MM4数值模式在曙光1000机上的并行化

§１．引言 近年来,中尺度数值模式已为国内气象界熟悉,随着应用中的改进,模式物理过程的复杂化以及对分辩率的提高,其计算量显著增长,并行计算尤为重要．目前,安徽省引进一台国产曙光－１０００并行计算机,为我们提供了良好的条件． 为实现该模式的并行化,对ＭＭ４串行程序彻头彻尾的分析和改造是至关重要的．经过试验,我们在数据结构和程序结构两方面,取得了突破性进展．其结果表明并行化计算与串行程序的执行完全一致,使加速比由初期的 １： １．５提高到 １： ６．７２（１６ ＣＰＵs）初步达到满意程度, 此项工作是在中国…     

朴素并行LDA

并行潜在狄利克雷分配(LDA)主题模型在计算与通信两方面的时间消耗较大,导致训练模型的时间过长,因而无法被广泛应用.提出朴素并行LDA算法,针对计算和通信分别提出改进方法.一方面通过加入单词影响因子以及设置阈值的方法来降低文本训练的粒度,另一方面通过降低通信频率来减少通信时间.实验结果表明,优化后的并行LDA在保证精度损失为1％的前提下,将训练速度提高了36％,有效提高了并行的加速比.     

Spark平台下聚类算法的性能比较

通过实验,从运行时间、加速比、可扩展性和规模增长性4个方面比较了 Spark平台中3种典型的聚类算法即K-means聚类算法、二分K-means聚类算法和高斯混合聚类算法 的性能。实验结果表明:1)随着节点个数的增加,3种算法对百兆以上规模数据集聚类的运行时间明显减少;2)当数据集规模大于500MB时,3种算法的加速比均有明显提高,且随着节点个数的增加,加速比近似于线性增长;3)3种算法的可扩展性随着节点个数的增加而降低,当数据集规模大于500MB时,相对于K-means和高斯混合算法,二分K-means算法的可扩展性最差;4)当数据集规模大于100MB时,高斯混合算法的规模增长性远高于K-means和二分K-means算法。     

Architecture-based design and optimization of genetic algorithms on multi- and many-core systems

A Genetic Algorithm (GA) is a heuristic to find exact or approximate solutions to optimization and search problems within an acceptable time. We discuss GAs from an architectural perspective, offering a general analysis of performance of GAs on multi-core CPUs and on many-core GPUs. Based on the widely used Parallel GA (PGA) schemes, we propose the best one for each architecture. More specifically, the Asynchronous Island scheme, Island/Master–Slave Hierarchy PGA and Island/Cellular Hierarchy PGA are the best for multi-core, multi-socket multi-core and many-core architectures, respectively. Optimization approaches and rules based on a deep understanding of multi- and many-core architectures are also analyzed and proposed. Finally, the comparison of GA performance on multi-core and many-core architectures are discussed. Three real GA problems are used as benchmarks to evaluate our analysis and findings.There are three extra contributions compared to previous work. Firstly, our findings based on deeply analyzing architectures can be applied to all GA problems, even for other parallel computing, not for a particular GA problem. Secondly, the performance of GAs in our work not only concerns execution speed, also the solution quality has not been considered seriously enough. Thirdly, we propose the theoretical performance and optimization models of PGA on multi-core and many-core architectures, finding a more practical result of the performance comparison of the GA on these architectures, so that the speedup presented in this work is more reasonable and is a better guide to practical decisions.     

基于网络并行计算的分布式PGA结构优化

网络并行计算是当今并行计算发展的新方向。在网络并行环境下探讨了并行遗传算法进行结构优化设计及其算法的实现方法。并在四台 PC 机组成的网络平台上,进行了钢屋架结构优化设计的数值测试。计算结果表明,设计的并行算法在网络并行计算环境中具有较高的加速比和效率,同时验证了并行遗传算法用于结构优化是可行的和有效的。     

Tie-line constrained distributed state estimation

This paper presents an implementable distributed state estimation method for online analysis of power systems having multiple, geographically separated areas. Distributed state estimation apart from giving a faster solution, also improves the condition number of the resultant gain matrices. The method proposed here uses the conventional WLS estimator (Gauss-Newton method) with equality constraints forcing the tie line flows as calculated by the adjacent areas to be equal. Based on the topology of the network, the system is partitioned into multiple areas and a processor is assigned to each of these areas for solving the local state estimation problem. Simulations carried out on the IEEE 14, 30 and 118 bus systems show good convergence properties and improvement on the condition number of the gain matrices when compared with centralized algorithms.     

GPS软件接收机实时化设计与实现

提出了基于软相关的12通道GPS软件接收机实时化的改进方法,主要针对耗时较多的信号检测模块和相关器进行算法优化和程序优化,并给出了改进前后的实际运行结果和性能分析,表明了所提出的优化方法是行之有效的。采用这些方法改进之后,实现了12通道实时接收的GPS软件接收机。     

加速比的局限性分析

并行处理技术是获得高性能计算机的有效而必由的手段，因而成为新一代的结构特征，在并行处理的性能评价中，被广泛采用的一个性能特征就是加速比。加速比是否能够合理地反映并行处理系统的性能呢？我们在本文中分析了加速比的局限性。     

基于A*算法的2种任务-处理器分配改进算法

针对分布式计算中的任务-处理器分配问题,提出了2种基于A*算法的改进算法。通过对A*算法原理的分析,得到将A*算法应用于任务分配问题的搜索树模型,和通过将搜索树在处理单元之间尽可能均匀地进行划分和通过避免不必要的节点扩展来得到最优解;实验结果表明,提出的2种改进算法,相比于传统的A*算法,不仅能够通过减少搜索空间节省内存和减少任务执行时间,而且能够通过并行分配来提高算法执行时的加速性。     

基于对角划分的矩阵乘并行算法

提出了一种新的基于对角划分的矩阵乘并行算法，它在以往行列划分策略的基础上，采用基于对角划分的策略。数值试验表明该算法具有较高的加速比和并行效率。