MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces

arXiv:2605.11333v3 Announce Type: replace-cross Abstract: 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). Thes

Source: arXiv cs.LG — read the full report at the original publisher.

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