#!/usr/bin/env python

"""This is the minimal example from the README"""

import numpy

from kernel_tuner import tune_kernel

from kernel_tuner.file_utils import store_output_file, store_metadata_file

def tune():

kernel_string = """

__global__ void vector_add(float *c, float *a, float *b, int n) {

int i = blockIdx.x * block_size_x + threadIdx.x;

if (i<n) {

c[i] = a[i] + b[i];

}

}

"""

size = 10000000

a = numpy.random.randn(size).astype(numpy.float32)

b = numpy.random.randn(size).astype(numpy.float32)

c = numpy.zeros_like(b)

n = numpy.int32(size)

args = [c, a, b, n]

tune_params = dict()

tune_params["block_size_x"] = [128+64*i for i in range(15)]

results, env = tune_kernel("vector_add", kernel_string, size, args, tune_params)

# Store the tuning results in an output file

store_output_file("vector_add.json", results, tune_params)

# Store the metadata of this run

store_metadata_file("vector_add-metadata.json")

return results

if __name__ == "__main__":

tune()