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2010
IEEE

Efficiently Using a CUDA-enabled GPU as Shared Resource

8 years 3 months ago
Efficiently Using a CUDA-enabled GPU as Shared Resource
GPGPU is getting more and more important, but when using CUDA-enabled GPUs the special characteristics of NVIDIAs SIMT architecture have to be considered. Particularly, it is not possible to run functions concurrently, although NVIDIAs GPUs consist of many processing units. Therefore, the processing power of GPUs can not be shared among processes, and for an efficient use of the GPU, it has to be fully utilized by a single function launch of a single process. In this contribution we present an approach that overcomes these restrictions. A GPGPU service launches a persistent kernel which consists of a set of device functions. The service controls kernel execution via memory transfers and provides interfaces, through that clients can access the device functions. Using this novel approach, the GPU is shared by many clients at the same time, what greatly increases the flexibility without loss in performance.
Hagen Peters, Martin Koper, Norbert Luttenberger
Added 13 Feb 2011
Updated 13 Feb 2011
Type Journal
Year 2010
Where IEEECIT
Authors Hagen Peters, Martin Koper, Norbert Luttenberger
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