Hybrid CPU-GPU Solver for Gradient Domain Processing of Massive Images
Hybrid CPU-GPU Solver for Gradient Domain Processing of Massive Images
复制标题
用于海量图像梯度域处理的混合 CPU-GPU 求解器
DOI：
10.1109/icpads.2011.69
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
P. Bremer
中科院分区：
文献类型：
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作者：
Sujin Philip;B. Summa;Valerio Pascucci;P. Bremer
梯度域处理是一种计算成本昂贵的图像处理技术。使用串行技术处理千兆或万亿像素的海量图像可能需要几个小时。为了应对这一挑战，正在开发并行算法，以使此类技术适用于可用的最大图像，并且运行时间更能为用户所接受。为此，我们的目标是当今最普遍的计算能力形式，即中小型商用硬件集群。此类集群的规模不断增加，不仅包括节点数量，还包括每个节点内以多核 CPU 和 GPU 形式提供的并行性数量。在本文中，我们提出了一种梯度域处理的混合并行实现，用于利用 MPI、线程和基于 CUDA 的 GPU 组件来无缝拼接十亿像素全景图。我们通过展示两个 GPU 集群处理两个大型数据集的结果来展示我们实现的性能和可扩展性。
Gradient domain processing is a computationally expensive image processing technique. Its use for processing massive images, giga or terapixels in size, can take several hours with serial techniques. To address this challenge, parallel algorithms are being developed to make this class of techniques applicable to the largest images available with running times that are more acceptable to the users. To this end we target the most ubiquitous form of computing power available today, which is small or medium scale clusters of commodity hardware. Such clusters are continuously increasing in scale, not only in the number of nodes, but also in the amount of parallelism available within each node in the form of multicore CPUs and GPUs. In this paper we present a hybrid parallel implementation of gradient domain processing for seamless stitching of gigapixel panoramas that utilizes MPI, threading and a CUDA based GPU component. We demonstrate the performance and scalability of our implementation by presenting results from two GPU clusters processing two large data sets.