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Multi-GPU Communication Benchmarks

This repo contains benchmarks for different multi GPU communication libraries. The bechmarks included are:

  • Cuda Aware MPI:
    • Point to Point
    • All Reduce
  • NCCL:
    • MPI based Point to Point
    • MPI based All Reduce
    • Single thread Point to Point
    • Single thread All Reduce
  • NVSHMEM:
    • Point to Point
    • All to All (using the builtin all-to-all function)
    • Host initiated Broadcast
    • Boradcast (in device kernel)
    • Max All Reduce
    • Sum All Reduce

Arguments

All the benchmarks contain some command line arguments to alter their behaviour:

Usage: single_thread_all_to_all [OPTION...] 
Single thread all to all with nccl

  -d, --data-len=N           Lenght of the data block.
  -i, --num-iter=N           Number of iterations in the main iteration loop.
  -n, --num-gpus=N           Number of GPUS (ignored if MPI process)
  -t, --data-type=FLOAT|INT|CHAR   Type of the data block.
  -w, --num-warmup-iter=N    Number of iterations in the warmup section. 0 by
                             default.
  -?, --help                 Give this help list
      --usage                Give a short usage message

You need to run MPI based benchmarks with mpirun and specify the number of devices as the number of processes.

Building

Running make in one of the cuda_aware_mpi, nccl, nvshmem folders builds all the benchmarks in that folder. You can also run make in individual folders. Different benchmarks need different environment variables to build. In general you need to define $CUDA_PATH, $NCCL_PATH, $MPI_HOME and $NVSHMEM_HOME.

The makefiles use nvcc, mpicc and g++ that are already on the $PATH by default. If your compiler is in a different path, you can modify the makefiles accordingly.

Results

After one benchmark is completed, a set of results are displayed:

parsing arguments: 0.00
cuda setup: 1.27
warmup, avg: 0.07, 0.00
iterations, avg: 0.01, 0.00
cleanup: 0.04
total: 1.39

The times reported can be imprecise or not correct, the repo needs additional work on that.