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2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing最新文献

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Fault-Tolerant Global Load Balancing in X10 X10中的容错全局负载均衡
Marco Bungart, Claudia Fohry, Jonas Posner
Scalability postulates fault tolerance to be effective. We consider a user-level fault tolerance technique to cope with permanent node failures. It is supported by X10, one of the major Partitioned Global Address Space (PGAS) languages. In Resilient X10, an exception is thrown when a place (node) fails. This paper investigates task pools, which are often used by irregular applications to balance their load. We consider global load balancing with one worker per place. Each worker maintains a private task pool and supports cooperative work stealing. Tasks may generate new tasks dynamically, are free of side-effects, and their results are combined by reduction. Our first contribution is a task pool algorithm that can handle permanent place failures. It is based on snapshots that are regularly written to other workers and are updated in the event of stealing. Second, we implemented the algorithm in the Global Load Balancing framework GLB, which is part of the standard library of X10. We ran experiments with the Unbalanced Tree Search (UTS) and Between ness Centrality (BC) benchmarks. With 64 places on 4 nodes, for instance, we observed an overhead of about 4% for using fault-tolerant GLB instead of GLB. The protocol overhead for a place failure was neglectable.
可伸缩性假定容错是有效的。我们考虑了一种用户级容错技术来处理永久性节点故障。它由主要的分区全局地址空间(PGAS)语言之一X10支持。在Resilient X10中,当一个位置(节点)发生故障时抛出异常。本文研究了不规则应用程序经常使用的任务池来平衡其负载。我们考虑全局负载平衡,每个位置有一个工作人员。每个工人维护一个私有任务池,并支持协同工作窃取。任务可以动态地生成新的任务,没有副作用,并且它们的结果是通过缩减组合起来的。我们的第一个贡献是一个可以处理永久位置故障的任务池算法。它基于定期写入其他worker的快照,并在发生窃取时进行更新。其次,我们在全局负载平衡框架GLB中实现了该算法,该框架是X10标准库的一部分。我们使用不平衡树搜索(UTS)和间中心性(BC)基准进行了实验。例如,在4个节点上有64个位置时,我们观察到使用容错GLB而不是GLB的开销约为4%。位置故障的协议开销可以忽略不计。
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引用次数: 2
Genetic Improvement of Programs 程序的遗传改良
W. Langdon
Genetic programming can optimise software, including: evolving test benchmarks, generating hyper-heuristics by searching meta-heuristics, generating communication protocols, composing telephony systems and web services, generating improved hashing and C++ heap managers, redundant programming and even automatic bug fixing. Particularly in embedded real-time or mobile systems, there may be many ways to trade off expenses (such as time, memory, energy, power consumption) vs. Functionality. Human programmers cannot try them all. Also the best multi-objective Pareto trade off may change with time, underlying hardware and network connection or user behaviour. It may be GP can automatically suggest different trade offs for each new market. Recent results include substantial speed up by evolving a new version of a program customised for a special case.
遗传编程可以优化软件,包括:进化测试基准,通过搜索元启发式生成超启发式,生成通信协议,组合电话系统和web服务,生成改进的散列和c++堆管理器,冗余编程,甚至自动修复错误。特别是在嵌入式实时或移动系统中,可能有许多方法可以权衡费用(如时间、内存、能量、功耗)与功能。人类程序员无法全部尝试。此外,最佳的多目标Pareto权衡可能会随着时间、底层硬件、网络连接或用户行为而变化。GP可能会自动为每个新市场提出不同的折衷方案。最近的结果包括通过开发针对特殊情况定制的程序的新版本来大幅提高速度。
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引用次数: 34
Performance Improvements for the "Linear Nonadiabatic Nonradial Waves" Pulsational Model “线性非绝热非径向波”脉动模型的性能改进
Mihai Ovidiu Tirsa, E. Slusanschi, M. Suran
Aster seismology is an emerging branch of astrophysics that studies the interior and global parameters of pulsating stars, based on their natural oscillation frequency. This work proposes different methods for increasing the performance of LNAWENR (Linear Non Adiabatic Non Radial WavEs), a computational intensive pulsational model that studies stars based on their seismic properties. As part of the aster seismological package ROMOSC, LNAWENR is one of the several non-adiabatic models in use now by the international astrophysics and space science community. The model was implemented to study data from NASA's CoRoT mission. The improved version aims to be used for NASA's KEPLER mission. In this context, we employed several serial and parallel code optimizations. The programming frameworks used for the parallel optimizations are Open MP and Open CL.
