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The use of GPUs to accelerate general-purpose scientific and engineering applications is mainstream today, but their adoption in current high-performance computing clusters is impaired primarily by acquisition costs and power consumption. Furthermore, GPU utilization is in general low, causing that the investment on GPU hardware cannot be quickly amortized. Virtualizing GPUs is an appealing strategy to deal with all these drawbacks simultaneously. By leveraging GPU virtualization, physical GPUs are install
rCUDA provides full compatibility support with CUDA. It implements all of the functions in the CUDA Runtime API and Driver API, excluding only those related with graphics interoperability. It additionally includes highly optimized TCP and low-level InfiniBand pipelined communications as well as full multi-thread and multi-node capabilities. rCUDA targets the same Linux OS distributions as CUDA does, providing also support for x86 and ARM processor architectures. Furthermore, an integration of rCUDA with th
Currently, rCUDA has been successfully tested with several applications selected from the list of “Popular GPU-accelerated Applications” by NVIDIA. In this way, in addition to show the right behavior with the NVIDIA SDK samples, rCUDA has been applied to the following applications: LAMMPS, WideLM, CUDASW++, HOOMDBlue, mCUDA-MEME, GPU-Blast, Gromacs, GAMESS, DL-POLY, and HPL. In the papers and presentations available in the documentation page you may find additional information about the performance of man
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