Intel OpenCL performance: 3rd generation hardware

Brian Kloppenborg ArrayFire, OpenCL 1 Comment

Introduction With Intel CPUs making up nearly 80% of the CPU market and 66% of computers using integrated graphics one can easily argue that integrated graphics devices represent one of the greatest markets for GPU-accelerated computing. Here at ArrayFire, we have long recognized the potential of these devices and offer built-in support for Intel CPUs, GPUs, and AMD APUs in the OpenCL backend of our ArrayFire GPU computing library. Yet one common theme for debate in the office has been how the hardware performs on different operating systems with different drivers across hardware revisions. To answer these questions (and, perhaps, to win some intra-office geek cred) I decided to write a series of blog posts about Intel's GPU OpenCL performance. In this first installment I will compare the performance ...

ArrayFire Examples (Part 2 of 8) - Benchmarks

ArrayFire ArrayFire, Benchmarks, CUDA Leave a Comment

This is the second in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the benchmarks/ directory. In these examples, my machine has the following configuration:

Blas This example shows a simple bench-marking process using ArrayFire's matrix multiply routine. For more information on Blas, click here. The data measured in this example is the Giga-Flop (GFLOP Floating Point Operations Per Second). I got the following results using the example code on my machine:

Just for fun, I wrote a small program to perform a simple matrix multiply using the CPU in a triple-for loop (a pretty standard, but non-optimized solution). This ...