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Statistical Regression Methods For Gpgpu Design Space Exploration, Nimisha Raut

All Theses

General Purpose Graphics Processing Units (GPGPUs) have leveraged the performance and power efficiency of today's heterogeneous systems to usher in a new era of innovation in high-performance scientific computing. These systems can offer significantly high performance for massively parallel applications; however, their resources may be wasted due to inefficient tuning strategies. Previous application tuning studies pre-dominantly employ low-level, architecture specific tuning which can make the performance modeling task difficult and less generic. In this research, we explore the GPGPU design space featuring the memory hierarchy for application tuning using regression-based performance prediction framework and rank the design space based on …