DFG implementation on multi GPU cluster with computation-communication overlap

Abstract

Nowadays, it is possible to build a multi-GPU supercomputer, well suited for implementation of digital signal processing algorithms, for a few thousand dollars. However, to achieve the highest performance with this kind of architecture, the programmer has to focus on inter-processor communications, tasks synchronization . . . In this paper, we propose a design flow allowing an efficient implementation of a Digital Signal Processing (DSP) application specified as a Data Flow Graph (DFG) on a multi GPU computer cluster. We focus particularly on the effective implementation of communications by automating the computation-communication overlap, which can lead to significant speedups as shown in the presented benchmark. The approach is validated on a 3D granulometry application developed for research on materials.

Silhouette Processing Via Mathematical Morphology with Pose-Aware Structuring Elements Based on 3D Model

“…Usefulness of GPU architecture when applied to image processing was initially verified [1]. Advantages in acceleration to morphological granulometry was also presented in [9]. However in both works, GPU-vendorspecific CUDA (Compute Unified Device Architecture) environment was used instead of portable OpenCL.…”