Taking on RISC for Energy-Efficient Computing in HEP

In pursuit of energy-efficient solutions for computing in High Energy Physics (HEP) we have extended our investigations of non-x86 architectures beyond the ARM platforms that we have previously studied. In this work, we have taken a first look at the RISC-V architecture for HEP workloads, leveraging advancements in both hardware and software maturity. We introduce the Pioneer Milk-V, a 64-core RISC-V machine running Fedora Linux, as our new testbed, available at ScotGrid Glasgow (UK) and INFN Bologna and Pisa (Italy). Despite this early stage of RISC-V adoption in HEP, significant progress has been made in software compatibility. Standard frameworks such as ROOT, Geant4, CVMFS, and XRootD can be successfully built and run on the RISC-V platform, showcasing the evolving ecosystem. Additionally, efforts are underway to port CMSSW, promising further integration of HEP experiment software. In this first study, we assess performance and power efficiency, and we leverage various benchmarking tools to compare the RISC-V system with existing ARM and x86 architectures. Although it is not yet possible to run the HEPScore suite, we have conducted other tests such as running ROOT, DB12 and Geant4 benchmarks, while assessing and comparing performance per Watt across various platforms. These early results suggest that RISC-V architecture, despite being still immature for large scale adoption, holds potential for entering the increasingly heterogeneous computing landscape of HEP.

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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701163
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Taking on RISC for Energy-Efficient Computing in HEP

T. Boccali, Shahzad Muzaffar, A. G. Borbély, Samuel Cadellin Skipsey et al.
EPJ Web of Conferences
Parallel Computing and Optimization Techniques
article

Taking on RISC for Energy-Efficient Computing in HEP

T. Boccali, Shahzad Muzaffar, A. G. Borbély, Samuel Cadellin Skipsey, Gordon Stewart, D. Britton, E. Simili
article en

Abstract

In pursuit of energy-efficient solutions for computing in High Energy Physics (HEP) we have extended our investigations of non-x86 architectures beyond the ARM platforms that we have previously studied. In this work, we have taken a first look at the RISC-V architecture for HEP workloads, leveraging advancements in both hardware and software maturity. We introduce the Pioneer Milk-V, a 64-core RISC-V machine running Fedora Linux, as our new testbed, available at ScotGrid Glasgow (UK) and INFN Bologna and Pisa (Italy). Despite this early stage of RISC-V adoption in HEP, significant progress has been made in software compatibility. Standard frameworks such as ROOT, Geant4, CVMFS, and XRootD can be successfully built and run on the RISC-V platform, showcasing the evolving ecosystem. Additionally, efforts are underway to port CMSSW, promising further integration of HEP experiment software. In this first study, we assess performance and power efficiency, and we leverage various benchmarking tools to compare the RISC-V system with existing ARM and x86 architectures. Although it is not yet possible to run the HEPScore suite, we have conducted other tests such as running ROOT, DB12 and Geant4 benchmarks, while assessing and comparing performance per Watt across various platforms. These early results suggest that RISC-V architecture, despite being still immature for large scale adoption, holds potential for entering the increasingly heterogeneous computing landscape of HEP.

EPJ Web of ConferencesVol. 337
Istituto Nazionale di Fisica Nucleare, Sezione di Pisa (IT), European Organization for Nuclear Research (CH), University of Glasgow (GB)
European Commission, University of Glasgow
Openalex Percentile: Top 70%
Parallel Computing and Optimization Techniques
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Taking on RISC for Energy-Efficient Computing in HEP — T. Boccali, Shahzad Muzaffar, et al. · EPJ Web of Conferences (2025) | TGRS Research Map | TGRS