%0 Conference Proceedings
%T Using PCI Pass-Through for GPU Virtualization with CUDA
%+ Tunghai University [Taichung]
%A Yang, Chao-Tung
%A Wang, Hsien-Yi
%A Liu, Yu-Tso
%Z Part 11: Cloud & Grid
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 9th International Conference on Network and Parallel Computing (NPC)
%C Gwangju, South Korea
%Y James J. Park
%Y Albert Zomaya
%Y Sang-Soo Yeo
%Y Sartaj Sahni
%I Springer
%3 Network and Parallel Computing
%V LNCS-7513
%P 445-452
%8 2012-09-06
%D 2012
%R 10.1007/978-3-642-35606-3_53
%K CUDA
%K GPU virtualization
%K Cloud computing
%K IaaS
%K PCI passthrough
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-01551356/document
%2 https://inria.hal.science/hal-01551356/file/978-3-642-35606-3_53_Chapter.pdf
%L hal-01551356
%U https://inria.hal.science/hal-01551356
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-7513