#!/usr/bin/env python

import numpy

import kernel_tuner

from collections import OrderedDict

with open('convolution.cl', 'r') as f:

kernel_string = f.read()

problem_size = (4096, 4096)

size = numpy.prod(problem_size)

input_size = (problem_size[0]+16) * (problem_size[0]+16)

output = numpy.zeros(size).astype(numpy.float32)

input = numpy.random.randn(input_size).astype(numpy.float32)

filter = numpy.random.randn(17*17).astype(numpy.float32)

args = [output, input, filter]

tune_params = OrderedDict()

tune_params["block_size_x"] = [16*i for i in range(1,9)]

tune_params["block_size_y"] = [2**i for i in range(6)]

tune_params["tile_size_x"] = [2**i for i in range(3)]

tune_params["tile_size_y"] = [2**i for i in range(3)]

grid_div_x = ["block_size_x", "tile_size_x"]

grid_div_y = ["block_size_y", "tile_size_y"]

kernel_tuner.tune_kernel("convolution_kernel", kernel_string,

problem_size, args, tune_params,

grid_div_y=grid_div_y, grid_div_x=grid_div_x, verbose=True)