Abstract
We evaluate Julia as a single language and ecosystem paradigm powered by LLVM to develop workflow components for high-performance computing. We run a Gray-Scott, 2-variable diffusion-reaction application using a memory-bound, 7-point stencil kernel on Frontier, the US Department of Energy's first exascale supercomputer. We evaluate the performance, scaling, and trade-offs of (i) the computational kernel on AMD's MI250x GPUs, (ii) weak scaling up to 4,096 MPI processes/GPUs or 512 nodes, (iii) parallel I/O writes using the ADIOS2 library bindings, and (iv) Jupyter Notebooks for interactive analysis. Results suggest that although Julia generates a reasonable LLVM-IR, a nearly 50% performance difference exists vs. native AMD HIP stencil codes when running on the GPUs. As expected, we observed near-zero overhead when using MPI and parallel I/O bindings for system-wide installed implementations. Consequently, Julia emerges as a compelling high-performance and high-productivity workflow composition language, as measured on the fastest supercomputer in the world.
Original language | English |
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Title of host publication | Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 |
Publisher | Association for Computing Machinery |
Pages | 1989-1999 |
Number of pages | 11 |
ISBN (Electronic) | 9798400707858 |
DOIs | |
State | Published - Nov 12 2023 |
Event | 2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 - Denver, United States Duration: Nov 12 2023 → Nov 17 2023 |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 |
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Country/Territory | United States |
City | Denver |
Period | 11/12/23 → 11/17/23 |
Funding
This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the US Department of Energy Office of Science and the National Nuclear Security Administration. This research used resources of the Oak Ridge Leadership Computing Facility and the Experimental Computing Laboratory (ExCL) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725. This work is funded, in part, by Bluestone, a X-Stack project in the DOE Advanced Scientific Computing Office with program manager Hal Finkel. This manuscript has been authored by UT-Battelle LLC under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (https://energy.gov/downloads/doe-public-access-plan).
Keywords
- Frontier supercomputer
- HPC
- High-Performance Computing
- Julia
- Jupyter notebooks
- data analysis
- end-to-end workflows
- exascale