Merging Parameterized Task Graphs in PaRSEC Through JDF Composition with LLM Assistance

The Parameterized Task Graph (PTG) interface in the PaRSEC runtime system provides a powerful mechanism to express scalable, parameterized task dependencies for distributed heterogeneous computing. However, as applications become increasingly complex and hierarchical, developers often need to compose or merge multiple JDF (Job Data Flow) descriptions—each representing a subgraph—into a single coherent PTG, a process that is currently manual, error-prone, and difficult to generalize. In this work, we introduce a Python- and AI-assisted tool to automate the merge-to-JDF process in PaRSEC. The tool recursively analyzes two or more JDFs, identifies overlapping dataflows, and merges their task and data definitions into a unified PTG while preserving correctness and dependency semantics. We demonstrate this approach on representative PTG applications, including multi-stage solvers and AI model, like Transformer, showing that the automated merge tool substantially reduces development effort while maintaining runtime efficiency.