Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance Computing
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 c...
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doaj-40cb02604edf4adfb6e7b97caf4395182020-11-24T22:51:58ZengMDPI AGComputers2073-431X2013-11-012417621410.3390/computers2040176computers2040176Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance ComputingTeng Li0Vikram K. Narayana1Tarek El-Ghazawi2NSF Center for High-Performance Reconfigurable Computing (CHREC), Department of Electrical and Computer Engineering, The George Washington University, 801 22nd Street NW, Washington, DC, 20052, USANSF Center for High-Performance Reconfigurable Computing (CHREC), Department of Electrical and Computer Engineering, The George Washington University, 801 22nd Street NW, Washington, DC, 20052, USANSF Center for High-Performance Reconfigurable Computing (CHREC), Department of Electrical and Computer Engineering, The George Washington University, 801 22nd Street NW, Washington, DC, 20052, USAThe 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.http://www.mdpi.com/2073-431X/2/4/176GPUresource sharingSPMDperformance modelinghigh performance computing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Teng Li Vikram K. Narayana Tarek El-Ghazawi |
spellingShingle |
Teng Li Vikram K. Narayana Tarek El-Ghazawi Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance Computing Computers GPU resource sharing SPMD performance modeling high performance computing |
author_facet |
Teng Li Vikram K. Narayana Tarek El-Ghazawi |
author_sort |
Teng Li |
title |
Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance Computing |
title_short |
Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance Computing |
title_full |
Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance Computing |
title_fullStr |
Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance Computing |
title_full_unstemmed |
Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance Computing |
title_sort |
exploring graphics processing unit (gpu) resource sharing efficiency for high performance computing |
publisher |
MDPI AG |
series |
Computers |
issn |
2073-431X |
publishDate |
2013-11-01 |
description |
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. |
topic |
GPU resource sharing SPMD performance modeling high performance computing |
url |
http://www.mdpi.com/2073-431X/2/4/176 |
work_keys_str_mv |
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