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To-Do List

  • (Everyone) Understand gpaw (read paper)
    • what other steps could we parallelize?
    • Can we do existing parallelization better? (e.g. use ideas in Todd's GPU papers)
  • (Everyone) Go through CUDA tutorial here.
    • Understand blocks/threads/warps and how they map onto GPU hardware (details of which can be seen with "deviceQuery" command)
  • (Lin) Find tool to measure:
    • memory bandwidth usage
    • gpu flops usage
  • (Jun) :
    • Parallelize LCAO/planewave/RPA?
    • merge with trunk?
  • (cpo) :
    • Understand code flow
  • Understand pycuda
  • Understand gpaw interface to cuda (c/cuda subdirectory)
  • Read CUDA programming manual here.
  • Do all gpaw self-tests pass with GPUs?
  • Can we get bigger bang-per-buck with GeForce instead of Tesla? (don’t need GPUDirect, maybe live with less memory/bandwidth?)
  • Understand cuda better
    • Does Samuli use pinned memory correctly?
    • run/understand cuda a bandwidth benchmark
    • Could we use GPUdirect for MPI data transfer?
  • Duplicate Samuli results
  • Update to most recent version in svn
  • Do another gpu-gpaw install (to learn)
  • Understand where gpaw scaling maxes out for Pt 3x2x3
  • Understand Pt 3x4x3 CPU/GPU difference
  • Why is CO on 2 GPUs slower than on 8 CPUs?
  • Does the GPU performance scale with the product of gridpoints*bands? Might be a combinatorial effect with the bands, linear with the gridpoints?
  • Can we do something less precise in the vacuum area? (fewer grid points?)
  • Do we need a fatter interconnect for GPUs?
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