Chakra ‐ Advancing ML with Standardized Execution Graphs
Chakra, a novel framework transforming machine learning (ML) performance benchmarking and co-design. Central to Chakra is the concept of Standardized Execution Graphs that redefine the representation and evaluation of ML workloads.
- Foundation of Chakra: These graphs are pivotal in Chakra, offering a standardized, adaptable format to represent ML processes.
- Detailed Workload Analysis: They enable intricate interaction and dependency mapping within ML systems, crucial for identifying performance bottlenecks.
- Optimization and Benchmarking: These graphs facilitate accurate benchmarking and optimization, particularly in distributed ML systems.
- Compatibility: Chakra seamlessly integrates with popular ML frameworks like PyTorch, TensorFlow, and FlexFlow, enabling the collection and conversion of execution traces into the Chakra format.
- Execution Trace Conversion: Chakra includes tools for converting execution traces from these frameworks into a unified Chakra format, enhancing interoperability and analysis.
- Enhanced Performance Analysis: Leveraging execution graphs for detailed performance analysis, Chakra identifies and addresses system inefficiencies.
- Collaborative Co-design: The framework bridges the gap between hardware and software, fostering collaboration and innovation in ML system performance optimization.
- Versatility Across Architectures: Chakra demonstrates adaptability in profiling and optimizing ML workloads across various hardware architectures, showcasing its wide applicability.
Chakra stands out for its innovative use of Standardized Execution Graphs in ML performance benchmarking and co-design. These graphs, integrated with frameworks like PyTorch and FlexFlow, enable a unified, effective approach for analyzing, benchmarking, and optimizing ML systems. This methodology is poised to significantly influence future ML development, promoting enhanced efficiency and collaborative innovation in the field.