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dc.contributor.authorAl-Mouhamed, Mayez
dc.contributor.authorKhan, Ayaz ul Hassan
dc.date.accessioned2016-02-25T13:18:06Z
dc.date.available2016-02-25T13:18:06Z
dc.date.issued2012-12
dc.identifier.citationAl-Mouhamed M, Khan A ul H (2012) Exploration of automatic optimization for CUDA programming. 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing. Available: http://dx.doi.org/10.1109/PDGC.2012.6449791.
dc.identifier.doi10.1109/PDGC.2012.6449791
dc.identifier.urihttp://hdl.handle.net/10754/598291
dc.description.abstractGraphic processing Units (GPUs) are gaining ground in high-performance computing. CUDA (an extension to C) is most widely used parallel programming framework for general purpose GPU computations. However, the task of writing optimized CUDA program is complex even for experts. We present a method for restructuring loops into an optimized CUDA kernels based on a 3-step algorithm which are loop tiling, coalesced memory access, and resource optimization. We also establish the relationships between the influencing parameters and propose a method for finding possible tiling solutions with coalesced memory access that best meets the identified constraints. We also present a simplified algorithm for restructuring loops and rewrite them as an efficient CUDA Kernel. The execution model of synthesized kernel consists of uniformly distributing the kernel threads to keep all cores busy while transferring a tailored data locality which is accessed using coalesced pattern to amortize the long latency of the secondary memory. In the evaluation, we implement some simple applications using the proposed restructuring strategy and evaluate the performance in terms of execution time and GPU throughput. © 2012 IEEE.
dc.description.sponsorshipThanks to the ICS-KFUPM and KAUST for givingaccess to their GPU computers and workstations.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectCompiler Transformations
dc.subjectCUDA
dc.subjectdirective-based language
dc.subjectGPGPU
dc.subjectGPU
dc.subjectParallel Programming
dc.subjectsource-to-source compiler
dc.titleExploration of automatic optimization for CUDA programming
dc.typeConference Paper
dc.identifier.journal2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing
dc.contributor.institutionKing Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia


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