profilingDeepLearning

Profiling Deep Learning frameworks

Led by: Murali Emani, Denis Boyda

Communication profiling

We introduce two profiling tools for understanding the communication in distributed deep learning.

• MPI flat profiling using mpitrace To turn on the profiling, one has to set the following environment variable LD_PRELOAD.

  export LD_PRELOAD=/lus/theta-fs0/software/datascience/thetagpu/hpctw/lib/libmpitrace.so
  

  Then run the application as usual. MPI profiling results will be generated after the run finishes mpi_profile.XXXX.[rank_id].

• Horovod timeline To perform Horovod timeline analysis, one has to set the environment variable HOROVOD_TIMELINE which specifies the file for the output.

  export HOROVOD_TIMELINE=timeline.json
  

  This file is only recorded on rank 0, but it contains information about activity of all workers. You can then open the timeline file using the chrome://tracing facility of the Chrome browser.

More details: https://horovod.readthedocs.io/en/stable/timeline_include.html

Single node performance

Tensorflow Profiler

We introduce you to profiling using TensorFlow. Text Here

Intel Vtune profiler

This is for profiling on Intel architecture. IntelProfiler/

Nvidia Nsight profiler

This is for profiling on Nvidia architecture. NvidaProfiler/