CuBLonD usage and performance

• kick, drift, histogram, etc. comparison on single CPU/GPU with single and double precision
  • up to a factor 5 for large enough input
• mixed CPU/GPU arrays that can be accessed and updated from either
• multi-GPU scaling: we don't gain a factor 2 with 2 GPUs
  • some non-parallel regions and communication time
• MPI experiments with multiple workers
  • local histogram calculation and reduce_all() to calculate global histogram
  • use of approximate methods (update only every x turns, "SRP", or assume every worker has a representative subset, "RDS")
  • 20xCPU + 1xGPU -> up to 60x speed-up with approximations
  • comparison of 2013 and 2017 GPU architecture -> good scaling, future-proof
• Future work
  • distributed initialisation (if e.g. beam doesn't fit in a single memory)
  • OpenCL for multi-GPU
  • Experiments with equal-capability CPU & GPU -> test of load-balancing in heterogeneous HW

Easy to merge, so it should be integrated into the basic BLonD code. Code can be used for mixed MPI multi-CPU and GPU environment. A cluster with 4 GPUs or more would be ideal. (in most heavy simulations, CPUs were used just for small calculations)