High-Performance Computing (HPC) have evolved to be used to perform simulations of systems where physical experimentation is prohibitively impractical, expensive, or dangerous. This paper provides a general overview and showcases the analysis of non-functional properties in RISC-V-based platforms for HPCs. In particular, our analyses target the evaluation of power and energy control, thermal management, and reliability assessment of promising systems, structures, and technologies devised for current and future generation of HPC machines. The main set of design methodologies and technologies developed within the activities of the Future and HPC & Big Data spoke of the National Centre of HPC, Big Data and Quantum Computing project are described along with the description of the testbed for experimenting two-phase cooling approaches.

RISC-V-Based Platforms for HPC: Analyzing Non-functional Properties for Future HPC and Big-Data Clusters

Fornaciari, William;Reghenzani, Federico;Terraneo, Federico;Baroffio, Davide;
2023-01-01

Abstract

High-Performance Computing (HPC) have evolved to be used to perform simulations of systems where physical experimentation is prohibitively impractical, expensive, or dangerous. This paper provides a general overview and showcases the analysis of non-functional properties in RISC-V-based platforms for HPCs. In particular, our analyses target the evaluation of power and energy control, thermal management, and reliability assessment of promising systems, structures, and technologies devised for current and future generation of HPC machines. The main set of design methodologies and technologies developed within the activities of the Future and HPC & Big Data spoke of the National Centre of HPC, Big Data and Quantum Computing project are described along with the description of the testbed for experimenting two-phase cooling approaches.
2023
Proceedings of SAMOS 2023
978-3-031-46076-0
978-3-031-46077-7
High Performance Computing (HPC) · Power Modeling and Control · Reliability · RISC-V-based Platform
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1255759
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