Benchmarking in the Data Center: Expanding to the Cloud
workshop held in conjunction with PPoPP 2021: Principles and Practice of Parallel Programming 2021
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 so 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.
Date and Location
Saturday, February 27, 17:00 - 21:00, UTC-5, Eastern Time (US & Canada)
Online Event
Co-located with PPoPP 2021
Submissions
We invite novel, unpublished research paper submission within the scope of this workshop.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:- Multi-, many-core CPUs, GPUs, hybrid system evaluation
- Performance, power, efficiency, and cost analysis
- HPC, data center, and cloud workloads and benchmarks
- System, workload, and workflow configuration and optimization
Full paper: | Up to 10 pages (not including references) |
Short paper: | Up to 5 pages (including references) |
All submissions must be made electronically through the submission site (to be announced). Full paper submissions must be in PDF formatted printable on both A4 and US letter size paper. All papers must be prepared in ACM Conference Format using the 2-column acmart format: use the SIGPLAN proceedings template acmart-sigplanproc-template.tex for Latex, and interim-layout.docx for Word. You may also want to consult the official ACM information on the Master Article Template and related tools. Important note: The Word template (interim-layout.docx) on the ACM website uses 9pt font; you need to increase it to 10pt.
Paper text typeface should be no smaller than 10 point. References must include the name of all authors (not {et al.}), and there is no page limit for references in full paper submissions. Appendices are not allowed, but the authors may provide supplementary material (proofs or source code etc.) to the Program Chairs. Review of supplementary material is at the discretion of the reviewers; papers must be complete and self-contained.
Submission is double blind and authors will need to identify any potential conflicts of interest with PC and Extended Review Committee members, as defined here: ACM SIGPLAN policy.
Workshop submission site: https://easychair.org/conferences/?conf=bid2021Workshop Program
Speaker abstracts and bios can be found here: View full programFeb 27, 17:00 - 21:00, UTC-5, Eastern Time (US & Canada) | |
17:00: Welcome Remark, 2”, Juan (Jenny) Chen, Workshop Chair | |
Keynote Session (17:10 - 19:45) | |
17:10-17:35 | Mr. Sven Breuner, VAST Data & Dr. Chin Fang, Zettar Title: elbencho – A new storage benchmark for AI et al Slides Video Moderator: Benson Muite |
17:35-17:45 | Q&A Moderator: Benson Muite |
17:45-18:10 | Dr. Wanling Gao, Institute of Computing Technology, Chinese Academy of Sciences Title: AIBench Scenario: Scenario-distilling AI Benchmarking Slides Video Moderator: Juan (Jenny) Chen |
18:10-18:20 | Q&A Moderator: Juan (Jenny) Chen |
18:20-18:45 | Dr. Jason (Zhixiang) Ren, Peng Cheng Laboratory, China Title: AIPerf: AutoML as an AI-HPC benchmark Video Moderator: Kevin Brown |
18:45-18:55 | Q&A Moderator: Kevin Brown |
18:55 - 19:10, Coffee Break | |
19:10-19:30 | Dr. Dan Huang, Sun Yat-sen University, China Title: A Comprehensive Study of In-Memory Computing on Large HPC Systems Slides Video Moderator: Samar Aseeri |
19:35-19:45 | Q&A Moderator: Samar Aseeri |
Panel Session | |
19:45-20:30 | Industry Panel Discussion Panelists: Dr. Sreeram Potluri, NVIDIA, USA Dr. Jithin Jose, Microsoft, USA Ms. Verónica G. Melesse Vergara, Oakridge Leadership Computing Facility, USA Slides Moderators: Ammar Awan, Shahzeb Siddiqui |
20:30-20:45 | General Discussion |
Close Remarks |
Resgister for access to the workshop Zoom meeting.
Registration
Please register at the main conference website, here.
Schedule
Paper submission deadline: | |
Preliminary author notification on presentation: | |
Workshop: | 27 February 2021, 17:00 Easter Time (US & Canada) |
Final manuscripts due: | 29 March 2021 AoE |
Organizing Committee
Juan (Jenny) Chen (National University of Defense Technology, China)
Kevin Brown (Argonne National Laboratory)
Contact: [email protected]Industrial Panel Chairs
Ammar Awan (Microsoft Research)
Shahzeb Siddiqui (Lawrence Berkeley National Laboratory)
Contact: [email protected]Web
Xinxin (Alva) Qi (National University of Defense Technology, China)