high performance computing on graphics processing units: hgpu.org

Graphics processor units (GPUs) offer high performance and power efficiency for a large number of data-parallel applications. Previous research has shown that a GPU-based version of a neuromorphic motion estimation algorithm can achieve a x32 speedup using these devices. However, the memory consumption creates a bottleneck due to the expansive tree of signal processing operations performed. In the present contribution, an improvement in memory reduction was carried out, which limited accelerator viability usage. An evolutionary algorithm was used to find the best configuration. It supposes a trade-off solution between consumption resources, parallel efficiency, and accuracy. A multilevel parallel scheme was exploited: grain level by means of multi-GPU systems, and a finer level by data parallelism. In order to achieve a more relevant analysis, some optical flow benchmarks were used to validate this study. Satisfactory results opened the chance of building an intelligent motion estimation system that auto-adapted according to real-time, resource consumption, and accuracy requirements.