Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects
<|reference_start|>Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects: Multi-GPU nodes are increasingly common in the rapidly evolving landscape of exascale supercomputers. On these systems, GPUs on the same node are connected through dedicated networks, with bandwidths up to a few terabits per second. However, gauging performance expectations and maximizing system efficiency is challenging due to different technologies, design options, and software layers. This paper comprehensively characterizes three supercomputers - Alps, Leonardo, and LUMI - each with a unique architecture and design. We focus on performance evaluation of intra-node and inter-node interconnects on up to 4096 GPUs, using a mix of intra-node and inter-node benchmarks. By analyzing its limitations and opportunities, we aim to offer practical guidance to researchers, system architects, and software developers dealing with multi-GPU supercomputing. Our results show that there is untapped bandwidth, and there are still many opportunities for optimization, ranging from network to software optimization.<|reference_end|>
@article{de sensi2024exploring,
    title={Exploring GPU-to-GPU Communication: Insights into Supercomputer
  Interconnects},
    author={Daniele De Sensi, Lorenzo Pichetti, Flavio Vella, Tiziano De Matteis,
  Zebin Ren, Luigi Fusco, Matteo Turisini, Daniele Cesarini, Kurt Lust, Animesh
  Trivedi, Duncan Roweth, Filippo Spiga, Salvatore Di Girolamo, Torsten Hoefler},
    journal={Published in Proceedings of The International Conference for High
  Performance Computing Networking, Storage, and Analysis (SC '24) (2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14090},
    primaryClass={cs.DC cs.AI cs.AR cs.NI cs.PF}
}