Fine-Grained Parallel Optimization of Large-Scale Data for PMVS Algorithm

We address the problem of fine-grained parallel optimization of large-scale data. Patch-based multi-view stereo (PMVS) algorithm has been widely applied to digital city and other fields because of its good three-dimensional reconstruction effect, however, its large-scale computing algorithm has a low execution efficiency. Therefore, to address the limitation, this paper proposes a fine-grained parallel optimization method, including task allocation and load-balancing; strategies of main system memory and GPU memory; the optimization of communication. We perform CPU multi-threading operation using the pthreads function library to take full advantage of the computing power of multi-core CPUs. And for GPUs, we utilize the CUDA framework while optimizing thread organization and memory access. Besides that, we propose the idea of adapting memory pool model and pipelining model to improve bandwidth availability ratio. The memory pool model reduces the impact of data resources transferring on the bus for CPUs_GPUs while waiting for resources; the pipelining model hides communication time for CPU to read data from memory. At the same time, this paper utilizes the Harris-DOG feature extraction of PMVS algorithm of sequences of images as the example to verify our optimization strategies. The experiments demonstrate that the multi-threading CPU-based strategy can achieve 4 times speed-up ratio, the highest ratio that parallel CUDA-based strategy can achieve is 34 times, and our strategy can improve the performance 30% on the basis of the parallel CUDA-based strategy. In the future, our optimization strategy can be applied to quick computing resource scheduling in big data processing of other domains.