Aster地震学是天体物理学的一个新兴分支,它研究脉动恒星的内部和全局参数,基于它们的自然振荡频率。这项工作提出了不同的方法来提高LNAWENR(线性非绝热非径向波)的性能,LNAWENR是一个计算密集型的脉动模型,基于它们的地震特性来研究恒星。LNAWENR是国际天体物理学和空间科学界目前使用的几种非绝热模型之一,是ROMOSC的一部分。该模型用于研究NASA CoRoT任务的数据。改进后的版本旨在用于美国宇航局的开普勒任务。在这种情况下,我们采用了几个串行和并行代码优化。用于并行优化的编程框架是Open MP和Open CL。
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引用次数: 0
Using Domain Specific Hierarchical Good Practice for Ranking Service Compositions 使用特定领域的分层良好实践对服务组合进行排序
A. Marginean, I. A. Letia, S. Zaporojan
We propose a method for ranking the service compositions according to the good practice of each domain. Knowledge about good practice is modeled in a hierarchical manner inspired from Hierarchical Task Networks. In describing the good practice knowledge we give a model for HTN in N3 notation and we enhanced it with an importance value. Each candidate service composition is checked against good practice in a model checking style. A candidate composition is a sequence of services. The candidate composition is compared to the constraints defined in good practice and is considered good if for each simple task the most important constraints are fulfilled.
我们提出了一种根据每个领域的良好实践对服务组合进行排序的方法。关于良好实践的知识以受分层任务网络启发的分层方式建模。在描述良好实践知识时,我们给出了一个N3表示法的HTN模型,并对其进行了重要值的增强。每个候选服务组合都按照模型检查风格的良好实践进行检查。候选组合是一系列服务。将候选组合与良好实践中定义的约束进行比较,如果每个简单任务都满足了最重要的约束,则认为候选组合是良好的。
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引用次数: 1
A Practical Approach on Cleaning-Up Large Data Sets 大型数据集清理的实用方法
Marius Barat, Dumitru-Bogdan Prelipcean, Dragos Gavrilut
In this paper we propose a noise detection system based on similarities between instances. Having a data set with instances that belongs to multiple classes, a noise instance denotes a wrongly classified record. The similarity between different labeled instances is determined computing distances between them using several metrics among the standard ones. In order to ensure that this approach is computational feasible for very large data sets, we compute distances between pairs of different labels instances that have a certain degree of similarity. This speed-up is possible through a new clustering method called BDT Clustering presented within this paper, which is based on a supervised learning algorithm.
本文提出了一种基于实例相似性的噪声检测系统。如果数据集的实例属于多个类,则噪声实例表示错误分类的记录。不同标记实例之间的相似性是使用标准度量中的几个度量来确定它们之间的计算距离。为了确保这种方法在非常大的数据集上是计算可行的,我们计算具有一定程度相似性的不同标签实例对之间的距离。本文提出了一种新的聚类方法,称为BDT聚类,该方法基于监督学习算法,可以实现这种加速。
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引用次数: 0
Implementing Reasoning Modules in Implicit Induction Theorem Provers 在隐式归纳定理证明中实现推理模块
Sorin Stratulat
We detail the integration in SPIKE, an implicit induction theorem prover, of two reasoning modules operating over naturals combined with interpreted symbols. The first integration schema is à la Boyer-Moore, based on the combination of a congruence closure procedure with a decision procedure for linear arithmetic over rationals/reals. The second follows a 'black-box' approach and is based on external SMT solvers. It is shown that the two extensions significantly increase the power of SPIKE, their performances are compared when proving a non-trivial application.
