Adaptation Fidelity of SPEC CPU2026

[2608.27710] Adaptation Fidelity of SPEC CPU2026 Skip to main content Search Submit Donate Log in Computer Science > Performance arXiv:2608.27710 (cs) [Submitted on 27 Aug 2026] Title: Adaptation Fidelity of SPEC CPU2026 Authors: Doa'a Al-Otoom , Mahesh Madhav View PDF HTML (experimental) Abstract: Standardized benchmarks are often criticized for not being "real workloads," but this critique is rarely backed by data. This paper provides the first systematic, quantitative analysis of the "fidelity gap" between the SPEC CPU2026 suite and its original, upstream open-source counterparts. We compile both the SPEC benchmarks and their upstream applications and execute them with official input workloads under two scenarios: a single-copy latency run and a 192-copy throughput run. Our findings show that most benchmarks exhibit high fidelity in single-copy runs, while a few outliers reveal the impact of SPEC's adaptation process. The multi-copy results further highlight the necessity of this adaptation: several benchmarks become significantly more efficient than their upstream versions under heavy load, underscoring the importance of I/O reduction. This work offers data-driven validation of SPEC's methodology, showing that the fidelity gap is not a flaw but a quantifiable consequence of enforcing portability, determinism, and CPU-centric measurement. Comments: 5 pages, 2 figures, Presented at the 2026 IEEE International Symposium on Workload Characterization (IISWC 2026), Boulder, Colorado Subjects: Performance (cs.PF) MSC classes: 68U01 ACM classes: B.8.2; C.4 Cite as: arXiv:2608.27710 [cs.PF] (or arXiv:2608.27710v1 [cs.PF] for this version) https://doi.org/10.48550/arXiv.2608.27710 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mahesh Madhav [ view email ] [v1] Thu, 27 Aug 2026 20:56:52 UTC (138 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.PF < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• Adaptation Fidelity of SPEC CPU2026#779632
• CCL-Bench 1.0: A Trace-Based Benchmark for LLM Infrastructure#168235
• Automatic Model-Hardware Co-Adaptation for Heterogeneous AI Accelerators#820550
• SPECTRA: Adaptive Execution of Speculative Decoding on a Runtime-Reconfigurable Tiled Architecture#1051246
• SPECTRA: Adaptive Execution of Speculative Decoding on a Runtime-Reconfigurable Tiled Architecture#1028648