Poster 40

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

Srinivas ⋅ Andrey Balogh ⋅ Brad B ⋅ Brian Coutinho ⋅ Louis Feng ⋅ Sheng Fu ⋅ Sanshan Gao ⋅ Mehryar Garakani ⋅ Taekyung Heo ⋅ David Kanter ⋅ Josh Ladd ⋅ Ziwei Li ⋅ Winston Liu ⋅ Changhai Man ⋅ Dan Mihailescu ⋅ Spandan More ⋅ Joongun Park ⋅ Ashwin Ramachandran ⋅ Vinay Ramakrishnaiah ⋅ Saeed Rashidi ⋅ Vijay Janapa Reddi ⋅ Puneet Sharma ⋅ Phio Tian ⋅ William Won ⋅ Hanjiang Wu ⋅ Huan Xu ⋅ Jinsun Yoo ⋅ Tushar Krishna

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 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.