Speaker
Description
Modern detectors in scientific infrastructures yield data at higher rates and in larger volumes. Despite the extensive data reduction on detector electronics, scientists employ software-based functions, e.g., high-level triggers, for further online data reduction that run on a dedicated computer cluster located at the scientific infrastructure's site. As a consequence, scaling, operating, and maintaining the stability and performance of this cluster becomes a demanding responsibility that requires extensive time and manpower. This paper proposes Data Acquisition Functions Virtualization (DFV), a new paradigm to minimize these operational efforts by eliminating computer clusters in scientific infrastructures and by running software-based online data reduction functions on widely available general-purpose campus computing facilities. DFV leverages computer virtualization and high-performance Ethernet networking to isolate software-based functions on campus computing facilities while sustaining their required input throughput. We explore the key technical challenges to realize DFV and propose the Data Acquisition Development Kit (DQDK), a novel framework for high-performance Ethernet-based readout functions and a cornerstone in DFV. We quantify the performance of the framework with and without computer virtualization, considering different computer virtualization setups. The new data acquisition paradigm is applied to the TRISTAN upgrade of the KATRIN experiment at the Karlsruhe Institute of Technology. The framework can reduce CPU resources by a factor of 2.67x and save up to 15% of the consumed energy.
| Minioral | Yes |
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| IEEE Member | No |
| Are you a student? | No |