Intel Core 2 Extreme Quad Core Computer Processor Multithreaded Matrix Multiplication Performance: Dual-core and Quad-core processors are fast becoming the staple in desktop and mobile computing. In this article, performance data comparing single-threaded and multi-threaded matrix-vector multiplication are presented. In addition to multi-threading the routine, it was also optimized using Single Instruction Multiple Data (SIMD) instructions. The different implementations were tested on an Intel Core 2 Extreme quad-core processor, QX6700.

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by Kiefer Kuah

Dual-core and Quad-core processors are fast becoming the staple in desktop and mobile computing. In this article, performance data comparing single-threaded and multi-threaded matrix-vector multiplication are presented. In addition to multi-threading the routine, it was also optimized using Single Instruction Multiple Data (SIMD) instructions. The different implementations were tested on an Intel® Core™ 2 Extreme quad-core processor, QX6700. Measurable speedups were observed; the highest speedup was 20.8x. Next, the impact of increasing the data set to sizes beyond the last level cache was also characterized.

Introduction

The multiplication of a matrix and a vector is a common operation in applications such as in the skinning and physics code of 3D graphics games. We investigated a few ways to write the code for this operation and assess the performance of each version on a 2.66GHz Intel® Core™ 2 Extreme quad-core processors.

Six different versions of the code were written. The first version was written using C++ code. It involved two nested loops, iterating through each element of the data sets. This version would be used as the reference by which the performance of other versions would be measured. This version was then multithreaded using Windows* threading functions.

The SIMD versions were written in assembly and they operated on 4 floating point data in each loop. One version assumed that the data was in the array of structure (AOS) format while the other assumed the data was in the structure of array (SOA) format. Figure 2 shows an example of the structure of array construct. Two other versions were subsequently derived from these SIMD versions by converting them to multi-threaded code.