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Characterization and Exploitation of GPU Memory Systems

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dc.contributor.authorLee, Kenneth Sydneyen
dc.contributor.committeechairFeng, Wu-chunen
dc.contributor.committeememberCao, Yongen
dc.contributor.committeememberLin, Heshanen
dc.contributor.departmentComputer Science and Applicationsen
dc.date.accessioned2014-03-14T20:42:07Zen
dc.date.adate2012-10-25en
dc.date.available2014-03-14T20:42:07Zen
dc.date.issued2012-07-06en
dc.date.rdate2012-10-25en
dc.date.sdate2012-07-27en
dc.description.abstractGraphics Processing Units (GPUs) are workhorses of modern performance due to their ability to achieve massive speedups on parallel applications. The massive number of threads that can be run concurrently on these systems allow applications which have data-parallel computations to achieve better performance when compared to traditional CPU systems. However, the GPU is not perfect for all types of computation. The massively parallel SIMT architecture of the GPU can still be constraining in terms of achievable performance. GPU-based systems will typically only be able to achieve between 40%-60% of their peak performance. One of the major problems affecting this effeciency is the GPU memory system, which is tailored to the needs of graphics workloads instead of general-purpose computation. This thesis intends to show the importance of memory optimizations for GPU systems. In particular, this work addresses problems of data transfer and global atomic memory contention. Using the novel AMD Fusion architecture, we gain overall performance improvements over discrete GPU systems for data-intensive applications. The fused architecture systems offer an interesting trade off by increasing data transfer rates at the cost of some raw computational power. We characterize the performance of different memory paths that are possible because of the shared memory space present on the fused architecture. In addition, we provide a theoretical model which can be used to correctly predict the comparative performance of memory movement techniques for a given data-intensive application and system. In terms of global atomic memory contention, we show improvements in scalability and performance for global synchronization primitives by avoiding contentious global atomic memory accesses. In general, this work shows the importance of understanding the memory system of the GPU architecture to achieve better application performance.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-07272012-152625en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-07272012-152625/en
dc.identifier.urihttp://hdl.handle.net/10919/34215en
dc.publisherVirginia Techen
dc.relation.haspartLee_KS_T_2012.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectData Transferen
dc.subjectPerformance Modelingen
dc.subjectGPGPUen
dc.subjectAPUen
dc.subjectGPUen
dc.subjectMemory Systemsen
dc.titleCharacterization and Exploitation of GPU Memory Systemsen
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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