AI RESEARCH
MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces
arXiv CS.LG
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ArXi:2605.11333v1 Announce Type: cross 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.