[Hidl-discuss] Announcing the release of MPI4cuML 0.5

[Panda, Dhabaleswar]

The High-Performance Deep Learning (HiDL) team is pleased to announce
the release of MPI4cuML 0.5, which is a custom version of the cuML and
associated RAFT libraries with support for the MVAPICH2 high-performance CUDA-aware communication backend. The communication handle in cuML uses mpi4py over the MVAPICH2-GDR library and targets modern HPC clusters built with GPUs and high-performance interconnects.

This release of the cuML package is equipped with the following
features:

* MPI4cuML 0.5:

    - Based on cuML 22.02.00
        - Include ready-to-use examples for KMeans, Linear Regression,
          Nearest Neighbors, and tSVD
    - MVAPICH2 support for RAFT 22.02.00
        - Enabled cuML’s communication engine, RAFT, to use MVAPICH2-GDR backend for
          Python and C++ cuML applications
           - KMeans, PCA, tSVD, RF, LinearModels
        - Added switch between available communication backends (MVAPICH2 and NCCL)
    - Built on top of mpi4py over the MVAPICH2-GDR library
    - Tested with
        - Mellanox InfiniBand adapters (FDR and HDR)
        - Various x86-based multi-core platforms (AMD and Intel)
        - NVIDIA A100, V100, and P100 GPUs

For downloading the MPI4cuML package and the associated user guide,
please visit the following URL:

http://hidl.cse.ohio-state.edu

Sample performance numbers for MPI4cuML using machine learning
application benchmarks can be viewed by visiting the `Performance' tab
of the above website.

All questions, feedback and bug reports are welcome. Please post to
hidl-discuss at lists.osu.edu.

Thanks,

The High-Performance Deep Learning (HiDL) Team
http://hidl.cse.ohio-state.edu

PS: The number of organizations using the HiDL stack has crossed 75
(from 39 countries).  The HiDL team would like to thank all its users
and organizations!!