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Design and performance evaluation of a portable parallel library for space-time adaptive processing

机译:时空自适应处理的便携式并行库的设计和性能评估

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Space-time adaptive processing (STAP) refers to a class of methods for detecting targets using an array of sensors. Various STAP methods use similar operations on different data or in different orders. We have developed a portable, parallel library of subroutines for prototyping STAP methods. The subroutines work on the IBM SP2 and the Intel Paragon under three different operating systems and three different communication libraries, and can also be configured for other systems. We provide execution-time models for predicting the performance of each subroutine. Using the library routines, we created a parallel version of element-space pre-Doppler processing, three parallel versions of higher-order post-Doppler processing, and two versions of PRI-staggered post-Doppler processing. We implemented a fourth version of higher-order post-Doppler processing, the hybrid method, which uses a combination of fine-grain and coarse-grain parallelism to reduce execution time. The hybrid method can be used to improve performance when a large number of processors is available. Our execution time models generally predict the best method and predict execution times to within 10 percent or better for large test cases.
机译:时空自适应处理(STAP)是指使用传感器阵列检测目标的一类方法。各种STAP方法对不同的数据或不同的顺序使用相似的操作。我们已经开发了可移植的,并行的子例程库,用于原型STAP方法。子例程在三种不同的操作系统和三种不同的通信库下在IBM SP2和Intel Paragon上工作,也可以为其他系统配置。我们提供执行时间模型来预测每个子例程的性能。使用库例程,我们创建了元素空间前多普勒处理的并行版本,高阶后多普勒处理的三个并行版本以及PRI交错后多普勒处理的两个版本。我们实现了高阶后多普勒处理的第四个版本,即混合方法,该方法结合了细粒度和粗粒度并行性来减少执行时间。当有大量处理器可用时,可以使用混合方法来提高性能。我们的执行时间模型通常可以预测最佳方法,并且对于大型测试用例,可以预测执行时间在10%或更短的时间内。

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