{"paper":{"title":"MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Chakra establishes a standardized graph-based execution trace format to represent distributed AI workloads for benchmarking and co-design.","cross_cats":["cs.LG","cs.PF"],"primary_cat":"cs.DC","authors_text":"Andy Balogh, Ashwin Ramachandran, Bradford M. Beckmann, Brian Coutinho, Changhai Man, Dan Mihailescu, David Kanter, Hanjiang Wu, Huan Xu, Jinsun Yoo, Joongun Park, Josh Ladd, Louis Feng, Mehryar Garakani, Phio Tian, Puneet Sharma, Saeed Rashidi, Sanshan Gao, Sheng Fu, Spandan More, Srinivas Sridharan, Taekyung Heo, Tushar Krishna, Vijay Janapa Reddi, Vinay Ramakrishnaiah, William Won, Winston Liu, Ziwei Li","submitted_at":"2026-05-11T23:38:10Z","abstract_excerpt":"The fast pace of artificial intelligence~(AI) innovation demands an agile methodology for observation, reproduction and optimization of distributed machine learning~(ML) workload behavior in production AI systems and enables efficient software-hardware~(SW-HW) co-design for future systems. We present Chakra, an open and portable ecosystem for performance benchmarking and co-design. The core component of Chakra is an open and interoperable graph-based representation of distributed AI/ML workloads, called Chakra execution trace~(ET). These ETs represent key operations, such as compute, memory, a"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The core component of Chakra is an open and interoperable graph-based representation of distributed AI/ML workloads, called Chakra execution trace (ET). These ETs represent key operations, such as compute, memory, and communication, data and control dependencies, timing, and resource constraints.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the proposed Chakra ET format and associated tools will be effectively adopted and used by a broad range of simulators, emulators, and replay tools to deliver meaningful improvements in benchmarking and co-design.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Chakra introduces a portable, interoperable graph-based execution trace format for distributed ML workloads along with supporting tools to standardize performance benchmarking and software-hardware co-design.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Chakra establishes a standardized graph-based execution trace format to represent distributed AI workloads for benchmarking and co-design.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"58c076b44d18faa7fecfe1d3a2b2f9db31044da5b57bb8b2241f975d9ef52f7e"},"source":{"id":"2605.11333","kind":"arxiv","version":2},"verdict":{"id":"0d84f0b2-9137-432f-a0ae-33920100e2e5","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T01:24:54.212830Z","strongest_claim":"The core component of Chakra is an open and interoperable graph-based representation of distributed AI/ML workloads, called Chakra execution trace (ET). 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