THROUGHPUT OPTIMIZATION AND RESOURCE ALLOCATION ON GPUS UNDER MULTI-APPLICATION EXECUTION

Platform heterogeneity prevails as a solution to the throughput and computational chal- lenges imposed by parallel applications and technology scaling. Specifically, Graphics Processing Units (GPUs) are based on the Single Instruction Multiple Thread (SIMT) paradigm and they can offer tremendous spe...

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Main Author: PUNYALA, SRINIVASA REDDY
Format: Others
Published: OpenSIUC 2017
Online Access:https://opensiuc.lib.siu.edu/theses/2255
https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=3270&context=theses
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spelling ndltd-siu.edu-oai-opensiuc.lib.siu.edu-theses-32702018-12-20T04:42:55Z THROUGHPUT OPTIMIZATION AND RESOURCE ALLOCATION ON GPUS UNDER MULTI-APPLICATION EXECUTION PUNYALA, SRINIVASA REDDY Platform heterogeneity prevails as a solution to the throughput and computational chal- lenges imposed by parallel applications and technology scaling. Specifically, Graphics Processing Units (GPUs) are based on the Single Instruction Multiple Thread (SIMT) paradigm and they can offer tremendous speed-up for parallel applications. However, GPUs were designed to execute a single application at a time. In case of simultaneous multi-application execution, due to the GPUs’ massive multi-threading paradigm, ap- plications compete against each other using destructively the shared resources (caches and memory controllers) resulting in significant throughput degradation. In this thesis, a methodology for minimizing interference in shared resources and provide efficient con- current execution of multiple applications on GPUs is presented. Particularly, the pro- posed methodology (i) performs application classification; (ii) analyzes the per-class in- terference; (iii) finds the best matching between classes; and (iv) employs an efficient re- source allocation. Experimental results showed that the proposed approach increases the throughput of the system for two concurrent applications by an average of 36% compared to other optimization techniques, while for three concurrent applications the proposed approach achieved an average gain of 23%. 2017-12-01T08:00:00Z text application/pdf https://opensiuc.lib.siu.edu/theses/2255 https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=3270&context=theses Theses OpenSIUC
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description Platform heterogeneity prevails as a solution to the throughput and computational chal- lenges imposed by parallel applications and technology scaling. Specifically, Graphics Processing Units (GPUs) are based on the Single Instruction Multiple Thread (SIMT) paradigm and they can offer tremendous speed-up for parallel applications. However, GPUs were designed to execute a single application at a time. In case of simultaneous multi-application execution, due to the GPUs’ massive multi-threading paradigm, ap- plications compete against each other using destructively the shared resources (caches and memory controllers) resulting in significant throughput degradation. In this thesis, a methodology for minimizing interference in shared resources and provide efficient con- current execution of multiple applications on GPUs is presented. Particularly, the pro- posed methodology (i) performs application classification; (ii) analyzes the per-class in- terference; (iii) finds the best matching between classes; and (iv) employs an efficient re- source allocation. Experimental results showed that the proposed approach increases the throughput of the system for two concurrent applications by an average of 36% compared to other optimization techniques, while for three concurrent applications the proposed approach achieved an average gain of 23%.
author PUNYALA, SRINIVASA REDDY
spellingShingle PUNYALA, SRINIVASA REDDY
THROUGHPUT OPTIMIZATION AND RESOURCE ALLOCATION ON GPUS UNDER MULTI-APPLICATION EXECUTION
author_facet PUNYALA, SRINIVASA REDDY
author_sort PUNYALA, SRINIVASA REDDY
title THROUGHPUT OPTIMIZATION AND RESOURCE ALLOCATION ON GPUS UNDER MULTI-APPLICATION EXECUTION
title_short THROUGHPUT OPTIMIZATION AND RESOURCE ALLOCATION ON GPUS UNDER MULTI-APPLICATION EXECUTION
title_full THROUGHPUT OPTIMIZATION AND RESOURCE ALLOCATION ON GPUS UNDER MULTI-APPLICATION EXECUTION
title_fullStr THROUGHPUT OPTIMIZATION AND RESOURCE ALLOCATION ON GPUS UNDER MULTI-APPLICATION EXECUTION
title_full_unstemmed THROUGHPUT OPTIMIZATION AND RESOURCE ALLOCATION ON GPUS UNDER MULTI-APPLICATION EXECUTION
title_sort throughput optimization and resource allocation on gpus under multi-application execution
publisher OpenSIUC
publishDate 2017
url https://opensiuc.lib.siu.edu/theses/2255
https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=3270&context=theses
work_keys_str_mv AT punyalasrinivasareddy throughputoptimizationandresourceallocationongpusundermultiapplicationexecution
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