high performance computing on graphics processing units: hgpu.org

In this research, we target at the investigation of a dynamic energy-aware management framework on the execution of independent workloads (e.g., bag-of-tasks) in hybrid CPU-GPU PARA-computing platforms, aiming at optimizing the execution of workloads in appropriate computing resources concurrently while balancing the use of solely virtual or physical resources or hybridly selected resources, to achieve the best performance in executing application workloads and minimizing the energy associated with computation selected. Experimental results show that the proposed strategy can contribute to improve performance by introducing optimization techniques, such as workload consolidation and dynamic scheduling. We observed that workload consolidation can potentially improve performance, depending on characteristics of the workload. Also, the workload scheduling results present the importance of resource management by revealing the performance gap among different execution schedules for shared computing resources.