ELMO: A User-Friendly API to Enable Local Memory in OpenCL Kernels

Abstract

Recent parallel architectures are equipped with local memory, which simplifies hardware design at the cost of increased program complexity due to explicit management. To simplify this extra-burden that programmers have, we introduce an easy-to-use API, ELMO, that improves productivity while preserving high performance of local memory operations. Specifically, ELMO is a generic API that covers different local memory use-cases. We also present prototype implementations for these APIs and perform multiple GPU-inspired optimizations to maximize their performance. Experimental results on the NVIDIA Quadro5000 GPU show that performance is significantly improved by using ELMO on native implementations: the achieved speedup ranges from 1.3x to 3.7x. Furthermore, using ELMO we still achieve performance comparable (if not better) with that of hand-tuned applications, while the code is shorter, clearer, and safer.