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Accelerating MPI Collectives with process-in-process-based Multi-object Techniques 23

Accelerating MPI Collectives with Process-in-Process-based M...

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32nd International Symposium on High-Performance Parallel and Distributed Computing (HPDC) part of the ACM Federated Computing Research Conference (FCRC)

作者： Huang, Jiajun Ouyang, Kaiming Zhai, Yujia Liu, Jinyang Si, Min Raffenetti, Ken Zhou, Hui Hori, Atsushi Chen, Zizhong Guo, Yanfei Thakur, Rajeev Univ Calif Riverside Riverside CA 92521 USA NVIDIA Corp Santa Clara CA USA Meta Platforms Inc Menlo Pk CA USA Argonne Natl Lab Argonne IL USA Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798400701559

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 Interprocess 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.

关键词： mpi collective process-in-process distributed systems message passing interface parallel algorithms

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PiP-MColl: process-in-process-based Multi-object MPI Collectives 25

PiP-MColl: Process-in-Process-based Multi-object MPI Collect...

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25th IEEE International Conference on Cluster Computing (CLUSTER)

作者： Huang, Jiajun Ouyang, Kaiming Zhai, Yujia Liu, Jinyang Si, Min Raffenetti, Ken Zhou, Hui Hori, Atsushi Chen, Zizhong Guo, Yanfei Thakur, Rajeev Univ Calif Riverside Riverside CA 92521 USA NVIDIA Corp Santa Clara CA USA Meta Platforms Inc Cambridge MA USA Argonne Natl Lab Argonne IL USA Natl Inst Informat Chiyoda City Tokyo Japan

ISBN: (纸本)9798350307924

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.

关键词： MPI Collective Message Passing Interface process-in-process Parallel Algorithms Distributed Systems

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Exploring Interprocess Techniques for High-Performance MPI Communication

Exploring Interprocess Techniques for High-Performance MPI C...

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作者： Ouyang, Kaiming University of California Riverside

学位级别：Ph.D., Doctor of Philosophy

In exascale computing era, applications are executed at larger scale than ever before, which results in higher requirement of scalability for communication library design. Message Passing Interface (MPI) is widely adopted by the parallel application nowadays for interprocess communication, and the performance of the communication can significantly impact the overall performance of applications especially at large scale. There are many aspects of MPI communication that need to be explored for the maximal message rate and network throughput. Considering load balance, communication load balance is essential for high-performance applications. Unbalanced communication can cause severe performance degradation, even in computation-balanced Bulk Synchronous Parallel (BSP) applications. MPI communication imbalance issue is not well investigated like computation load balance. Since the communication is not fully controlled by application developers, designing communicationbalanced applications is challenging because of the diverse communication implementations at the underlying runtime system. In addition, MPI provides nonblocking point-to-point and one-sided communication models where asynchronous progress is required to guarantee the completion of MPI communications and achieve better communication and computation overlap. Traditional mechanisms either spawn an additional background thread on each MPI process or launch a fixed number of helper processes on each node. For complex multiphase applications, unfortunately, severe performance degradation may occur due to dynamically changing communication characteristics. On the other hand, as the number of CPU cores and nodes adopted by the applications greatly increases, even the small message size MPI collectives can result in the huge communication overhead at large scale if they are not carefully designed. There are MPI collective algorithms that have been hierarchically designed to saturate inter-node network bandwidth for

关键词： Asynchronous progress Multiobject interprocess collective Load balance Message Passing Interface process-in-process

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