PICO: Performance Insights for Collective Operations

[2508.16809] PICO: Performance Insights for Collective Operations Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2508.16809 (cs) [Submitted on 22 Aug 2025 ( v1 ), last revised 11 Apr 2026 (this version, v2)] Title: PICO: Performance Insights for Collective Operations Authors: Saverio Pasqualoni , Tommaso Bonato , Lorenzo Piarulli , Torsten Hoefler , Marco Canini , Daniele De Sensi View a PDF of the paper titled PICO: Performance Insights for Collective Operations, by Saverio Pasqualoni and 5 other authors View PDF HTML (experimental) Abstract: Collective operations are cornerstones of both HPC applications and large-scale AI training and inference, yet benchmarking them in a systematic and reproducible way remains difficult on modern systems due to the complexity of their hardware and software stacks. Existing suites primarily report end-to-end timings and offer limited support for controlled algorithm and configuration selection, fine-grained profiling, and capturing the runtime environment. We present PICO (Performance Insights for Collective Operations), an open-source framework that decouples portable experiment setup from platform execution, provides a backend-adaptive parameter selection interface across MPI and NCCL, supplies plain-MPI reference collective implementations, optionally instrumentable, and records the system configuration for reproducible comparisons. Evaluated on three major supercomputers, PICO shows that default collective algorithms and transport settings can be up to $5\times$ slower than the best available choice. It provides diagnostic evidence by isolating topology sensitive algorithmic choices and, through instrumentation, reveals detailed algorithmic breakdowns. To assess end-to-end effects of benchmark-informed tuning and evaluate application-level impacts, we replay open-source LLM training traces in ATLAHS simulator with optimized collective profiles identified by PICO, achieving reductions in training times of up to $44\%$. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) ; Performance (cs.PF) Cite as: arXiv:2508.16809 [cs.DC] (or arXiv:2508.16809v2 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2508.16809 Focus to learn more arXiv-issued DOI via DataCite Journal reference: ISC High Performance 2026 Research Paper Proceedings (41st International Conference) Related DOI : https://doi.org/10.23919/ISC.2026.11520486 Focus to learn more DOI(s) linking to related resources Submission history From: Saverio Pasqualoni [ view email ] [v1] Fri, 22 Aug 2025 21:30:59 UTC (2,543 KB) [v2] Sat, 11 Apr 2026 18:53:48 UTC (588 KB) Full-text links: Access Paper: View a PDF of the paper titled PICO: Performance Insights for Collective Operations, by Saverio Pasqualoni and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.DC < prev | next > new | recent | 2025-08 Change to browse by: cs cs.PF References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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