Embedded Runtime for Reconfigurable Dataflow Graphs on Manycore Architectures

Embedded manycore architectures offer energy-efficient super-computing capabilities but are notoriously difficult to program with traditional parallel Application Programming Interfaces (APIs). To address this challenge, dataflow Models of Computation (MoCs) are increasingly used as their high-level of abstraction eases the automation of computation mapping, memory allocation, and communication management. Reconfigurable dataflow is a class of dataflow MoC that fosters a unique trade-off between application dynamicity and predictability. This paper introduces the first embedded runtime manager enabling the execution of reconfigurable dataflow graphs on a Non-Uniform Memory Access (NUMA) architecture. The proposed runtime manager dynamically deploys reconfigurable dataflow graphs on clustered Processing Elements (PEs) through the Networks-on-Chips (NoCs) of the manycore architecture. An open-source implementation on the Kalray MPPA® processor demonstrates the feasibility and the great potential of such a runtime. The first results with an image processing application show a power efficiency 2.5 times better than on a multicore x86 architecture.