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 1, 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.
