The Legion support for advanced parameter-space studies on a grid

Parameter-space (p-space) studies involve running a single application several times with different parameter sets. Since the jobs are mutually independent, many computing resources can be recruited to conduct an entire study in a distributed manner. The p-space studies are attractive applications for grids, which are networked collections of computing and other resources. Legion is a grid infrastructure that facilitates the secure and easy use of heterogeneous, geographically distributed resources by providing the illusion of a single virtual machine from those resources. Legion provides tools and services that support advanced p-space studies, i.e., studies that make complex demands such as transparent access to distributed files, fault-tolerance and security. We demonstrate these benefits with a protein-folding experiment in which a molecular simulation package was run over a grid managed by Legion.     

网格环境下的作业运行支持系统分析*

网格环境下的作业运行支持系统支持用户在网格资源上远程提交作业任务,执行科学计算应用程序,并管理运行着的作业任务.作业运行支持系统解决了计算执行环境的准备、状态监视汇报、运行时操纵和I/O支持等方面的关键问题.现有的几种主要的网格中间件系统均提供了作业执行和管理工具,很好地解决了几个主要问题,但并不能完全满足用户的需要,还需进一步改进与完善.     

Genetic Threading

The biological function of proteins is dependent, to a large extent, on their native three dimensional conformation. Thus, it is important to know the structure of as many proteins as possible. Since experimental methods for structure determination are very tedious, there is a significant effort to calculate the structure of a protein from its linear sequence. Direct methods of calculating structure from sequence are not available yet. Thus, an indirect approach to predict the conformation of protein, called threading, is discussed. In this approach, known structures are used as constraints, to restrict the search for the native conformation. Threading requires finding good alignments between a sequence and a structure, which is a major computational challenge and a practical bottleneck in applying threading procedures. The Genetic Algorithm paradigm, an efficient search method that is based on evolutionary ideas, is used to perform sequence to structure alignments. A proper representation is discussed in which genetic operators can be effectively implemented. The algorithm performance is tested for a set of six sequence/structure pairs. The effects of changing operators and parameters are explored and analyzed.     

蛋白质折叠计算机模拟研究进展

蛋白质折叠过程中的结构变异可能导致\"折叠病\",比如老年痴呆症和多聚谷氨酰胺疾病等。因此蛋白质折叠研究对于揭示\"折叠病\"致病机理、指导药物设计等具有重大意义。文章阐述了蛋白质折叠计算机模拟研究的研究近况,分别介绍了蛋白质侧链研究、蛋白质折叠算法、蛋白质折叠病研究、蛋白质的分子动力学模拟和蛋白质结构预测等几个方面。     

求解蛋白质折叠构形预测问题的PERM改进算法

PERM算法用来求解蛋白质折叠构形预测问题具有非常高的效率。本文介绍了PERM算法的思想，并详细介绍了一种我们改进的PERM算法。使用该算法求解蛋白质折叠构形预测的二维HP格点模型取得了相当好的计算结果。     

PaGrid: A Mesh Partitioner for Computational Grids

Computational Grids are emerging as a new infrastructure for high performance computing. Since the resources in a Grid can be heterogeneous and distributed, mesh-based applications require a mesh partitioner that considers both processor and network heterogeneity. We have developed a heterogeneous mesh partitioner, called PaGrid. PaGrid uses a multilevel graph partitioning approach, augmented by execution time load balancing in the final uncoarsening phase. We show that minimization of total communication cost (e.g., as used by JOSTLE) can lead to significant load being placed on processors connected by slow links, which results in higher application execution times. Therefore, PaGrid balances the estimated execution time of the application across processors. PaGrid performance is compared with two existing mesh partitioners, METIS 4.0 and JOSTLE 3.0, for mapping several application meshes to two models of heterogeneous computational Grids. PaGrid is found to produce significantly better partitions than JOSTLE and slightly better partitions than METIS in most cases, in terms of estimated application execution time averaged over a large number of runs with different random number seeds.     

