A Distributed Framework for Low-Latency OpenVX over the RDMA NoC of a Clustered Manycore

International audienceOpenVX is a standard proposed by the Khronos group for cross-platform acceleration of computer vision and deep learning applications. OpenVX abstracts the target processor architecture complexity and automates the implementation of processing pipelines through high-level optimizations. While highly efficient OpenVX implementations exist for shared memory multi-core processors, targeting OpenVX to clustered manycore processors appears challenging. Indeed, such processors comprise multiple compute units or clusters, each fitted with an on-chip local memory shared by several cores. This paper describes an efficient implementation of OpenVX that targets clustered manycore processors. We propose a framework that includes computation graph analysis, kernel fusion techniques, RDMA-based tiling into local memories, optimization passes, and a distributed execution runtime. This framework is implemented and evaluated on the 2nd-generation Kalray MPPA (R) clustered manycore processor. Experimental results show that super-linear speed-ups are obtained for multi-cluster execution by leveraging the bandwidth of on-chip memories and the capabilities of asynchronous RDMA engines