This class reading

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This class reading

  • This class reading

    • “Program optimization space pruning for a multithreaded GPU,” S. Ryoo, C. Rodrigues, S. Stone, S. Baghsorkhi, S. Ueng, J. Straton, and W. Hwu, Proc. Intl. Sym. on Code Generation and Optimization, Mar. 2008.
  • Project demos

    • Dec 13-16, 19 (19th is full)
    • Send me email with date and a few timeslots if your group does not have a slot
    • Almost all have signed up already

Demo format

  • Demo format

    • Each group gets 30 mins
    • Plan for 20 mins of presentation (no more!), 10 mins questions
      • Some slides are helpful, try to have all group members say something
      • Talk about what you did (basic idea, previous work), how you did it (approach + implementation), and results
      • Demo or real code examples are good
  • Report

“Compute Unified Device Architecture”

  • “Compute Unified Device Architecture”

  • General purpose programming model

    • User kicks off batches of threads on the GPU
  • Advantages of CUDA

Who has written CUDA, how have you optimized it, how long did it take?

  • Who has written CUDA, how have you optimized it, how long did it take?

    • Did you do tune using a better algorithm than trial and error?
  • Is there any hope to build a GPU compiler that can automatically do what CUDA programmers do?

    • How would you do it?
    • What’s the input language? C, C++, Java, StreamIt?
  • Are GPUs a compiler writers best friend or worst enemy?

  • What about non-scientific codes, can they be mapped to GPUs?

    • How can GPUs be made more “general”?

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