将迭代并行区域增长算法从MPP移植到MasPar MP-1

J. Tilton
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引用次数: 3

摘要

介绍了在NASA戈达德大型并行处理器(MPP)上开发和实现的迭代并行区域生长(IPRG)算法。介绍了将IPRG算法从MPP移植到MasPar MP-1的经验。移植非常简单和直接,特别是当使用Dorband虚拟化软件时。所讨论的移植由1879行MPL代码组成,作者仅在两周内就完成了。这两种实现之间的主要区别在于,在MPP Pascal实现中,虚拟并行数组的循环必须显式地完成,并且必须是最外层的循环(为了效率),而在MPL实现中,相同的循环是隐式地完成的,并且可以在最内层的循环中完成。在对七波段Landsat主题绘图仪图像数据集的256*256像素部分进行的性能测试中,较小的MasPar MP-1计算机具有与MPP大致相同或更好的性能。在最初的迭代中,当区域仍然很小时,MPP比MasPar MP-1快25%左右。在第14次迭代中,MasPar MP-1比MPP快33%,并且在随后的迭代中,MasPar MP-1比MPP的加速速度将会更大。
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Porting an iterative parallel region growing algorithm from the MPP to the MasPar MP-1
An iterative parallel region growing (IPRG) algorithm, developed and implemented on the massively parallel processor (MPP) at NASA Goddard, is described. The experience of porting the IPRG algorithm from the MPP to the MasPar MP-1 is related. Porting was very easy and straightforward, especially when the Dorband virtualization software was used. The porting discussed, consisting of 1879 lines of MPL code, was accomplished in just two weeks by the author. The major difference between the two implementations is that the looping over virtual parallel arrays had to be done explicitly and had to be the outermost loop (for efficiency) in the MPP Pascal implementation, whereas the same looping was done implicitly in the MPL implementation and could be done in the innermost loop. In a performance test on a 256*256 pixel section of a seven-band Landsat thematic mapper image data set, the smaller MasPar MP-1 computer had roughly the same or better performance as the MPP. In the initial iterations, when the regions were still very small, the MPP was about 25% faster than the MasPar MP-1. By iteration 14, the MasPar MP-1 was 33% faster than the MPP, and for ensuing iterations indications are that the MasPar MP-1 speedup versus the MPP will be even larger.<>
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