Benchmarking in the Data Center: Expanding to the Cloud

Workshop held in conjunction with ICPE 2025: the 15th ACM/SPEC International Conference on Performance Engineering 2025

Workshop Scope

High performance computing (HPC) is no longer confined to universities and national research laboratories, it is increasingly used in industry and in the cloud. Education of users 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 hence needs to be protected.

Recent general adoption of machine learning has motivated migration of HPC workloads to cloud data centers, and there is a growing interest by the community on performance evaluation in this area, especially for end-to-end workflows. 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 involved running specific workloads that are reflective of typical HPC workloads, 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 will be composed of submitted papers, invited talks and a panel composed of representatives from industry.

Submission

• Multi-, many-core CPUs, GPUs, accelerators, heterogeneous system evaluation
• Performance, power, efficiency, and cost analysis
• HPC, data center, and cloud workloads and benchmarks
• System, workload, topology, and workflow configuration and optimization

Authors are invited to submit work as regular paper (up to 8 pages including references). All papers must be prepared in ACM Primary Article Template format: proceedings template. The submitted work shall be in the English language. Inclusion of artefact evaluation/description appendix is encouraged and does not count towards the page limit.

Submitted papers will be peer-reviewed by the technical program committee (TPC). Review of supplementary material is at the discretion of the reviewers; papers must be complete and self-contained.

All accepted papers will be indexed and published in the ACM Digital Library after the workshop as part of the ICPE’25’s workshop track.

Workshop submission site: BID 25 (EasyChair)

Schedule

Date and Location

Workshop Program

Registration

ICPE 2025 Registration

Attending

Workshop agenda will be updated soon!

Organizing Committee

Wei-Chen (Tom) Lin ( University of Bristol, UK)

Kaushik Velusamy ( Argonne National Laboratory, USA)

Contact: chairs2025(at)parallel.computer

Program Committee

Katinka Wolter, Freie Universitat Berlin, Germany.

Alessandro Papadopoulos, Mälardalen University, Sweden.

Raffaela Mirandola, Karlsruhe Institute of Technology, Germany.

Weiyi (Ian) Shang, University of Waterloo, Canada.

Lishan Yang, George Mason University, USA.

Alexander Podelko, Amazon, USA.

Ana Lucia Varbanescu, University of Twente, Netherlands.

Y.C. Tay, National University of Singapore, Singapore.

Nikolas Herbst, University of Würzburg, Germany.

Sören Henning, Johannes Kepler University Linz, Austria.

Luca Traini, University of L'Aquila, Italy.

Marios Fokaefs, York University, Canada.

Vincenzo Stoico, Vrije Universiteit Amsterdam, Netherlands.

Local and Social Events Co-chair

Hung Viet Pham, York University, Canada.

Arik Sendorovich, York University, Canada.

Advisory Committee

Samar Aseeri (King Abdullah University of Science and Technology)

Juan (Jenny) Chen (National University of Defense Technology, China)

Benson Muite (Kichakato Kizito)

Kevin A. Brown (Argonne National Laboratory, US)