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Recent advances in hardware and software virtualisation capabilities have made it possible to customise hardware and software environments for a huge variety of applications. Grid infrastructures have capitalised on many of these advances, for example, through the use of grid-enabled virtual machines which provide well-known and trusted user services and environments. In particular, the StratusLab cloud distribution has greatly fac ilitated the creation of hybrid cloud/grid infrastructures. At the same time, we have seen a rapid increase in the utilisation of General Purpose Graphical Processing Units (GPGPUs) to handle massively data parallel workloads. There are significant technical difficulties in integrating GPGPUs as first-class grid-resources. Furthermore, the use of full GPGPU hardware pass-through to virtual machines, which could be used to overcome some of these challenges, has only had limited success. An alternative networkbased GPGPU virtualisation method has been shown to be more successful. We review these difficulties, and propose how both StratusLa b and network-based GPGPU virtualisation, such as rCUDA and Mosix VCL, may be used to ameliorate some of these issues.

Supporting grid-enabled GPU workloads using rCUDA and StratusLab