Benchmarking in the Data Center: Expanding to the Cloud
Workshop Scope and Topics
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
Sunday, 28 February, 2021 8:00am KST
Co-located with PPoPP 2021
SubmissionsThe call for paper will be announced soon.
|Paper submission deadline:||15 January 2021 AoE|
|Author notification:||8 February 2021|
|Workshop:||28 February 2021, 8:00am KST|
Workshop ProgramTo be announced.
Please register at the main conference website, here.
Juan (Jenny) Chen (National University of Defense Technology, China)
Kevin Brown (Argonne National Laboratory)
Industrial Panel Chairs
Ammar Awan (Microsoft Research)
Shahzeb Siddiqui (Lawrence Berkeley National Laboratory)
To be announced.
Xinxin (Alva) Qi (National University of Defense Technology, China)