HPC 2017-18 | High-performance computing


The path to an energy- efficient exascale supercomputer


Adrian Giordani explores the methods used to accurately measure the performance of supercomputers


Te International Supercomputing Conference (ISC17) closed on 22 June 2017 in Frankfurt, Germany, ending an eventful week from a growing community energised on how to advance the sector. Highlights included the latest product announcements, updates on machine learning, GPU accelerators, the conclusion of the sixth iteration of the computing cluster competition and – of course – the Top500 supercomputer list. China’s Sunway TaihuLight climbed to the


top of the supercomputing list, achieving 93 quadrillion operations per second (93 Pflops) on approximately 15 megawatts of power. Even with Sunway TaihuLight’s achievement, it is clear that we may be living in a post-Moore’s law world where processor performance growth is slowing overall. Te next question when you compare the


Sunway TaihuLight’s Top500 list achievement to the goal of exascale computing – a system that will run one billion billion calculations per second – is how will the first exascale supercomputer keep energy consumption low while sustaining far more computational power? Te Top500 list contestants all compete using the Linpack benchmark, which


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uses the IEEE Standard 754 floating point arithmetic, measured in floating operations per second (flops) that solves a dense system of linear equations, most of which are dense matrix–matrix multiplications. Currently, some mavericks within the supercomputing community are striving to shiſt the focus to methods that produce consistent reproducible results, while also looking at whole applications to give a better idea of real-world performance.


Diversifying benchmarks for real- world performance While a flops-based approach keeps pushing managers of supercomputing centres onwards in one dimension of complexity, other lists have emerged to compliment the Top500. For example, the Green500 list looks at Linpack flops-per-watt for energy efficiency. At this year’s ISC17 another


complementary benchmark to the High Performance Linpack (HPL), known as the High Performance Conjugate Gradients (HPCG) benchmark, entered its seventh year. HPCG placed the Sunway TaihuLight


system in fiſth place on its list of 110 entries – and Japan’s Riken/Fujitsu K Computer at number one. To date HPCG, which measure


performance that is more representative of how today’s scientific calculations perform, has been run on many large-scale supercomputing systems in Europe, Japan and the US. Last year, in a peer-reviewed paper


published in the journal International Journal of High Performance Computing Applications, Jack Dongarra, director of the University of Tennessee’s Innovative Computing Laboratory, US, who has been involved in Linpack’s development since 1993, along with two other colleagues, analysed the performance of HPCG in comparison to HPL. Te team concluded that their preliminary


tests show that HPCG exhibits performance levels that are far below the levels seen by HPL, one of the main reasons being the so-called memory wall. Still, HPCG scales equally well when compared with HPL. ‘HPCG, in addition to HPL, is a good


benchmark and should be run on every new system in addition to running HPL. HPCG shows a different characteristic of the system that is benchmarked and should be a good addition,’ said Robert Henschel, chair of the Standard Performance Evaluation Corporation’s High-Performance Group (SPEC/HPG). ‘I disagree with the statement that HPCG


measures “real application performance”,’ said Horst Simon, deputy laboratory director and chief research officer at Lawrence Berkeley National Laboratory, US, and co-editor of the biannual TOP500 list. ‘Since HPCG is mostly determined by this fundamental speed of the machine, it will correlate with HPL in the foreseeable future.’ Ten there is the High Performance


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