How to parallelize this task using all GPU processors?

My Intel GPU has 40 shader units, 64bit width, and support opencl 1.2, . For example I need to calculate something like:

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

I need to split this task between 40 units evenly. How to do it? Also I need suggestions how to optimize some code for specific number of internal GPU processors. Full code of example:

from __future__ import absolute_import
from __future__ import print_function
import pyopencl as cl
import pyopencl.array as cl_array
import numpy
import numpy.linalg as la

a = numpy.random.rand(50000).astype(numpy.float32)
b = numpy.random.rand(50000).astype(numpy.float32)

ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)

a_dev = cl_array.to_device(queue, a)
b_dev = cl_array.to_device(queue, b)
dest_dev = cl_array.empty_like(a_dev)

prg = cl.Program(ctx, """
    __kernel void sum(__global const float *a,
    __global const float *b, __global float *c)
    {
      int gid = get_global_id(0);
      c[gid] = a[gid] + b[gid];
    }
    """).build()

prg.sum(queue, a.shape, None, a_dev.data, b_dev.data, dest_dev.data)

print(la.norm((dest_dev - (a_dev+b_dev)).get()))