Invited Paper: Programming Dynamic Task Parallelism for Heterogeneous EDA Algorithms

Many EDA applications are extremely sparse, irregular, and control-flow intensive. Parallelizing this type of application can benefit from the ability to express dynamic task parallelism across arbitrary decision-making points at runtime. Unlike the traditional construct-and-run models, dynamic task parallelism offers programmers great flexibility to parallelize EDA algorithms that incorporate complex execution logic under dynamic control flow, such as branch- and-bound techniques, on-the-fly pruning, and recursive decomposition strategies. In this paper, we introduce a new programming model that supports the dynamic building of a computational task graph. We will cover scheduling details and best practices for exploring task parallelism under dynamic control flow. We will present a real use case of our model that has successfully parallelized a static timing analysis workload.