Show simple item record

dc.contributor.advisorQasem, Apan
dc.contributor.authorConnors, Tiffany A. ( )
dc.date.accessioned2017-07-13T12:49:48Z
dc.date.available2017-07-13T12:49:48Z
dc.date.issued2017-05
dc.identifier.citationConnors, T. A. (2017). Automatically selecting profitable thread block sizes using machine learning (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/6731
dc.descriptionPresented to the Honors Committee of Texas State University in Partial Fulfillment of the Requirements for Graduation in the University Honors Program, May 2017.
dc.description.abstractGraphics processing units (GPUs) provide high performance at low power consumption as long as resources are well utilized. Thread block size is one factor in determining a kernel's occupancy, which is a metric for measuring GPU utilization. A general guideline is to find the block size that leads to the highest occupancy. However, many combinations of block and grid sizes can provide highest occupancy, but performance can vary significantly between different configurations. This is because variation in thread structure yields different utilization of hardware resources. Thus, optimizing for occupancy alone is insufficient and thread structure must also be considered. It is the programmer's responsibility to set block size, but selecting the right size is not always intuitive. In this paper, we propose using machine learning to automatically select profitable block sizes. Additionally, we show that machine learning techniques coupled with performance counters can provide insight into the underlying reasons for performance variance between different configurations.en_US
dc.formatText
dc.format.extent39 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.subjectMachine learningen_US
dc.subjectOptimizationen_US
dc.subjectPerformance tuningen_US
dc.subjectAuto-tuningen_US
dc.subjectGPUen_US
dc.subjectPerformance heuristicsen_US
dc.subjectSupervised machine learningen_US
dc.titleAutomatically Selecting Profitable Thread Block Sizes Using Machine Learningen_US
txstate.documenttypeThesis
thesis.degree.departmentHonors College
thesis.degree.disciplineComputer Science
thesis.degree.grantorTexas State University
txstate.departmentHonors College


Download

Thumbnail

This item appears in the following Collection(s)

Show simple item record