distributed memory中文是什么意思

• 分布存储器
• 分散式记忆体

• distribute    vt. 1.分配，分给，分发，配给；〔古语〕实施，颁布。 ...
• memory    n. 1.记忆；记忆力；【自动化】存储器；信息存储方式； ...
• distributed logic memory    分布逻辑存储器; 分散式逻辑记忆体
• distributed memory machine    分布存储器机
• distributed memory network    分配存储网络,分布式存储网
• distributed memory parallel systems    分布存储器并行系统
• distributed memory system    分散记忆系统
• distributed processing memory    分布处理存储器; 分布式处理存储器
• distributed processsing memory    分布处理存储器
• distributed shared memory    分布式共享存储处理机; 分布式共享存储器
• dm distributed memory    分布式存储器
• dmm distributed memory multicomputer    分布式存储的多计算机
• dom distributed object memory    分布式对象存储器
• dsm distributed shared memory    分布式共享存储器
• vdom versioned distributed object memory    变型的分布式对象存储器
• distributed    分布的; 分布式的; 分发; 分散对齐; 分散式的; 已分配的，已分摊的
• in memory of    纪念, 追念; 纪念，作为…的纪念; 以纪念...，为了纪念..; 以纪念……
• in-memory    追忆
• memory    n. 1.记忆；记忆力；【自动化】存储器；信息存储方式；存储量。 2.回忆。 3.纪念。 4.死后的名声，遗芳。 5.追想得起的年限[范围]。 artificial memory 记忆法。 retentive memory 良好的记忆力。 a translation memory (电子计算机的)译码存储器。 Keep your memory active. 好好记住，不要忘记。 It is but a memory. 那不过是往事而已。 bear [have, keep] in memory 记着，没有忘记。 beyond [within] the memory of man [men] 在有史以前[以来]。 cherish the memory of (sb.) 怀念(某人)。 come to one's memory 想起，忆及，苏醒。 commit to memory 记住。 from memory 凭记忆。 have a good [bad, poor, short] memory 记性好[坏]。 have no memory of 完全忘记。 If my memory serves me. 如果我的记性不错。 in memory of 纪念…。 of blessed [famous, happy, glorious] memory 故〔加在已死王公名上的颂词〕 (King Charles of blessed memory 已故查理王)。 slip sb.'s memory 被某人一时忘记。 to the best of one's memory 就记忆所及。 to the memory of 献给…〔著者书前纪念性题词〕。 within living memory 现在还被人记着。
• memory (    开启除错
• no memory    无存储器
• of memory    记忆
• on memory of    为纪念……
• computing, distributed    分布式运算
• decentralized, distributed    分散式

