PiP-MColl: Process-in-Process-based Multi-object MPI Collectives

In the era of exascale computing, the adoption of a large number of CPU cores and nodes by high-performance computing (HPC) applications has made MPI collective performance increasingly crucial. As the number of cores and nodes increases, the importance of optimizing MPI collective performance becomes more evident. Current collective algorithms, including kernel-assisted inter-process data exchange techniques and data sharing based shared-memory approaches, are prone to significant performance degradation due to the overhead of system calls and page faults or the cost of extra data-copy latency. These issues can negatively impact the efficiency and scalability of HPC applications. To address these issues, we propose PiP-MColl, a Process-in-Process-based Multi-object Interprocess MPI Collective design that maximizes small message MPI collective performance at scale. We also present specific designs to boost the performance for larger messages, such that we observe a comprehensive improvement for a series of message sizes beyond small messages. PiP-MColl features efficient multiple sender and receiver collective algorithms and leverages Processin-Process shared memory techniques to eliminate unnecessary system call, page fault overhead and extra data copy, which results in improved intra- and inter-node message rate and throughput. Experimental results demonstrate that PiP-MColl significantly outperforms popular MPI libraries, including OpenMPI, MVAPICH2, and Intel MPI, by up to 4.6X for the MPI collectives MPI Scatter, MPI Allgather, and MPI Allreduce.