27214

SCALSALE: Scalable SALE Benchmark Framework for Supercomputers

Re’em Harel, Matan Rusanovsky, Ron Wagner, Harel Levin, Gal Oren
Department of Computer Science, Ben-Gurion University of the Negev, Israel
arXiv:2209.01983 [cs.DC], (5 Sep 2022)

@misc{https://doi.org/10.48550/arxiv.2209.01983,

   doi={10.48550/ARXIV.2209.01983},

   url={https://arxiv.org/abs/2209.01983},

   author={Harel, Re’em and Rusanovsky, Matan and Wagner, Ron and Levin, Harel and Oren, Gal},

   keywords={Distributed, Parallel, and Cluster Computing (cs.DC), Performance (cs.PF), FOS: Computer and information sciences, FOS: Computer and information sciences},

   title={ScalSALE: Scalable SALE Benchmark Framework for Supercomputers},

   publisher={arXiv},

   year={2022},

   copyright={Creative Commons Attribution 4.0 International}

}

Supercomputers worldwide provide the necessary infrastructure for groundbreaking research. However, most supercomputers are not designed equally due to different desired figure of merit, which is derived from the computational bounds of the targeted scientific applications’ portfolio. In turn, the design of such computers becomes an optimization process that strives to achieve the best performances possible in a multi-parameters search space. Therefore, verifying and evaluating whether a supercomputer can achieve its desired goal becomes a tedious and complex task. For this purpose, many full, mini, proxy, and benchmark applications have been introduced in the attempt to represent scientific applications. Nevertheless, as these benchmarks are hard to expand, and most importantly, are over-simplified compared to scientific applications that tend to couple multiple scientific domains, they fail to represent the true scaling capabilities. We suggest a new physical scalable benchmark framework, namely ScalSALE, based on the well-known SALE scheme. ScalSALE’s main goal is to provide a simple, flexible, scalable infrastructure that can be easily expanded to include multi-physical schemes while maintaining scalable and efficient execution times. By expanding ScalSALE, the gap between the over-simplified benchmarks and scientific applications can be bridged. To achieve this goal, ScalSALE is implemented in Modern Fortran with simple OOP design patterns and supported by transparent MPI-3 blocking and non-blocking communication that allows such a scalable framework. ScalSALE is compared to LULESH via simulating the Sedov-Taylor blast wave problem using strong and weak scaling tests. ScalSALE is executed and evaluated with both rezoning options – Lagrangian and Eulerian.
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