Page History

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• 12GB/s from pyFAI?  https://www.silx.org/doc/pyFAI/latest/usage/tutorial/Parallelization/MultiGPU.html (see conclusion)
• Stefano getting worrisome numbers for standalone angular integration (as low as 3GB/s but up to 50GB/s if we batch)
  • will run the profiler
  • pyFAI is not yet running on GPU: will work on that
• Gabriel moved the GPU nodes to the FEE alcove xpm10. 
  • 1 free connection to xpm10 in the BOS
  • all streams are blocked on dma single-event-buffers, so need to create a separate stream to transfer calibration constants on beginrun
    • maybe make this "maintenance" stream permanent?
  • couldn't launch kernels on anything except default stream because cuda context hadn't been bound to CPU threads.  async GPU processes failed with no error.
  • set up basic skeleton for transferring the calibration constants to a  gpuMemPool (transitions, constants (maybe on the beginrun transition?)) and launches a dummy calibration on L1Accepts
  • did both the full serial number and a subset (2 segments) to fetch calibration 
  • calibration constant dictionary hierarchy: pedestal and gain array (one per gain range).  need status/geometry/.  could try to do something general?  map a python dictionary to a std::map?
• Fundamental idea in CPU-DRP is a MemPool with a file-descriptor
  • changing it affects many things
  • put many file-descriptors in there?  not easy.
  • GPUMemPool and CPUMemPool
    • both inherit from base MemPool which inherits pebble/transitions
    • derived classes handle file-descriptor and dma buffers
    • the cudacontext broke the symmetry between the cpu/gpu side
  • separated mempool into a PGP side and a pebble side.
    • got cpu-drp to run with the new structure
    • tried on gpu-drp, but issues arose.  made progress over weekend.
    • missing a lot of code for multiple file descriptors