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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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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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 |
_version_ |
1718803494421397504 |