Efficient implementation of data flow graphs on multi-gpu clusters by Vincent Boulos, Sylvain Huet, Vincent Fristot, Luc Salvo and Dominique Houzet.
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 high level programming model based on a data flow graph (DFG) allowing an efficient implementation of digital signal processing applications on a multi-GPU computer cluster. This DFG-based design flow abstracts the underlying architecture. We focus particularly on the efficient implementation of communications by automating computation-communication overlap, which can lead to significant speedups as shown in the presented benchmark. The approach is validated on three experiments: a multi-host multi-gpu benchmark, a 3D granulometry application developed for research on materials and an application for computing visual saliency maps.
Analysis of the statistics of sizes in images (granulometry) and focusing on a particular place of interest in an image (visual saliency) were interesting use cases.
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