LAUSR

Physical synthesis engines need to embrace all available parallelism to cope with the increasing complexity of modern designs and still offer high quality of results. To achieve this goal, the involved algorithms need to be expressed in a way that facilitates fast execution time across a range of computing platforms. In this work, we introduce a task-based parallel programming template that can be used for speeding up timing and power optimization. This approach utilizes all available parallelism and enables significant speedup relative to custom multithreaded approaches. Task-based parallelism is applied to all parts of the optimization engine covering also parts that are traditionally executed serially for preserving maximum timing accuracy. Using Taskflow as the parallel programming and execution engine, we achieved a speedup of $1.7\times $ to $2.8\times $ for gate sizing optimizations on the ISPD13 benchmarks with marginal extra leakage power relative to state-of-the-art multithreaded gate sizers. This result was supported by two dynamic heuristics that restrict the number of examined gate sizes and simplify local timing updates. Both heuristics tradeoff additional runtime reduction with marginal leakage power increases.