首页> 外文会议>Frontiers of Massively Parallel Computation, 1990. Proceedings., 3rd Symposium on the >Porting an iterative parallel region growing algorithm from the MPP to the MasPar MP-1
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Porting an iterative parallel region growing algorithm from the MPP to the MasPar MP-1

机译:将迭代并行区域增长算法从MPP移植到MasPar MP-1

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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.
机译:描述了在NASA Goddard的大规模并行处理器(MPP)上开发和实现的迭代并行区域增长(IPRG)算法。涉及将IPRG算法从MPP移植到MasPar MP-1的经验。移植非常容易和直接,特别是在使用Dorband虚拟化软件时。作者在短短两周内就完成了由1879行MPL代码组成的移植工作。两种实现之间的主要区别在于,虚拟并行数组的循环必须显式完成,并且必须是MPP Pascal实现中的最外层循环(以提高效率),而相同的循环在MPL实现中是隐式完成的,并且可以在最里面的循环中完成。在对7波段Landsat专题映射器图像数据集的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%​​。通过迭代14,MasPar MP-1比MPP快33%,随后的迭代表明,相对于MPP,MasPar MP-1的加速甚至更大。

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