The mathematical acceleration delivered by many-core General Purpose Graphics Processing Units (GPGPUs) offers significant performance advantages for many classes of numerically intensive applications. Parallel computing tools such as NVIDIA’s CUDA and the openCL framework have made harnessing the power of these technologies much more accessible to developers, resulting in the increasing deployment of hybrid GPGPU-based systems and introducing significant challenges for administrators and workload management systems.