Sun, X., Singer, J. and Wang, Z. orcid.org/0000-0001-6157-0662 (2025) Sweet or Sour CHERI: Performance Characterization of the Arm Morello Platform. In: Proceedings of the 2025 IEEE International Symposium on Workload Characterization (IISWC). 2025 IEEE International Symposium on Workload Characterization (IISWC), 12-14 Oct 2025, Irvine, California, USA. . IEEE. ISBN: 979-8-3315-4917-6. ISSN: 2835-2238. EISSN: 2835-2238.
Abstract
Capability Hardware Enhanced RISC Instructions (CHERI) is an emerging hardware approach to memory safety that enforces strong spatial and temporal protections. This paper presents the most comprehensive performance evaluation of CHERI to date. Using on-chip performance monitoring counters (PMCs) on the CHERI-enabled Arm Morello platform, we analyze 20 C/C++ applications, including SPEC CPU2017, a SQL database engine, a JavaScript engine, and a large language model inference framework, across three CHERI Application Binary Interfaces (ABIs). We find that CHERI overheads range from negligible to 1.65×, with the highest costs in pointer-intensive and memory-sensitive workloads, largely due to increased memory traffic and higher L1/L2 cache pressure from 128-bit capabilities. Importantly, our projections suggest that modest microarchitectural improvements could significantly reduce these costs, enabling CHERI to deliver memory safety with minimal performance impact. We hope these findings offer timely evidence to guide the development of future architectures that combine strong memory security with high performance.
Metadata
| Item Type: | Proceedings Paper |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | This is an author produced version of a conference paper published in Proceedings of the 2025 IEEE International Symposium on Workload Characterization (IISWC), made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Dates: |
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| Institution: | The University of Leeds |
| Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
| Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/X037304/1 |
| Date Deposited: | 12 Sep 2025 11:16 |
| Last Modified: | 21 Apr 2026 12:35 |
| Status: | Published |
| Publisher: | IEEE |
| Identification Number: | 10.1109/IISWC66894.2025.00042 |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:231424 |
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