HPCG benchmark

The HPCG (high performance conjugate gradient) 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. 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. Because it is internally I/O bound, HPCG testing generally achieves only a tiny fraction of the peak FLOPS of the computer.

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

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

In November of 2020 they were superseded by Fugaku with a speed of 16.0 HPCG-petaflops, an increase of 540% over Summit.

See also

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

External links

• HPCG Benchmark
• source code