Stream processing on GPUs using distributed multimedia middleware

Abstract

SUMMARYAvailable GPUs provide increasingly more processing power especially for multimedia and digital signal processing. Despite the tremendous progress in hardware and thus processing power, there are and always will be applications that require using multiple GPUs either running inside the same machine or distributed in the network due to computationally intensive processing algorithms. Existing solutions for developing applications for GPUs still require a lot of hand-optimization when using multiple GPUs inside the same machine and provide in general no or only limited support for using remote GPUs distributed in the network. In this paper we address this problem and show that an open distributed multimedia middleware, like the Network-Integrated Multimedia Middleware (NMM), is able (1) to seamlessly integrate processing components using GPUs, while completely hiding GPU-specific issues from the application developer, (2) to transparently combine processing components using GPUs or CPUs, and (3) to transparently use local and remote GPUs for distributed processing. Furthermore, we present a generic distribution framework to simplify the development of complex application scenarios.

On the support of task-parallel algorithmic skeletons for multi-GPU computing

“…Beyond the algorithm skeleton realm, the StreamIt [7] language and the NMM [11] multimedia middleware leverage the use of multiple GPUs for stream processing, mainly applied to the multimedia and digital signal processing fields. Mapping stream processing onto GPUs is done by partitioning the graph across the multiple devices, and by streaming the data along this graph in a pipelined fashion, migrating it between GPUs when required, until the end is reached.…”