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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 R 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.
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Dates and versions

hal-01704702 , version 1 (08-02-2018)



Hugo Miomandre, Julien Hascoët, Karol Desnos, Kevin Martin, Benoît Dupont de Dinechin, et al.. Embedded Runtime for Reconfigurable Dataflow Graphs on Manycore Architectures. PARMA-DITAM, Jan 2018, Manchester, United Kingdom. ⟨10.1145/3183767.3183780⟩. ⟨hal-01704702⟩
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