Dataflow Graph

Dataflow graphs represent computations as directed graphs, where nodes are operations and edges represent data dependencies, aiming to optimize parallel execution and resource utilization. Current research focuses on improving efficiency through techniques like fine-grained orchestration, critical-path-based parallelization, and optimized memory management, often employing machine learning for scheduling and resource allocation. These advancements are crucial for accelerating various applications, particularly in machine learning and large language model deployments, leading to significant performance gains in training and inference. The ability to represent and analyze dataflow graphs also facilitates tasks such as fault localization and security analysis in complex systems.