The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Deploying Web-Based Visual Exploration Tools on the Grid
IEEE Computer Graphics and Applications
Resource Management through Multilateral Matchmaking
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Entropia: architecture and performance of an enterprise desktop grid system
Journal of Parallel and Distributed Computing - Special issue on computational grids
GPU Gems: Programming Techniques, Tips and Tricks for Real-Time Graphics
GPU Gems: Programming Techniques, Tips and Tricks for Real-Time Graphics
BOINC: A System for Public-Resource Computing and Storage
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
GPU Cluster for High Performance Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation (Gpu Gems)
IEEE Micro
Performance study of LU decomposition on the programmable GPU
HiPC'05 Proceedings of the 12th international conference on High Performance Computing
A GPGPU approach for accelerating 2-d/3-d rigid registration of medical images
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
Cooperative Multitasking for GPU-Accelerated Grid Systems
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
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Modern programmable graphics processing units (GPUs) provide increasingly higher performance, motivating us to perform general-purpose computation on the GPU (GPGPU) beyond graphics applications. In this paper, we address the problem of resource selection in the GPU grid. The GPU grid here consists of desktop computers at home and the office, utilizing idle GPUs and CPUs as computational engines for compute-intensive applications. Our method tackles this challenging problem (1) by defining idle resources and (2) by developing a resource selection method based on a screensaver approach with low-overhead sensors. The sensors detect idle GPUs by checking video random access memory (VRAM) usage and CPU usage on each computer. Detected resources are then selected according to a matchmaking framework and benchmark results obtained when the screensaver is installed on the machines. The experimental results show that our method achieves a low overhead of at most 262 ms, minimizing interference to resource owners with at most 10% performance drop.