Chakra execution traces for benchmarking and network performance optimization

• Meta has introduced Chakra execution traces, a graph-based representation of AI/ML workload execution, to enable benchmarking and network performance optimization.
• Traditional AI benchmarking methods rely on running full ML workloads, which makes it difficult to forecast future system performance.
• Chakra execution traces provide a standardized schema for performance modeling and offer tools for conversion, visualization, generation, and simulation of these traces.
• Visualization and analysis of collective message sizes in production execution traces can help optimize performance.
• The Chakra working group, in collaboration with MLCommons, is addressing challenges and developing an open ecosystem of tools for benchmarks, simulations, and emulations.
• Generative AI models can be used to identify and generate execution traces that are representative of observed characteristics.
• Interested individuals and companies are invited to join the Chakra working group and contribute to the paradigm shift in benchmarking and network performance optimization.