Improving the fast back projection algorithm through massive parallelizations
Improving the fast back projection algorithm through massive parallelizations
复制标题
通过大规模并行化改进快速反投影算法
DOI：
10.1117/12.850332
复制
发表时间：
2010
影响因子：
1.3
通讯作者：
R. Carande
中科院分区：
文献类型：
--
作者：
A. Rogan;R. Carande
随着传感器技术的快速发展，处理越来越大的数据集的需求正在成为许多与持续性监视相关的实时应用的主要瓶颈，例如视频合成孔径雷达和体积合成孔径雷达成像。在许多情况下，图像保真度是最重要的，这可能会在选择适当的算法来生成所需的数据产品时产生影响。事实证明，快速反投影(FBP)算法等算法提供的性能改进对于这样的环境很有吸引力。不幸的是，即使FBP算法比传统的反投影算法快了几个数量级，它仍然不能满足上述一些实时应用的严格要求。然而，近年来通用图形处理单元(GPGPU)的出现为许多科学领域提供了针对各种应用的数量级的性能改进。FBP算法也是如此。通过将处理分布在单个视频卡上的480个处理核心上，与串行FBP算法相比，有可能实现显著的性能改进。考虑到许多PC能够容纳三到四个视频卡，使用并行方法有可能在性能上获得两个数量级以上的改进。这项技术提供了在现场处理海量数据集的能力，而不需要超级计算机，而超级计算机是迄今为止与传入数据保持同步的唯一手段。
With sensor technologies rapidly improving, the need to process increasingly larger data sets is becoming the main bottleneck in many real time applications associated with persistence surveillance such as VideoSAR and volumetric SAR imaging. In many instances, the image fidelity is of utmost importance which can have implications when choosing the appropriate algorithm to generate the desired data products. The performance improvements afforded by algorithms such as the fast back projection (FBP) algorithm prove attractive for such environments. Unfortunately, even though the FBP algorithm is magnitudes faster than a traditional back projection algorithm it is still incapable of meeting the strict requirements of some of the aforementioned real time applications. However, the emergence of general purpose graphical processing units (GPGPUs) in recent years have afforded many scientific fields orders of magnitudes improvement in performance for a large variety of applications. This is also the case for the FBP algorithm. By distributing the processing across 480 processing cores located on a single video card, it possible to achieve substantial performance improvements compared to the serial FBP algorithm. Considering that many PCs are capable of housing three to four video cards, it is possible to obtain more than two orders of magnitude improvement in performance with the parallel approach. This technology provides the ability to process enormous datasets in the field without the need of supercomputers that have to date been the only means of keeping pace with the incoming data.