skip to main content


Search for: All records

Award ID contains: 2018627

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available August 1, 2024
  2. Free, publicly-accessible full text available August 1, 2024
  3. Free, publicly-accessible full text available July 23, 2024
  4. Summary To accelerate the communication between nodes, supercomputers are now equipped with multiple network adapters per node, also referred to as HCAs (Host Channel Adapters), resulting in a “multi‐rail”/“multi‐HCA” network. For example, the ThetaGPU system at Argonne National Laboratory (ANL) has eight adapters per node; with this many networking resources available, utilizing all of them becomes non‐trivial. The Message Passing Interface (MPI) is a dominant model for high‐performance computing clusters. Not all MPI collectives utilize all resources, and this becomes more apparent with advances in bandwidth and adapter count in a given cluster. In this work, we provide a thorough performance analysis of existing multirail solutions and their implications on collectives and present the necessity for further enhancement. Specifically, we propose novel designs for hierarchical, multi‐HCA‐aware Allgather. The proposed designs fully utilize all the available network adapters within a node and provide high overlap between inter‐node and intra‐node communication. At the micro‐benchmark level, we see large inter‐node improvements up to 62% and 61% better than HPC‐X and MVAPICH2‐X for 1024 processes. Because Allgather is used in Ring‐Allreduce, our designs also improve its performance by 56% and 44% compared to HPC‐X and MVAPICH2‐X, respectively. At the application level, our enhanced Allgather shows and improvement in a matrix‐vector multiplication kernel when compared to HPC‐X and MVAPICH2‐X, and Allreduce performs up to 7.83% better in deep learning training against MVAPICH2‐X. 
    more » « less
    Free, publicly-accessible full text available July 1, 2024
  5. Free, publicly-accessible full text available June 21, 2024
  6. Free, publicly-accessible full text available June 17, 2024
  7. Free, publicly-accessible full text available May 1, 2024
  8. Free, publicly-accessible full text available May 1, 2024
  9. Free, publicly-accessible full text available May 1, 2024
  10. Free, publicly-accessible full text available May 1, 2024