General‐purpose computation on GPUs for high performance cloud computing. (15th May 2012)

• Record Type: Journal Article
• Authors: Expósito, Roberto R.; Taboada, Guillermo L.; Ramos, Sabela; Touriño, Juan; Doallo, Ramón
• Other Names: Vaquero Luis Miguel guestEditor.; Rodero‐Merino Luis guestEditor.; Buyya Rajkumar guestEditor.; Kolodziej Joanna guestEditor.; Khan Samee Ullah guestEditor.; Gelenbe Erol guestEditor.; Talbi El‐Ghazali guestEditor.
• Is Part Of: Concurrency and computation. Volume 25:Number 12(2013:Aug.)
• Page Start: 1628
• Page End: 1642
• Publication Date: 2012-05-15
• Subjects: Cloud Computing -- General‐Purporse computation on GPU (GPGPU) -- High Performance Computing (HPC) -- 10 Gigabit Ethernet -- CUDA -- OpenCL -- MPI; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.2845
• Languages: English
• ISSNs: 1532-0626

Abstract

SUMMARY: Cloud computing is offering new approaches for High Performance Computing (HPC) as it provides dynamically scalable resources as a service over the Internet. In addition, General‐Purpose computation on Graphical Processing Units (GPGPU) has gained much attention from scientific computing in multiple domains, thus becoming an important programming model in HPC. Compute Unified Device Architecture (CUDA) has been established as a popular programming model for GPGPUs, removing the need for using the graphics APIs for computing applications. Open Computing Language (OpenCL) is an emerging alternative not only for GPGPU but also for any parallel architecture. GPU clusters, usually programmed with a hybrid parallel paradigm mixing Message Passing Interface (MPI) with CUDA/OpenCL, are currently gaining high popularity. Therefore, cloud providers are deploying clusters with multiple GPUs per node and high‐speed network interconnects in order to make them a feasible option for HPC as a Service (HPCaaS). This paper evaluates GPGPU for high performance cloud computing on a public cloud computing infrastructure, Amazon EC2 Cluster GPU Instances (CGI), equipped with NVIDIA Tesla GPUs and a 10 Gigabit Ethernet network. The analysis of the results, obtained using up to 64 GPUs and 256‐processor cores, has shown that GPGPU is a viable option for high performance cloud computing despite the significant impact that virtualized environments still have on network overhead, which still hampers the adoption of GPGPU communication‐intensive applications. Copyright © 2012 John Wiley & Sons, Ltd.