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dc.contributor.advisorBurtscher, Martin
dc.contributor.authorAzimi Moghaddam, Sahar ( )
dc.date.accessioned2018-09-19T21:09:21Z
dc.date.available2018-09-19T21:09:21Z
dc.date.issued2017-05
dc.identifier.citationAzimi Moghaddam, S. (2017). GPU execution tracing and compression (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/7738
dc.description.abstractProgram tracing is widely used for debugging and performance optimization. Whenever a program is traced, the overhead in terms of extra runtime and in terms of storage for the generated trace information are a concern. These concerns are greatly exacerbated on GPUs due to the large amount of parallelism. In fact, GPUs provide such massive parallelism that conventional tracing approaches either fail or only manage to trace very few events per thread. Hence, we need not only a low-overhead but also a space-efficient approach to make detailed tracing possible on GPUs. To the best of my knowledge, none of the existing GPU tracing tools support both. Thus, in this thesis, I developed an execution tracing tool for GPUs called ECL-Tracer that is light-weight and immediately compresses the generated trace data before they are stored.
dc.formatText
dc.format.extent51 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.subjectGPU execution tracing
dc.subjectGPU trace compression
dc.subjectTrace compression
dc.subjectParallel programming
dc.subjectData compression
dc.titleGPU Execution Tracing and Compression
txstate.documenttypeThesis
dc.contributor.committeeMemberQasem, Apan
dc.contributor.committeeMemberZong, Ziliang
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorTexas State Universityen_US
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
txstate.departmentComputer Science


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