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Big Data Reading Group Slides on MS Research’s “Accelerator”

Big Data Reading Group Slides on MS Research’s “Accelerator”. Jason Campbell Sept 17, 2007. GPUs for non-graphics computing. Upside: high numbers of FLOPs now e.g., ATI Radeon x1900  250 GFLOPS. GPUs for non-graphics computing. Upside: high numbers of FLOPs now

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Big Data Reading Group Slides on MS Research’s “Accelerator”

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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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