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The increase in scale and heterogeneity of high-performance computing (HPC) systems predispose the performance of Message Passing Interface (MPI) collective communications to be susceptible to noise, and to adapt to a complex mix of hardware capabilities. The designs of state of the art MPI collectives heavily rely on synchronizations; these designs magnify noise across the participating processes, resulting in significant performance slowdown. Therefore, such design philosophy must be reconsidered to efficiently and robustly run on the large-scale heterogeneous platforms. In this paper, we present ADAPT, a new collective communication framework in Open MPI, using event-driven techniques to morph collective algorithms to heterogeneous environments. The core concept of ADAPT is to relax synchronizations, while mamtaining the minimal data dependencies of MPI collectives. To fully exploit the different bandwidths of data movement lanes in heterogeneous systems, we extend the ADAPT collective framework with a topology-aware communication tree. This removes the boundaries of different hardware topologies while maximizing the speed of data movements. We evaluate our framework with two popular collective operations: broadcast and reduce on both CPU and GPU clusters. Our results demonstrate drastic performance improvements and a strong resistance against noise compared to other state of the art MPI libraries. In particular, we demonstrate at least 1.3X and 1.5X speedup for CPU data and 2X and 10X speedup for GPU data using ADAPT event-based broadcast and reduce operations.
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Evaluating the Viability of LogGP for Modeling MPI Performance with Non-contiguous Datatypes on Modern Architectures
Modern architectures and communication systems software include complex hardware, communication abstractions, and optimizations that make their performance difficult to measure, model, and understand. This paper examines the ability of modified versions of the existing Netgauge communication performance measurement tool and LogGOPS performance model to accurately characterize communication behavior of modern hardware, MPI abstractions, and implementations. This includes analyzing their ability to model both GPU-aware communication in different MPI implementations and quantifying the performance characteristics of different approaches to non-contiguous data communication on modern GPU systems. This paper also applies these techniques to quantify the performance of different implementations and optimization approaches to non-contiguous data communication on a variety of systems, demonstrating that modern communication system design approaches can result in widely-varying and difficult-to-predict performance variation, even within the same hardware/communication software combination.
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- Award ID(s):
- 2103510
- PAR ID:
- 10463017
- Date Published:
- Journal Name:
- EuroMPI'23: Proceedings of the 30th European MPI Users' Group Meeting
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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