In the exascale computing era, optimizing MPI collective performance in high-performance computing (HPC) applications is critical. Current algorithms face performance degradation due to system call overhead, page faults, or data-copy latency, affecting HPC applications' efficiency and scalability. To address these issues, we propose PiP-MColl, a Process-in-Process-based Multi-object Inter-process MPI Collective design that maximizes small message MPI collective performance at scale. PiP-MColl features efficient multiple sender and receiver collective algorithms and leverages Process-in-Process shared memory techniques to eliminate unnecessary system call, page fault overhead, and extra data copy, improving intra- and inter-node message rate and throughput. Our design also boosts performance for larger messages, resulting in comprehensive improvement for various message sizes. Experimental results show that PiP-MColl outperforms popular MPI libraries, including OpenMPI, MVAPICH2, and Intel MPI, by up to 4.6X for MPI collectives like MPI_Scatter and MPI_Allgather.
@article{arxiv.2305.10612,
title = {Accelerating MPI Collectives with Process-in-Process-based Multi-object Techniques},
author = {Jiajun Huang and Kaiming Ouyang and Yujia Zhai and Jinyang Liu and Min Si and Ken Raffenetti and Hui Zhou and Atsushi Hori and Zizhong Chen and Yanfei Guo and Rajeev Thakur},
journal= {arXiv preprint arXiv:2305.10612},
year = {2023}
}