Intel®Xeon Phi™上高效的不规则波前传播算法。

Jeremias M Gomes, George Teodoro, Alba de Melo, Jun Kong, Tahsin Kurc, Joel H Saltz
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引用次数: 10

摘要

我们研究了在Intel®Xeon Phi™协处理器上执行不规则波前传播模式(IWPP),这是几种图像分析操作中使用的基本计算结构。在Xeon Phi处理器上有效实现IWPP是一个具有挑战性的问题,因为IWPP具有不规则性,并且在原始IWPP算法中使用原子指令来解决竞争条件。在Xeon Phi处理器上,SIMD和矢量化指令的使用对于获得高性能至关重要。但是,SIMD原子指令不受支持。因此,我们提出了一种新的IWPP算法,可以利用支持的SIMD指令集。我们还评估了一个替代存储容器(优先队列)来跟踪波前中的活动元素,以提高并行算法的效率。以形态重构和填充操作为例,对新的IWPP算法进行了评估。我们的结果表明,由于矢量化,在原始IWPP的基础上,性能提高了5.63倍。此外,与高效的CPU和GPU实现相比,新的IWPP分别实现了45.7倍和1.62倍的速度提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Efficient irregular wavefront propagation algorithms on Intel® Xeon Phi.

We investigate the execution of the Irregular Wavefront Propagation Pattern (IWPP), a fundamental computing structure used in several image analysis operations, on the Intel® Xeon Phi co-processor. An efficient implementation of IWPP on the Xeon Phi is a challenging problem because of IWPP's irregularity and the use of atomic instructions in the original IWPP algorithm to resolve race conditions. On the Xeon Phi, the use of SIMD and vectorization instructions is critical to attain high performance. However, SIMD atomic instructions are not supported. Therefore, we propose a new IWPP algorithm that can take advantage of the supported SIMD instruction set. We also evaluate an alternate storage container (priority queue) to track active elements in the wavefront in an effort to improve the parallel algorithm efficiency. The new IWPP algorithm is evaluated with Morphological Reconstruction and Imfill operations as use cases. Our results show performance improvements of up to 5.63× on top of the original IWPP due to vectorization. Moreover, the new IWPP achieves speedups of 45.7× and 1.62×, respectively, as compared to efficient CPU and GPU implementations.

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