EPJ Web Conf.
Volume 245, 202024th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|Number of page(s)||11|
|Section||7 - Facilities, Clouds and Containers|
|Published online||16 November 2020|
Beyond HEP: Photon and accelerator science computing infrastructure at DESY
DESY, Notkestraße 85, D-22607 Hamburg, Germany
Published online: 16 November 2020
DESY is one of the largest accelerator laboratories in Europe. It develops and operates state of the art accelerators for fundamental science in the areas of high energy physics, photon science and accelerator development. While for decades high energy physics (HEP) has been the most prominent user of the DESY compute, storage and network infrastructure, various scientific areas as science with photons and accelerator development have caught up and are now dominating the demands on the DESY infrastructure resources, with significant consequences for the IT resource provisioning. In this contribution, we will present an overview of the computational, storage and network resources covering the various physics communities on site.
Ranging from high-throughput computing (HTC) batch-like offline processing in the Grid and the interactive user analyses resources in the National Analysis Factory (NAF) for the HEP community, to the computing needs of accelerator development or of photon sciences such as PETRA III or the European XFEL. Since DESY is involved in these experiments and their data taking, their requirements include fast low-latency online processing for data taking and calibration as well as offline processing, thus high-performance computing (HPC) workloads, that are run on the dedicated Maxwell HPC cluster.
As all communities face significant challenges due to changing environments and increasing data rates in the following years, we will discuss how this will reflect in necessary changes to the computing and storage infrastructures.
We will present DESY compute cloud and container orchestration plans as a basis for infrastructure and platform services. We will show examples of Jupyter notebooks for small scale interactive analysis, as well as its integration into large scale resources such as batch systems or Spark clusters.
To overcome the fragmentation of the various resources for all scientific communities at DESY, we explore how to integrate them into a seamless user experience in an Interdisciplinary Data Analysis Facility.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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