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Preparing sparse solvers for exascale computing.
Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'
Evaluating the Potential of Disaggregated Memory Systems for HPC applications
Disaggregated memory is a promising approach that addresses the limitations
of traditional memory architectures by enabling memory to be decoupled from
compute nodes and shared across a data center. Cloud platforms have deployed
such systems to improve overall system memory utilization, but performance can
vary across workloads. High-performance computing (HPC) is crucial in
scientific and engineering applications, where HPC machines also face the issue
of underutilized memory. As a result, improving system memory utilization while
understanding workload performance is essential for HPC operators. Therefore,
learning the potential of a disaggregated memory system before deployment is a
critical step. This paper proposes a methodology for exploring the design space
of a disaggregated memory system. It incorporates key metrics that affect
performance on disaggregated memory systems: memory capacity, local and remote
memory access ratio, injection bandwidth, and bisection bandwidth, providing an
intuitive approach to guide machine configurations based on technology trends
and workload characteristics. We apply our methodology to analyze thirteen
diverse workloads, including AI training, data analysis, genomics, protein,
fusion, atomic nuclei, and traditional HPC bookends. Our methodology
demonstrates the ability to comprehend the potential and pitfalls of a
disaggregated memory system and provides motivation for machine configurations.
Our results show that eleven of our thirteen applications can leverage
injection bandwidth disaggregated memory without affecting performance, while
one pays a rack bisection bandwidth penalty and two pay the system-wide
bisection bandwidth penalty. In addition, we also show that intra-rack memory
disaggregation would meet the application's memory requirement and provide
enough remote memory bandwidth.Comment: The submission builds on the following conference paper: N. Ding, S.
Williams, H.A. Nam, et al. Methodology for Evaluating the Potential of
Disaggregated Memory Systems,2nd International Workshop on RESource
DISaggregation in High-Performance Computing (RESDIS), November 18, 2022. It
is now submitted to the CCPE journal for revie