Optimizing Parallel System Efficiency: Dynamic Task Graph Adaptation with Recursive Tasks
摘要
Task-based programming models significantly improve the efficiency of parallel systems. The Sequential Task Flow (STF) model focuses on static task sizes within task graphs, but determining optimal granularity during graph submission is tedious. To overcome this, we extend StarPU’s STF recursive tasks model, enabling dynamic transformation of tasks into subgraphs. Early evaluations on homogeneous shared memory reveal that this just-in-time adaptation enhances performance.