencl as cl

# Native Operation under Numpy (for prototyping & tests

def NativeAddition(a_np,b_np):

    return(a_np+b_np)

# OpenCL complete operation

def OpenCLAddition(a_np,b_np):

    # Context creation

    ctx = cl.create_some_context()

    # Every process is stored in a queue

    queue = cl.CommandQueue(ctx)

    # Copy from Host to Device using pointers

    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)

    # Definition of kernel under OpenCL

    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()

    # Memory allocation on Device for result

    res_g = cl.Buffer(ctx, mf.WRITE_ONLY, a_np.nbytes)

    # Synthesis of function "sum" inside Kernel Sources

    knl = prg.sum  # Use this Kernel object for repeated calls

    # Call of kernel previously defined

    knl(queue, a_np.shape, None, a_g, b_g, res_g)

    # Creation of vector for result with same size as input vectors

    res_np = np.empty_like(a_np)

    # Copy from Device to Host

    cl.enqueue_copy(queue, res_np, res_g)

    # Liberation of memory

    a_g.release()

    b_g.release()

    res_g.release()

    return(res_np)

#if __name__=='__main__':

a_np = np.random.rand(50000).astype(np.float32)

b_np = np.random.rand(50000).astype(np.float32)

res_np=NativeAddition(a_np,b_np)

res_cl=OpenCLAddition(a_np,b_np)

# Check on CPU with Numpy:

print(res_cl - res_np)

print(np.linalg.norm(res_cl - res_np))

assert np.allclose(res_np, res_cl)