Benchmarking in the Data Center: Expanding to the Cloud

Workshop held in conjunction with ICPE 2024: the 15th ACM/SPEC International Conference on Performance Engineering 2024

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

We invite novel, unpublished research paper submission within the scope of this workshop. Paper submission topics include, but are not limited to, the following areas:

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’24’s workshop track.

Workshop submission site: BID 24 (EasyChair)

Schedule

Paper submission deadline: 01. February 2024 (23:59 AoE) Final extension: 15. February 2024 (23:59 AoE)
Author notification: 01. March 2024
Camera ready deadline: 07. March 2024 (23:59 AoE)
Workshop date: 7-11. May 2024

Date and Location

7-11 May 2024
South Kensington, London, UK (Room: TBD)
Co-located with ICPE 2024

Workshop Program

TBD

Registration

ICPE 2024 Registration

Attending

We will support presenters with travel restrictions by setting up a way to present remotely. However, regular registration fees, as stated on ICPE website, will still apply.

Organizing Committee

Wei-Chen (Tom) Lin (Department of Computer Science, University of Bristol)

Jens Domke (RIKEN Center for Computational Science)

Contact: chairs2024(at)parallel.computer

Program Committee

Aksel Alpay (Universität Heidelberg)

Aleksandar Ilic (Universidade de Lisboa)

Andrey Alekseenko (KTH Royal Institute of Technology)

Filippo Spiga (NVIDIA)

Swapna Raj (NVIDIA)

Joseph Schuchart (University of Tennessee)

Mustafa Abduljabbar (Ohio State University)

Mubarak Ojewale (KAUST)

Ravi Reddy Manumachu (University College Dublin)

Tom Deakin (University of Bristol)

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)