我们详细介绍了在SPIKE(一个隐式归纳法定理证明器)中,两个推理模块在结合了解释符号的自然上运行的集成。第一种积分模式是基于同余闭包过程与有理数/实数上线性算法的决策过程的结合的 la Boyer-Moore。第二种方法采用“黑盒”方法,并基于外部SMT求解器。结果表明,这两种扩展都显著提高了SPIKE的性能,并在一个非平凡应用中对其性能进行了比较。
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引用次数: 4
Opinion Influence Analysis in Online Forum Threads 网络论坛话题的意见影响分析
Dumitru-Clementin Cercel, Stefan Trausan-Matu
This paper treats the phenomenon of opinion influence in online forum threads. Influence among users' opinions is analyzed by taking into consideration the changes in their opinions. Therefore, a change in a user's opinion is modeled as a change of his/her posts' polarity. The hypothesis that underlies our research is that users' opinions may change over time as a consequence of the interactions between them in online discussions such as online forum threads. Moreover, a user's new post in an online forum thread is considered to have an influence on all the posts sent by other users in reaction to this. Our approach to the opinion influence phenomenon that arises in online forum threads is based on Natural Language Processing techniques, Latent Semantic Analysis and Post-Level Sentiment Analysis. The results obtained by us show that all the previous posts that contain opinions have an influence, more or less significant, on a new post in the same online forum thread.
本文对网络论坛帖子中的意见影响现象进行了研究。通过考虑用户意见的变化来分析用户意见的影响。因此,用户观点的变化被建模为他/她的帖子极性的变化。我们研究的基础假设是,用户的观点可能会随着时间的推移而改变,这是他们在在线讨论(如在线论坛线程)中相互作用的结果。此外,一个用户在一个在线论坛上的新帖子被认为对其他用户在这个帖子上发表的所有帖子有影响。我们的方法是基于自然语言处理技术、潜在语义分析和后期情感分析来研究在线论坛帖子中出现的意见影响现象。我们得到的结果表明,所有先前包含观点的帖子对同一在线论坛帖子的新帖子都有或多或少的影响。
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引用次数: 3
Multi-phase Identification in Microstructures Images Using a GPU Accelerated Fuzzy C-Means Segmentation 基于GPU加速模糊c均值分割的微结构图像多相识别
D. Onchis, D. Frunzaverde, Mihail Gaianu, Relu Ciubotariu
This paper presents an effective algorithm for the identification of multiple phases in microstructures images. The procedure is based on an efficient image segmentation using the fuzzy c-means algorithm. Furthermore, the algorithm is accelerated on a GPU cluster in order to obtain optimal computing times for large size images. The results are compared on the same experimental images with the ones obtained from a commercial software and the accuracy of the proposed algorithm is demonstrated.
本文提出了一种有效的显微结构图像多相识别算法。该程序是基于一个有效的图像分割使用模糊c均值算法。此外,为了获得大尺寸图像的最佳计算时间,算法在GPU集群上进行了加速。在相同的实验图像上与商业软件得到的结果进行了比较,验证了算法的准确性。
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引用次数: 3
Branch Differences and Lambert W 分支差异与Lambert W
D. J. Jeffrey, J. Jankowski
The Lambert W function possesses branches labelled by an index k. The value of W therefore depends upon the value of its argument z and the value of its branch index. Given two branches, labelled n and m, the branch difference is the difference between the two branches, when both are evaluated at the same argument z. It is shown that elementary inverse functions have trivial branch differences, but Lambert W has nontrivial differences. The inverse sine function has real-valued branch differences for real arguments, and the natural logarithm function has purely imaginary branch differences. The Lambert W function, however, has both real-valued differences and complex-valued differences. Applications and representations of the branch differences of W are given.
