Exploitation of HPC Resources for data intensive sciences

M Girone ,Luca Atzori,Ingvild Brevik Høgstøyl,V Khristenko ,Miguel F Medeiros,D.l Southwick ,D Giordano

doi:10.1051/epjconf/202125102042

Abstract

The Large Hadron Collider (LHC) will enter a new phase beginning in 2027 with the upgrade to the High Luminosity LHC (HL-LHC). The increase in the number of simultaneous collisions coupled with a more complex structure of a single event will result in each LHC experiment collecting, storing, and processing exabytes of data per year. The amount of generated and/or collected data greatly outweighs the expected available computing resources. In this paper, we discuss effcient usage of HPC resources as a prerequisite for data-intensive science at exascale. First, we discuss the experience of porting CMS Hadron and Electromagnetic calorimeters reconstruction code to utilize Nvidia GPUs within the DEEP-EST project; second, we look at the tools and their adoption in order to perform benchmarking of a variety of resources available at HPC centers. Finally, we touch on one of the most important aspects of the future of HEP - how to handle the flow of petabytes of data to and from computing facilities, be it clouds or HPCs, for exascale data processing in a flexible, scalable and performant manner. These investigations are a key contribution to technical work within the HPC collaboration among CERN, SKA, GEANT and PRACE.

Highlights

The field of High-Performance Computing (HPC) is undergoing a transition to the major phase of its development, namely that of exascale computing
Accelerated processors like Graphical Processing Units (GPUs) or low-power ARM processors provide the bulk of the computing capacity at the majority of the largest
The DEEP-EST prototype consists of three types of compute modules: Cluster (CM), Extreme-scale Booster (ESB) and

Summary

Introduction

The field of High-Performance Computing (HPC) is undergoing a transition to the major phase of its development, namely that of exascale computing. HPC facilities rely heavily on heterogeneous hardware architectures. Accelerated processors like Graphical Processing Units (GPUs) or low-power ARM processors provide the bulk of the computing capacity at the majority of the largest. The use of heterogeneous architectures is both a challenge and an opportunity [1] for the High Energy Physics (HEP) community.

The DEEP-EST Project

Perform minimization of χ2 for the just computed correlation matrix

Containerized Benchmarking on HPC

Extending HEP benchmarking for HPC

HPC and Data Access

Conclusion