Methods for Optimizing OpenCL Applications on Heterogeneous Multicore Architectures

Abstract

Heterogeneous multicore architectures with CPU and add-on GPUs or streaming processors are now widely used in computer systems. These GPUs provide substantially more computation capability and memory bandwidth compared to traditional multi-cores. Also, because they are highly programmable, they provide the computational performance needed for realistic graphics rendering. Applications with general computations can also be leveraged onto these GPUs. This study discusses the architectures of these highly efficient GPUs and applies a unified programming standard called OpenCL to fully utilize their capabilities. Despite their great potential, applications of these GPUs are challenging because of their diverse underlying architectural characteristics. In this study, several optimizing techniques are applied on OpenCL-compatible heterogeneous multicore architectures to achieve thread-level and data-level parallelisms. The architectural implications of these techniques are discussed. Finally, optimization principles for these architectures will be are proposed. The experimental reveal average speedups of 24 and 430 for non-optimized and optimized kernels, respectively.