mlsanityData to 14 Oct 2026

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

arXiv 2605.11333 ↗First posted 11 May 2026cs.DC cs.LG cs.PFcited by 0

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Abstract (arXiv)

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, 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 ETs 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 42 references that are in the map. First posted on arXiv 11 May 2026.

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15 of 42 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2024DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Servingcited by 617
  4. 2023Splitwise: Efficient generative LLM inference using phase splittingcited by 558
  5. 2019Deep Learning Recommendation Model for Personalization and Recommendation Systemscited by 327
  6. 2020Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platformscited by 25
  7. 2023Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Tracescited by 13
  8. 2021Automap: Towards Ergonomic Automated Parallelism for ML Modelscited by 12
  9. 2025Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Trainingcited by 7
  10. 2022Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarkscited by 5
  11. 2025Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspectivecited by 5
  12. 2021LIBRA: Enabling Workload-aware Multi-dimensional Network Topology Optimization for Distributed Training of Large AI Modelscited by 3
3 more
  1. 2025Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learningcited by 3
  2. 2021DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distributioncited by 2
  3. 2025ATLAHS: An Application-centric Network Simulator Toolchain for AI, HPC, and Distributed Storagecited by 2

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