# PyOpenCL Parallel Patterns: Map/Elementwise¶

## Setup code¶

In [1]:

    import pyopencl as cl
    import pyopencl.array
    import pyopencl.clrandom
    import numpy as np

In [2]:

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

In [3]:

    n = 10**7
    a = cl.clrandom.rand(queue, n, np.float32)
    b = cl.clrandom.rand(queue, n, np.float32)

## A simple 'target application'¶

We would like to evaluate this linear combination:

In [4]:

    c1 = 5*a + 6*b

A problem with this is that every single operator (all three of them--and easily more for complicated expressions) corresponds to a kernel call, which can lead to high overhead. Let's try and avoid that by stuffing the entire operation into one kernel, in turn saving lots of memory traffic:

In [5]:

    from pyopencl.elementwise import ElementwiseKernel

In [6]:

    lin_comb = ElementwiseKernel(ctx,

            "float a, float *x, float b, float *y, float *c",

            "c[i] = a*x[i] + b*y[i]")

In [7]:

    c2 = cl.array.empty_like(a)
    lin_comb(5, a, 6, b, c2)

Out[7]:

    <pyopencl.cffi_cl.Event at 0x7f859c0d0fd0>

In [8]:

    import numpy.linalg as la
    print(la.norm(c1.get() - c2.get()))

    0.0

## Timing ElementwiseKernel¶

Did this optimization pay off?

In [11]:

    from time import time
    queue.finish()
    start_time = time()

    for i in range(10):
        c1 = 5*a + 6*b

    queue.finish()
    print("elapsed: {0} s".format(time()-start_time))

    elapsed: 5.775973320007324 s

In [12]:

    from time import time
    queue.finish()
    start_time = time()

    for i in range(10):
        lin_comb(5, a, 6, b, c2)

    queue.finish()
    print("elapsed: {0} s".format(time()-start_time))

    elapsed: 2.476457357406616 s

In [ ]: