Julia as a unifying end-to-end workflow language on the Frontier exascale system
Oak Ridge National Laboratory, Oak Ridge, TN, USA
arXiv:2309.10292 [cs.DC], (19 Sep 2023)
@article{godoy2023julia,
title={Julia as a unifying end-to-end workflow language on the Frontier exascale system},
author={Godoy, William F and Valero-Lara, Pedro and Anderson, Caira and Lee, Katrina W and Gainaru, Ana and da Silva, Rafael Ferreira and Vetter, Jeffrey S},
journal={arXiv preprint arXiv:2309.10292},
year={2023}
}
We evaluate using 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 feasibility, 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 data analysis. Our results suggest that although Julia generates a reasonable LLVM-IR kernel, 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 strategy, as measured on the fastest supercomputer in the world.
September 24, 2023 by hgpu
