Architecture Aware Programming on Multi-Core Systems

In order to improve the processor performance, the response of the industry has been to increase the number of cores on the die. One salient feature of multi-core architectures is that they have a varying degree of sharing of caches at different levels. With the advent of multi-core architectures, we are facing the problem that is new to parallel computing, namely, the management of hierarchical caches. Data locality features need to be considered in order to reduce the variance in the performance for different data sizes. In this paper, we propose a programming approach for the algorithms running on shared memory multi-core systems by using blocking, which is a well-known optimization technique coupled with parallel programming paradigm, OpenMP. We have chosen the sizes of various problems based on the architectural parameters of the system like cache level, cache size, cache line size. We studied the cache optimization scheme on commonly used linear algebra applications - matrix multiplication (MM), Gauss-Elimination (GE) and LU Decomposition (LUD) algorithm.