A Visual Programming Model to Implement Coarse-Grained DSP Applications on Parallel and Heterogeneous Clusters

Abstract

The digital signal processing (DSP) applications are one of the biggest consumers of computing. They process a big data volume which is represented with a high accuracy. They use complex algorithms, and must satisfy a time constraints in most of cases. In the other hand, it's necessary today to use parallel and heterogeneous architectures in order to speedup the processing, where the best examples are the supercomputers "Tianhe-2" and "Titan" from the top500 ranking. These architectures could contain several connected nodes, where each node includes a number of generalist processor (multi-core) and a number of accelerators (many-core) to finally allows several levels of parallelism. However, for DSP programmers, it's still complicated to exploit all these parallelism levels to reach good performance for their applications. They have to design their implementation to take advantage of all heterogeneous computing units, taking into account the architecture specificities of each of them: communication model, memory management, data management, jobs scheduling and synchronization. .. etc. In the present work, we characterize DSP applications, and based on their distinctiveness, we propose a high level visual programming model and an execution model in order to drop down their implementations and in the same time make desirable performances.

A domain‐specific high‐level programming model

“…We also experiment with our approach on a real‐world application based on the primate retina, with the visual saliency model used to locate the regions of interest, that is, the capability of human vision to focus on particular places in a visual scene. For implementation, we use Algorithm 2 that was described in the preliminary work of our team . To implement the application with our programming models, the first step is to model Algorithm 2 with a DFG‐XML description using the SignalPU interface.…”

“…We also experiment with our approach on a real-world application based on the primate retina, with the visual saliency model used to locate the regions of interest, that is, the capability of human vision to focus on particular places in a visual scene. For implementation, we use Algorithm 2 that was described in the preliminary work of our team [39,40]. Node CPU GPU Archi1 Intel-i7 core (4 cores) 2 NVIDIA Quadro4000 + NVIDIA Quadro2000 Archi2 Intel-i7 core (4 cores) NVIDIA GTX TITAN + NVIDIA GTX780 Archi3 Intel-i7 core (4 cores) NVIDIA GTX680 + NVIDIA GTX680 Archi4 Intel-i7 core (4 cores) NVIDIA GTX480 Figure 8.…”