high performance computing on graphics processing units: hgpu.org

GPU provides powerful computational capabilities and huge potential optimization possibility of efficient. As a result, the CPU-GPU heterogeneous architecture is still the hot zone of the high performance computation. However, the energy consuming is still the bottle neck of the entire the system, when the system and its corresponding framework need massive scale calculation. Most of the existing study is focus on how to lower the GPU power requirement. However, they did not considered CPU and GPU as an entire architecture. This thesis is based on the GreenGPU heterogeneous architecture. Because of the new generation of platform, one of the assumptions is that the operation system and its correspondence driver will adjust the DVFS to its best optimizing point. I implement a workload division algorithm using Tesla CUDA GPUs and AMD CPUs to balance the time difference caused by the workload. The real physical test-bed results show the new workload division algorithm can provide at least 5X accuracy than previous algorithm without extra energy cost in the workload division procedure. This more accurate workload division algorithm could benefits the overall system energy consumption especially when the workload is huge.