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
