malt2.torch

malt-dstorm2 for torch packages

See the malt website for more details about MALT2.

1. Source your torch/cuda/MKL environment: on some machines, you might need things something like: source [torch-dir]/install/bin/torch-activate source /opt/intel/mkl/bin/intel64/mklvars.sh intel64

2. Before installing this torch module, you must compile dstorm and liborm) First, checkout the parent module:

   git clone https://github.com/malt2/malt2 --recursive
   

   and type make.

3. Run a quick test.

   • if MPI, then you'll need to run via mpirun, perhaps something like: mpirun -np 3 which th pwd -P/test.lua mpi 2>&1 | tee test-mpi.log

   • if GPU, mpirun -np 3 which th pwd -P/test.lua gpu 2>&1 | tee test-GPU-gpu.log

     • NEW: a WITH_GPU compile can also run with MPI transport mpirun -np 3 which th pwd -P/test.lua mpi 2>&1 | tee test-GPU-mpi.log

   • default transport is set to the "highest" built into libdstorm2:

     • GPU > MPI > SHM mpirun -np 3 which th pwd -P/test.lua 2>&1 | tee test-best.log

   • a very basic test is to run luajit (or th) and then try, by hand, require "torch" require "malt2"

4. Run over multiple GPUs. MPI only sees the hostname. By default, on everyhost, MPI jobs enumerate the GPUs and start running the processes. The only way to change this and run on other GPUs in a round-robin fashion is to change this enumeration for every rank using CUDA_VISIBLE_DEVICES. An example script is in redirect.sh. To run: mpirun -np 2 ./redirect.sh which th pwd/test.lua

   This script assigns available GPUs in a round-robin fashion. Since MPI requires visibility of all other GPUs to correctly access shared memory, this script only changes the enumeration order and does not restrict visibility.

TODO: cuda code has too many kernel invocations (can be streamlined) lua interface support for CudaDoubleTensor and DoubleTensor likely incomplete other improvements documented in source code