vCUDA: GPU accelerated high performance computing in virtual machines

By Lin Shi, Hao Chen, Jianhua Sun, Kenli Li. 2009 IEEE International Symposium on Parallel & Distributed Processing, 2009

367 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/042456c27f20d7fcae0b4a99675b119c5f87186f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Christopher J. Rossbach
2. Peifeng Yu
3. Mosharaf Chowdhury
4. Raouf Boutaba

In Information Systems

1. Christopher J. Rossbach
2. Peifeng Yu
3. Mosharaf Chowdhury
4. Amogh Akshintala
5. Arthur Michener Peters
6. Hangchen Yu

Citations in the corpus

• AvA: Accelerated Virtualization of Accelerators (2020, methodology): “RPC frameworks [27, 71, 93, 104] provide an interface description language (IDL) and tools to easily implement those interfaces.”
• Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications (2019, methodology): “Examples are gVirtuS [25], GViM [27], vCUDA [44], rCUDA [24], and work from Ravi et al.”
• rCUDA: Reducing the number of GPU-based accelerators in high performance clusters (2010, methodology): “We can divide virtualization into two major categories: front-end (application facing) virtualization and back-end (hardware facing) virtualization [5].”