• Recently , clusters with distributed memory are popular for parallel computing systems , and mpi is the actual standard of message passing programming . therefore , the application of clusters based on mpi to computational electromagnetics is studied in this dissertation . the methods are mom based on the integral equation method in conjunction with the preconditioning technique to speed up the iterative solution and a matrix order reducing method , namely the characteristic basis function method ( cbfm )
  目前,分布式内存的集群系统是并行计算系统的主流系统, mpi是消息传递编程实际上的标准,本文即研究了基于mpi的集群技术在计算电磁学中的应用,采用的方法主要是基于积分方程的矩量法,并结合了加速迭代的预条件技术以及一种矩阵降阶方法? ? cbf方法。
• Ii ) we organize the parallel computing of ' the third generation ocean general circulation model from the chinese institute of atmosphere physics ( lasg / iap ) , and we also apply several parallel optimization strategies including multi - overlapping - boundary . the parallelism is implemented both on distributed memory and shared memory parallel computers , based on the spitting of latitude zones . this parallel ocean general circulation model is the first one in china
  11 )发展了国内第一个高效并行海洋环流模式:深入研究了中国科学院大气物理所第三代海洋环流模式，分析了其串行计算特点和并行可扩展性，指出了其计算方法上存在的对并行计算的某些限制，对该模式组织了有效并行计算，并成功应用了包括多重叠边界在内的若干并行优化方法，在国产小型mpp系统和sgioriginzoo 。
• Nowadays , the most popular parallel computer architectures in the world are smp , mpp , cow and smp cluster , etc . smp cluster , which combines the characteristics of both shared memory and distributed memory architectures , is not only powerful in computing capabilities but also excellent in scalabilities
  目前国内外流行的并行计算机体系结构主要有smp 、 mpp 、 cow和smp集群等,其中smp集群体系结构结合了共享存储和分布式存储两种体系结构的特点,同时兼顾了计算性能和可扩展性两个方面。
• For the process of simulation , the databases are supposed to be provided with two sides : real - time access for the distributed memory data and intelligence simulation technology . because each site in this simulation database emulates the different position and function structure character , and comes down to multi - platform , multi - type data , the great amount of system parameters , models , model structure parameters and rules in simulation must be managed by distributed database for administration , rapid information query and dynamic storage modification
  由于该仿真系统的每一个仿真节点分别模拟了分布在不同位置、不同功能结构特征的仿真对象，而且，涉及到多平台、不同形式和种类的数据，因此仿真过程中大量的系统参数、模型、模型结构参数和规则，都必须使用分布式数据库技术来管理、进行高速的数据检索和动态存储修改，来支持仿真环境的动态维护与仿真环境之间的相互作用。
• We analysed the traditional automatic parallelization technology , including dependency analysis theory , program transformation technology , parallel scheme and the optimization of related synchronization and communication etc , which are the theoretical basis of the whole article . cfd computing features , especially the features of explicit difference computing , have also been further ananlysed . we also summarized drawbacks of traditional automatic parallelization technology used in cfd : small parallel granularity , difficulty in attaining global identical data partition , and difficulty in attaining high parallel efficiency on distributed memory system
  本文讨论、分析、总结了通用的自动并行化技术：相关性分析理论、程序变换技术、并行模式以及同步通信与优化问题等等，它们是本文研究工作的理论基础；针对研究对象，深入分析了cfd计算的特点，特别是显式差分计算的特点；并归纳出传统的自动并行化技术在cfd应用中存在的问题：并行粒度小、难以获得全局统一的数据划分方式，对于分布存储结构的并行机难以获得高效率。
• The kanerva ' s sparse distributed memory ( sdm ) tackles the problem of training large data patterns and extendes the storage mode of existing computer . but it ' s address array produced randomly ca n ' t reveal the distribution of patterns and it has ' t the ability of function approximation for its learning rule
  Kanerva的稀疏分布存储( sdm )模型解决了大维数样本的训练问题，推广了现有计算机的存储方式。但其地址矩阵的随机预置方式不能反映样本的分布，并且sdm的学习方式使之不能用于函数逼近及时间序列预测问题。
• Much more work on the fields had been taken and some fruits had been acquired . after analyzing existing sequence processing methods and software , in order to improve the parallel scalability and speed , some novel parallel algorithms for sequence processing on different computing environment , such as shared memory architecture , distributed memory architecture and grid computing architecture , are presented in this thesis
  本文在深入分析当前生物序列处理研究现状的基础上,以提高序列处理性能为目标,基于共享存储环境、分布存储环境以及网格计算环境等不同计算环境,使用不同并行处理技术,解决大规模基因组序列处理中所遇到的实际困难,如可扩展性差、计算速度慢等。
• The schemes are numerically tested on a variety of id and 2d problems ; solutions obtained in computation the results of the numerical solutions for euler equation and shallow water equations by gauss schemes presented in the paper under distributed memory parallel multiprocessor are presented . the efficient of parallel computation are satisfied
  利用曙光- 1000型分布存储大规模并行机，对我们在交错网格下所构造求解euler方程和浅水方程的高分辨差分格式进行了并行实现，并对并行效率进行了分析，计算结果和并行效率都比较令人满意。