加速混合集群的SIFT

S. Warn, A. Apon, J. Cothren
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引用次数: 2

摘要

我们描述了一种并行化SIFT和其他基于尺度空间的特征变换算法的方法。通过以一种新颖的方式对工作负载进行分区,我们的方法可以利用所有形式的并行性:线程编程的共享内存并行性、集群编程的分布式内存并行性以及基于gpu的加速。还描述了这种方法的一种实现,称为SOHC或混合簇上的SIFT,它可以利用混合簇来加速将任意大的图像转换为特征集。SOHC具有可移植性和可扩展性:它可以运行在各种系统上,从没有任何GPU硬件的桌面到多GPU节点的集群,唯一的区别是完成提取的时间。它是唯一能够在地理空间应用中经常遇到的十亿像素大小的图像上直接操作的SIFT实现(即不在tile边界上删除特征)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerating SIFT on hybrid clusters
We describe an approach to parallelizing SIFT and other scale-space-based feature transformation algorithms. By partitioning the workload in a novel fashion, our approach can take advantage of all forms of parallelism: the shared-memory parallelism of threaded programming, the distributed-memory approach of cluster programming, and GPU-based acceleration. Also described is an implementation of this approach called SOHC, or SIFT on hybrid clusters, which can take advantage of hybrid clusters to accelerate the transformation of arbitrarily large images into sets of features. SOHC is both portable and scalable: it can run on systems ranging from a desktop without any GPU hardware, to a cluster of multi-GPU nodes, with the only difference being time to complete the extraction. It is the only implementation of SIFT capable of operating directly (i.e. without dropping features at tile boundaries) on gigapixel-sized images often encountered in geospatial applications.
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