SIGPLAN logoBenchmarking in the Data Center

Workshop Scope and Topics

High performance computing (HPC) is no longer confined to universities and national research laboratories, it is increasingly used in industry. HPC use is growing and has significant industrial users. Education of students also needs to take this into account. Users need to be able to evaluate what benefits HPC can bring to their companies, what type of computational resources (e.g. multi-, many-core CPUs, GPUs, hybrid systems) would be best for their workloads and how they can evaluate what they should pay for these resources. Another issue that arises in shared computing environments is privacy: in commercial HPC environments, data produced and software used typically has commercial value, and so needs to be protected. Recent general adoption of machine learning has motivated migration of HPC to traditional data centers, and there is a growing interest by the community on performance evaluation in this area. In addition to traditional performance benchmarking and high performance system evaluation (including absolute performance, energy efficiency), as well as configuration optimizations, this workshop will discuss issues that are of particular importance in commercial HPC. Benchmarking has typically invovled running specific workloads that are reflective of typical of computational science and engineering, yet with growing diversity of workloads, theoretical performance modeling is also of interest to allow for performance prediction given a minimal set of measurements. The workshop solicits short papers that can later be expanded to full papers for a special volume addressing the above and related topics

Date and Location

Saturday 22 February

Hilton San Diego Resort and Spa, San Diego, California, USA


This ACM SIGPLAN approved workshop solicits:


Submissions here.


15,2024 January 2020: Papers due

25,2728 January 2020: Notification of acceptance/rejection of papers

Morning 22 February 2019: Workshop

Workshop Program

Time Author(s) Title Presentation Paper
9:00am-9:10am Samar Aseeri Opening slides paper
9:10am-9:35am Zhixin Ou, Juan Chen, Yunfang Zhang and Zheng Wang Power Modeling for Phytium FT-2000+ Multi-core Architecture paper
9:35am-10:00am Ammar Awan Benchmarking Deep Learning Workloads on Large-scale HPC Systems slides
10:00am-10:30am All Attendees Coffee Break
10:30am-10:55am Mahidhar Tatineni Evolution of Benchmarking on SDSC systems slides
10:55am-11:20am Christopher H Chang, Ilene L Carpenter and Wesley B Jones The ESIF-HPC-2 Benchmark Suite slides paper
11:20am-12:00noon All Attendees Discussion and Closing


Please register at the main conference website, here


Organizing committee

Samar Aseeri (King Abdullah University of Science and Technology)

Benson Muite (Kichakato Kizito)

Program committee

David Bailey (Lawrence Berkeley National Laboratory and University of California Davis)

Valeria Bartsch (Fraunhofer ITWM)

Ben Blamey (Uppsala University)

Rodrigo N. Calheiros (Western Sydney University)

Anando Chatterjee (Indian Institute of Technology Kanpur)

Juan Chen (National University of Defense Technology)

Paweł Czarnul (Gdansk University of Technology)

Denis Demidov (Kazan Federal University and Russian Academy of Sciences)

Joel Guerrero (University of Genoa and Wolf Dynamics)

Khaled Ibrahim (Lawrence Berkeley National Laboratory)

Kate Isaacs (University of Arizona)

Beau Johnston (Australian National University and University of New England)

Michael Lehn (University of Ulm)

Maged Korga

Guo Liang (Open Data Center Committee and China Academy of Information and Communications Technology)

Xiaoyi Lu (Ohio State University)

Amitava Majumdar (San Diego Supercomputing Center)

Jorji Nonaka (Riken)

Peter Pirkelbauer (Lawrence Livermore National Laboratory)

Harald Servat (Intel)

Ashwin Siddarth (University of Texas at Arlington)

Manodeep Sinha (Swinburne University of Technology)

Gábor Szárnyas (Budapest University of Technology and Economics)

Mahidhar Tatineni (San Diego Supercomputing Center)

Jianfeng Zhan (Chinese Academy of Sciences)