IterML: Iterative Machine Learning for Intelligent Parameter Pruning and Tuning in Graphics Processing Units

dc.contributor.authorCui, Xuewenen
dc.contributor.authorFeng, Wu-chunen
dc.date.accessioned2024-03-04T15:11:06Zen
dc.date.available2024-03-04T15:11:06Zen
dc.date.issued2020-11-06en
dc.description.abstractWith the rise of graphics processing units (GPUs), the parallel computing community needs better tools to productively extract performance from the GPU. While modern compilers provide flags to activate different optimizations to improve performance, the effectiveness of such automated optimization has been limited at best. As a consequence, extracting the best performance from an algorithm on a GPU requires significant expertise and manual effort to exploit both spatial and temporal sharing of computing resources. In particular, maximizing the performance of an algorithm on a GPU requires extensive hyperparameter (e.g., thread-block size) selection and tuning. Given the myriad of hyperparameter dimensions to optimize across, the search space of optimizations is extremely large, making it infeasible to exhaustively evaluate. This paper proposes an approach that uses statistical analysis with iterative machine learning (IterML) to prune and tune hyperparameters to achieve better performance. During each iteration, we leverage machine-learning models to guide the pruning and tuning for subsequent iterations. We evaluate our IterML approach on the GPU thread-block size across many benchmarks running on an NVIDIA P100 or V100 GPU. Our experimental results show that our automated IterML approach reduces search effort by 40% to 80% when compared to traditional (non-iterative) ML and that the performance of our (unmodified) GPU applications can improve significantly — between 67% and 95% — simply by changing the thread-block size.en
dc.description.versionAccepted versionen
dc.format.extentPages 391-403en
dc.identifier.doihttps://doi.org/10.1007/s11265-020-01604-4en
dc.identifier.eissn1939-8115en
dc.identifier.issn1939-8018en
dc.identifier.issue4en
dc.identifier.orcidFeng, Wu-chun [0000-0002-6015-0727]en
dc.identifier.urihttps://hdl.handle.net/10919/118248en
dc.identifier.volume93en
dc.language.isoenen
dc.publisherSpringeren
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleIterML: Iterative Machine Learning for Intelligent Parameter Pruning and Tuning in Graphics Processing Unitsen
dc.title.serialJournal of Signal Processing Systemsen
dc.typeArticle - Refereeden
dc.type.otherJournal Articleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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