Paper Info
Title
Exploring Graphics Processing Unit (Gpu) Resource Sharing Efficiency For High Performance Computing
Abstract
The increasing incorporation of Graphics Processing Units (GPUs) as accelerators has been one of the forefront High Performance Computing (HPC) trends and provides unprecedented performance; however, the prevalent adoption of the Single-Program Multiple-Data (SPMD) programming model brings with it challenges of resource underutilization. In other words, under SPMD, every CPU needs GPU capability available to it. However, since CPUs generally outnumber GPUs, the asymmetric resource distribution gives rise to overall computing resource underutilization. In this paper, we propose to efficiently share the GPU under SPMD and formally define a series of GPU sharing scenarios. We provide performance-modeling analysis for each sharing scenario with accurate experimentation validation. With the modeling basis, we further conduct experimental studies to explore potential GPU sharing efficiency improvements from multiple perspectives. Both further theoretical and experimental GPU sharing performance analysis and results are presented. Our results not only demonstrate the significant performance gain for SPMD programs with the proposed efficient GPU sharing, but also the further improved sharing efficiency with the optimization techniques based on our accurate modeling.
Year
DOI
Venue
2013
10.3390/computers2040176
COMPUTERS
Keywords
Field
DocType
GPU, resource sharing, SPMD, performance modeling, high performance computing
Graphics,SPMD,Computer architecture,Supercomputer,Programming paradigm,Resource distribution,CUDA,Computer science,Parallel computing,Graphics processing unit,Shared resource
Journal
Volume
Issue
ISSN
2
4
2073-431X
Citations 
PageRank 
References 
5
0.73
11
Authors
3
Name
Order
Citations
PageRank
Teng Li1535.40
Vikram K. Narayana210213.18
tarek elghazawi369784.30