SC19 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

HPCG Benchmark Update

Authors: Michael Heroux (Sandia National Laboratories, St. John’s University), Jack Dongarra (University of Tennessee), Piotr Luszczek (University of Tennessee)

Abstract: The High Performance Conjugate Gradients (HPCG) Benchmark is a TOP500 benchmark for ranking high performance computing systems. First results were released at ISC'14, including optimized results for systems built upon Fujitsu, Intel, Nvidia technologies. Optimized versions of HPCG are available for all major HPC platforms. Industry analyses cite HPCG in combination with LINPACK as indication of system balance.

In this BoF, we first present HPCG updates, then follow with presentations from vendors who have participated in recent HPCG optimization efforts, including recent AMD and Arm efforts. We end with open discussion about future improvements.

Long Description: The High Performance Conjugate Gradients (HPCG) Benchmark is designed to test features of a high performance computing (HPC) system in a way that complements the high performance Linpack (HPL) benchmark. HPL tends to approach the maximum achievable floating point performance on a given system, while most real applications reach a tiny fraction of what HPL achieves. In contrast, HPCG achieves a much smaller fraction of peak performance and instead tests interconnect latency and bandwidth of the interconnect network, the manycore/accelerator nodes and individual processors.

HPL derives its performance from an existing collection of optimized dense matrix kernels. As a result, achieving good performance from HPL is fairly straightforward. HPCG is a new benchmark that depends primarily on sparse linear algebra and optimization strategies and implementations are still emerging. Even so, Fujitsu, IBM, Intel, Nvidia and their related leadership computing facilities have made significant investments in HPCG optimization, obtaining substantial performance improvements.

In this BOF we review the architecture of the HPCG reference code, emphasizing opportunities for improving its performance and describing what kinds of optimizations are permissible, especially for the latest version HPCG 3.0. We follow this with a presentation from each of the computer system vendors on how they have optimized HPCG for their systems. We conclude the BOF with a general discussion about HPCG, future plans for its design and implementation and questions. At the very end we intend to announce the latest rankings and give awards for the top 3 systems.

This BOF will be valuable to any HPC community member who is interested in benchmarking and how HPCG can be used to obtain understanding about large-scale system performance.


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