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Accelerator: Harnessing GPUs for High-Performance Computing

Explore how GPUs can elevate computing with high FLOPs, regular memory access, and texture memory in this comprehensive slide deck from MS Research. Discover the power and limitations of GPUs in non-graphics computing and learn what works best for maximizing performance.

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Accelerator: Harnessing GPUs for High-Performance Computing

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  1. Big Data Reading Group Slides on MS Research’s “Accelerator” Jason Campbell Sept 17, 2007

  2. GPUs for non-graphics computing • Upside: high numbers of FLOPs now e.g., ATI Radeon x1900  250 GFLOPS

  3. GPUs for non-graphics computing • Upside: high numbers of FLOPs now e.g., ATI Radeon x1900  250 GFLOPS • Downside: you can have any ops you want as long as they’re all single precision MAC and the programs are ~< 20 instructionswith no branching or (dynamic) looping MAC = multiple accumulate single precision = 8 bit exponent, 23 bit mantissa Other GPUs have less precision, down to 8 bits!

  4. Accelerator uses texture memory & pixelshaders only

  5. Input arrays“eagerly” copied, results are lazy-exec

  6. What works best • Intensely parallel operations • Limited need for precision (or integers!) • Regular memory access patterns

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