Conveners
Converging Infrastructure Clouds, Virtualisation & HPC
- Dieter Kranzlmuller (LMU Munich)
The increasingly pervasive and dominant role of machine learning (ML) and deep learning (DL) techniques in High Energy Physics is posing challenging requirements to effective computing infrastructures on which AI workflows are executed, as well as demanding requests in terms of training and upskilling new users and/or future developers of such technologies.
In particular, a growth in the...
Science is constantly encountering parametric optimization problems whose computer-aided solutions require enormous resources. At the same time, there is a trend towards developing increasingly powerful computer clusters. Geneva is currently one of the best available frameworks for distributed optimization of large-scale problems with highly nonlinear quality surfaces. It is a great tool to be...
Approaching the exascale era, complex challenges arise within the existing high performance computing (HPC) frameworks. Highly optimized, heterogenous hardware systems on the one side, and HPC-unexperienced scientists with a continuously increasing demand for compute and data capacity on the other side. Bringing both together would enable a broad range of scientific domains to enhance the...
High Energy Photon Source (HEPS) will generate massive experimental data for diversified scientific analysis. The traditional way of data download and analysis by users using local computing environment cannot meet the growing demand for experiments. This paper proposes a virtual cloud desktop system of HEPS based on Openstack, which is used for imaging and crystal scattering experiments in...
