On construction of a virtual GPU cluster with InfiniBand and 10 Gb Ethernet virtualization View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

Chao-Tung Yang, Shuo-Tsung Chen, Yu-Sheng Lo, Endah Kristiani, Yu-Wei Chan

ABSTRACT

Due to increasing requirement of computing capability, the graphics processor unit and CUDA are used to build a higher-performance computing environment. The graphics processing unit (GPU) is necessary for building the high-performance computing environment because of its high computing performance. CUDA, a parallel computing platform and programming model created by NVIDIA, utilizes some parallel construction concepts to upgrade performance, such as hierarchical thread blocks, shared memory, and barrier synchronization. The GPU and CUDA are also used in cloud computing, because they can provide high-performance computing capabilities. Virtualization plays a very important part in the cloud architecture. Virtual machines built with the NVIDIA graphics card can use CUDA to provide virtual machine computing capability. This makes virtual machine have not only virtual CPUs, but also physical graphics processors to do computations. InfiniBand is faster than Ethernet as the transmission medium. In the past, virtual machine cannot use direct InfiniBand, but now, many virtualization platforms can do it, that brings transmission speed improvement between virtual machines. In this work, we use many graphics processing units to build a high-performance computing cloud cluster. Then, we compare performance of using direct InfiniBand with that of using indirect InfiniBand transmission performance by running High Performance Linpack benchmark. In this work, we use a well-known virtualization platform, i.e., VMware to do experiments in this paper. And then, we use GPU passthrough and InfiniBand virtual and 10 Gb Ethernet passthrough to improve performance of the virtual cluster. More... »

PAGES

1-22

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11227-018-2484-5

DOI

http://dx.doi.org/10.1007/s11227-018-2484-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105705825


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