朗伯特W函数拥有以索引k标记的分支。因此,W的值取决于它的参数z的值和它的分支索引的值。给定两个分支,标记为n和m,分支差是两个分支之间的差,当它们在相同的参数z处求值时。表明初等反函数具有平凡的分支差,但Lambert W具有非平凡的差异。对于实参数,反正弦函数具有实值分支差,而自然对数函数具有纯虚分支差。然而,Lambert W函数既有实值差,也有复值差。给出了W的分支差的应用和表示。
{"title":"Branch Differences and Lambert W","authors":"D. J. Jeffrey, J. Jankowski","doi":"10.1109/SYNASC.2014.16","DOIUrl":"https://doi.org/10.1109/SYNASC.2014.16","url":null,"abstract":"The Lambert W function possesses branches labelled by an index k. The value of W therefore depends upon the value of its argument z and the value of its branch index. Given two branches, labelled n and m, the branch difference is the difference between the two branches, when both are evaluated at the same argument z. It is shown that elementary inverse functions have trivial branch differences, but Lambert W has nontrivial differences. The inverse sine function has real-valued branch differences for real arguments, and the natural logarithm function has purely imaginary branch differences. The Lambert W function, however, has both real-valued differences and complex-valued differences. Applications and representations of the branch differences of W are given.","PeriodicalId":150575,"journal":{"name":"2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125130677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Simulation-Extrapolation Gaussian Processes for Input Noise Modeling 输入噪声建模的模拟-外推高斯过程
B. Bócsi, Hunor Jakab, L. Csató
Input noise is common in situations when data either is coming from unreliable sensors or previous outputs are used as current inputs. Nevertheless, most regression algorithms do not model input noise, inducing thus bias in the regression. We present a method that corrects this bias by repeated regression estimations. In simulation extrapolation we perturb the inputs with additional input noise and by observing the effect of this addition on the result, we estimate what would the prediction be without the input noise. We extend the examination to a non-parametric probabilistic regression, inference using Gaussian processes. We conducted experiments on both synthetic data and in robotics, i.e., Learning the transition dynamics of a dynamical system, showing significant improvements in the accuracy of the prediction.
当数据来自不可靠的传感器或以前的输出被用作当前输入时,输入噪声很常见。然而,大多数回归算法没有对输入噪声建模,从而在回归中引起偏差。我们提出了一种通过重复回归估计来纠正这种偏差的方法。在模拟外推中,我们用额外的输入噪声扰动输入,通过观察这种添加对结果的影响,我们估计没有输入噪声的预测会是什么。我们将检验扩展到非参数概率回归,使用高斯过程进行推理。我们对合成数据和机器人进行了实验,即学习动力系统的过渡动力学,显示出预测准确性的显着提高。
{"title":"Simulation-Extrapolation Gaussian Processes for Input Noise Modeling","authors":"B. Bócsi, Hunor Jakab, L. Csató","doi":"10.1109/SYNASC.2014.33","DOIUrl":"https://doi.org/10.1109/SYNASC.2014.33","url":null,"abstract":"Input noise is common in situations when data either is coming from unreliable sensors or previous outputs are used as current inputs. Nevertheless, most regression algorithms do not model input noise, inducing thus bias in the regression. We present a method that corrects this bias by repeated regression estimations. In simulation extrapolation we perturb the inputs with additional input noise and by observing the effect of this addition on the result, we estimate what would the prediction be without the input noise. We extend the examination to a non-parametric probabilistic regression, inference using Gaussian processes. We conducted experiments on both synthetic data and in robotics, i.e., Learning the transition dynamics of a dynamical system, showing significant improvements in the accuracy of the prediction.","PeriodicalId":150575,"journal":{"name":"2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128624880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
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