Poster

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

Srinivas ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ Hanjiang Wu ⋅ Changhai Man ⋅ Jinsun Yoo ⋅ Huan Xu ⋅ William Won ⋅ ⋅ Winston Liu ⋅ Andrey Balogh ⋅ Dan Mihailescu ⋅ Brad B ⋅ Vinay Ramakrishnaiah ⋅ Spandan More ⋅ Saeed Rashidi ⋅ Louis Feng ⋅ Ashwin Ramachandran ⋅ Puneet Sharma ⋅ ⋅ Vijay Janapa Reddi ⋅ David Kanter ⋅ Tushar Krishna

[ OpenReview]

Abstract

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 Traces~(ET). These ETs represent key operations, such as compute, memory, and communication, data and control dependencies, timing, and resource constraints. Additionally, Chakra includes a complementary set of tools and capabilities to enable the collection, analysis, generation, and adoption of Chakra ETs by a broad range of simulators, emulators, and replay tools. We present analysis of Chakra traces collected on production AI clusters and demonstrate value via real-world case studies. Chakra has been adopted by MLCommons and has active contributions and engagement across the industry, including but not limited to, NVIDIA, AMD, Meta, Keysight, HPE, and Scala, to name a few.

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