General-purpose computing on graphics processing units (GPGPU) has recently gained considerable attention in various domains such as bioinformatics, databases and distributed computing. GPGPU is based on using the GPU as a co-processor accelerator to offload computationally-intensive tasks from the CPU. This study starts from the observation that a number of GPU features (such as overlapping communication and computation, short lived buffer reuse, and harnessing multi-GPU systems) can be abstracted and reused across different GPGPU applications. This paper describes CrystalGPU, a modular framework that transparently enables applications to exploit a number of GPU optimizations. Our evaluation shows that CrystalGPU enables up to 16x speedup gains on synthetic benchmarks, while introducing negligible latency overhead.
@article{arxiv.1005.1695,
title = {CrystalGPU: Transparent and Efficient Utilization of GPU Power},
author = {Abdullah Gharaibeh and Samer Al-Kiswany and Matei Ripeanu},
journal= {arXiv preprint arXiv:1005.1695},
year = {2010}
}