Using PCI Pass-Through for GPU Virtualization with CUDA

Abstract : Nowadays, NVIDIA’s CUDA is a general purpose scalable parallel programming model for writing highly parallel applications. It provides several key abstractions – a hierarchy of thread blocks, shared memory, and barrier synchronization. This model has proven quite successful at programming multithreaded many core GPUs and scales transparently to hundreds of cores: scientists throughout industry and academia are already using CUDA to achieve dramatic speedups on production and research codes. GPU-base clusters are likely to play an important role in future cloud data centers, because some compute-intensive applications may require both CPUs and GPUs. The goal of this paper is to develop a VM execution mechanism that could run these applications inside VMs and allow them to effectively leverage GPUs in such a way that different VMs can share GPUs without interfering with one another.
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Communication dans un congrès
James J. Park; Albert Zomaya; Sang-Soo Yeo; Sartaj Sahni. 9th International Conference on Network and Parallel Computing (NPC), Sep 2012, Gwangju, South Korea. Springer, Lecture Notes in Computer Science, LNCS-7513, pp.445-452, 2012, Network and Parallel Computing. 〈10.1007/978-3-642-35606-3_53〉
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Chao-Tung Yang, Hsien-Yi Wang, Yu-Tso Liu. Using PCI Pass-Through for GPU Virtualization with CUDA. James J. Park; Albert Zomaya; Sang-Soo Yeo; Sartaj Sahni. 9th International Conference on Network and Parallel Computing (NPC), Sep 2012, Gwangju, South Korea. Springer, Lecture Notes in Computer Science, LNCS-7513, pp.445-452, 2012, Network and Parallel Computing. 〈10.1007/978-3-642-35606-3_53〉. 〈hal-01551356〉

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