Efficient Task-based Code Generation for SDF Graph Execution on Multicore Processors

The dataflow programming paradigm is a flexible and efficient methodology to describe parallel algorithms and optimize their scheduling and mapping in emerging multicore architectures. Transforming dataflow descriptions in executable code without scheduling overheads is however cumbersome, due to the challenge of translating effectively dataflow semantics. One of the promising techniques that have been proposed to address this problem is the correlation of dataflow and task programming models. In this paper we propose an efficient task-based code generator for PREESM dataflow framework. Our generator exploits synchronous dataflow graph information in order to improve task mapping and minimise overheads in the code's execution. We compare our task-based code against the current PREESM task generated code as well as annotated code using the popular OmpSs programming model, for the same test application. Results show that our approach achieves higher application throughput in both symmetric and asymmetric multi-core processors.