Speakers
Description
The Einstein Telescope, the third generation ground-based interferometer for gravitational wave detection, will observe a sky volume one thousand times larger than the second generation interferometers. This will be reflected in a higher observation rate. The physics information contained in the “strain” time series will increase, while on the machine side the size of the raw data from the instrument will scale with the number and the complexity of the detectors, that will be either four or six, depending on the chosen geometrical configuration. To meet ET specific computing needs, an adequate choice of the technologies, the tools and the framework to handle the collected data, share them among the interested users and enable their offline analysis is mandatory. On the computing side, since ET is expected to begin the data taking in no less than ten years, it is crucial to keep up with the technology that is always improving and to test the new architectures which gradually become available. In INFN-Torino, we are setting up a computing cluster dedicated to Technology Tracking, where machines with heterogeneous architectures are made available to the ET community to develop analysis algorithms and test them on advanced hardware. The cluster is orchestrated via Kubernetes, the authentication is provided via Indigo-IAM and equipped with a custom tool for resource booking written in Play. In INFN-Torino, the Rucio server and one of the storage elements for data distribution is under test and available for Mock Data Challenges. It is also connected to the Technology Tracking cluster. In this talk a description of the computing cluster deployment will be given.