HPCG benchmark

The High Performance Conjugate Gradients Benchmark is a supercomputing benchmark test proposed by Michael Heroux from Sandia National Laboratories, and Jack Dongarra and Piotr Luszczek from the University of Tennessee.^[1][2] It is intended to model the data access patterns of real-world applications such as sparse matrix calculations, thus testing the effect of limitations of the memory subsystem and internal interconnect of the supercomputer on its computing performance.^[3] Because it is internally I/O bound (the data for the benchmark resides in main memory as it is too large for processor caches), HPCG testing generally achieves only a tiny fraction of the peak FLOPS the computer could theoretically deliver.^[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.^[6]

As of June 2018, the Summit supercomputer held the top spot in the HPCG performance rankings, followed by the Sierra and the K computer.^[7]

In June of 2020, Summit was superseded by Fugaku with a speed of 16.0 HPCG-petaflops (an increase of 540%). Summit is currently 4th,^[8] LUMI 3rd and Frontier 2nd.

See also

• Graph500
• Memory access pattern
• Preconditioned conjugate gradient method
• Traversed edges per second

References

External links

• on GitHub

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