Skip to content

Latest commit

 

History

History
35 lines (22 loc) · 2.53 KB

README.md

File metadata and controls

35 lines (22 loc) · 2.53 KB
Raw

PARAM

PARAM Benchmarks is a repository of communication and compute micro-benchmarks as well as full workloads for evaluating training and inference platforms.

PARAM complements two broad categories of commonly used benchmarks:

  1. C++ based stand-alone compute and communication benchmarks using cuDNN, MKL, NCCL, MPI libraries - e.g., NCCL tests (https://github.com/NVIDIA/nccl-tests), OSU MPI benchmarks (https://mvapich.cse.ohio-state.edu/benchmarks/), and DeepBench (https://github.com/baidu-research/DeepBench).
  2. Application benchmarks such as Deep Learning Recommendation Model (DLRM) and the broader MLPerf benchmarks. Its worth noting that while MLPerf is the de-facto industry standard for benchmarking ML applications we hope to compliment this effort with broader workloads that are of more interest to Facebook with more in-depth analysis of each within this branch of Application benchmarks.

Our initial release of PARAM benchmarks focuses on AI training and comprises of:

  1. Communication: PyTorch based collective benchmarks across arbitrary message sizes, effectiveness of compute-communication overlap, and DLRM communication patterns in fwd/bwd pass
  2. Compute: PyTorch based GEMM, embedding lookup, and linear layer
  3. DLRM: tracks the ext_dist branch of DRLM benchmark use Facebook's DLRM benchmark (https://github.com/facebookresearch/dlrm). In short, PARAM fully relies on DLRM benchmark for end-to-end workload evaluation; with additional extensions as required for scale-out AI training platforms.
  4. PyTorch Execution Trace (ET) replay based tests: The PyTorch ET capturing capabilities, which have recently been introduced, allow for the recording of runtime information of a model at the operator level. This capability enables the creation of replay-based benchmarks (https://dl.acm.org/doi/abs/10.1145/3579371.3589072) to accurately reproduce the original performance.

In essence, PARAM bridges the gap between stand-alone C++ benchmarks and PyTorch/Tensorflow based application benchmarks. This enables us to gain deep insights into the inner workings of the system architecture as well as identify framework-level overheads by stressing all subcomponents of a system.

Version

0.1 : Initial release

Requirements

  • pytorch
  • future
  • numpy
  • apex

License

PARAM benchmarks is released under the MIT license. Please see the LICENSE file for more information.

Contributing

We actively welcome your pull requests! Please see CONTRIBUTING.md and CODE_OF_CONDUCT.md for more info.