HPCG benchmark

The HPCG (high performance conjugate gradient) benchmark is a supercomputing benchmark test proposed by Jack Dongarra of the University of Tennessee, with Piotr Luszczek and Michael Heroux.[1][2] It is intended to model the data access patterns of real-world applications such as sparse matrix calculations,[3] thus testing the effect of limitations of the memory subsystem and internal interconnect of the supercomputer on its computing performance. Because it is internally I/O bound, HPCG testing generally achieves only a tiny fraction of the peak FLOPS of the computer.[4]

HPCG is intended to complement benchmarks such as the LINPACK benchmarks that put relatively little stress on the internal interconnect.[5] The source of the HPCG benchmark is available on GitHub.

As of June 2016, the Tianhe-2 supercomputer held the top spot in the HPCG performance rankings, followed by the K computer and the Sunway TaihuLight.[6]

References

  1. ↑ Hemsoth, Nicole (June 26, 2014). "New HPC Benchmark Delivers Promising Results". HPCWire. Retrieved 2014-09-08.
  2. ↑ Dongarra, Jack; Heroux, Michael (June 2013). "Toward a New Metric for Ranking High Performance Computing Systems" (PDF). Sandia National Laboratory. Retrieved 2016-07-04.
  3. ↑ Trader, Tiffany (2015-07-16). "LINPACK's 'Companion Metric' Gains Traction". HPCwire. Retrieved 2016-07-04.
  4. ↑ Jackson, Adrian (30 July 2015). "HPCG: benchmarking supercomputers". www.epcc.ed.ac.uk. EPCC at the University of Edinburgh. Retrieved 2016-07-04.
  5. ↑ Brueckner, Rich (2015-07-13). "Latest HPCG Performance List Complements TOP500". Inside HPC. Retrieved 2016-07-04.
  6. ↑ "June 2016 HPCG Results". hpcg-benchmark.org. Retrieved 2016-07-04.

See also

External links


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