机器学习方法在蛋白质折叠结构预测中的应用研究

蛋白质的生物功能是由它们的空间折叠结构决定的,理解蛋白质的折叠过程是生物信息学领域中极具挑战性的问题之一。近年来,各种优化方法用于蛋白质空间折叠结构预测。这些方法仍存在着不足,算法在变量数目增大时,难以收敛到全局最优解,并容易产生早熟收敛,从而影响求解精度和效率。针对蛋白质结构预测模型多变量多极值的特点,文章采用机器学习方法对蛋白质进行折叠结构预测。     

网格体系结构、关键技术及其应用*

网格是信息社会的网络基础设施,是下一代Web技术。通过介绍网格概念、特点,阐述了网格的组成与体系结构,诠释了网格系统的关键技术,分析了几个网格系统,最后讨论了网格的应用领域。     

网格计算的研究与应用

网格计算是一种新型的分布计算技术.文章介绍了网格系统的概念、特点、功能,阐述了网格的体系结构.并对网格计算技术的应用前景作了分析.     

网格计算技术综述

本文介绍了网格的定义,详细讨论了网格的特征、体系结构、网格计算系统的功能和相应及其应用前景.     

网格计算的研究与应用

网格计算是一种新型的分布计算技术。文章介绍了网格系统的概念、特点、功能,阐述了网格的体系结构。并对网格计算技术的应用前景作了分析。     

网格计算的研究新进展

网格计算诞生了一个全新的领域,它以大规模的资源协作共享、创新的应用以及高性能计算的特点,区别于传统的分布式计算。文章简述了网格计算的定义、特点、功能和基本体系结构,重点综述了网格的研究新进展及其商业化应用前景,分析了当前网格研究的发展趋势及其我国的对策。     

网格计算的研究与探讨

网格是一个基础体系结构,它耦合了:计算机、软件和数据库,通过局域网/广域网,形成一个统一的集成资源。网格计算(Grid Computing)指的是独立的用户组在高速网络上动态地共享计算机资源,形成对用户透明的超级计算环境,以满足不断变化的计算需求。本文介绍了网格计算结构、基本功能、网络的项目及体系结构,并对国内外著名的网格计算理论和应用作介绍,分析了目前网格计算研究的现状和发展前景。     

进化策略的一种改进及其在蛋白质结构预测中的应用

进化策略算法是一种模拟自然界生物进化过程的全局优化方法。本文将一种改进的进化策略算法应用于蛋白质三维HPNX非格模型,较成功地预测了蛋白质序列1RPB、1BPI和1UBQ的折叠趋势,说明了三维HPNX非格模型比简化HP非格模型更能准确地描述蛋白质的折叠情况,同时表明了进化策略算法用于蛋白质结构预测问题是可行的、有效的。     

求解蛋白质折叠问题的模拟退火算法

通过构造新的数学模型,把三维AB模型的蛋白质折叠问题由一个带约束的优化问题转化为无约束优化问题,然后提出一个模拟退火算法.对如何得到初始构形,提出了一个启发式策略.实算结果表明,本文算法效率较高,对四条氨基酸测试序列,本文算法得到的最低能量都要优于nPERM算法得到的结果.     

基于Globus Toolkit和SunTM ONE Grid Engine构建计算网格

网格计算(grid computing)是近几年发展起来的一个崭新研究领域,引起国内外学术界及工业界的广泛关注。其目的是研究如何安全有效地将现有的各种计算资源(尤其是那些分布在Internet的异构网络中的计算资源)组织起来协同解决复杂的科学及工程计算问题。在化学信息学和生物信息学中最典型的应用是虚拟高通量筛选侯选药物分子。本文以两个Linux机群为基础,用开放源码的网格支持软件包Globus Toolkit 3．2及Sun^TM ONE Grid Engine 5．3成功构建了计算网格;并通过设计测试程序实现一次性提交多个作业(300个)以及分析作业在计算网格中各个节点的分配及运行情况,从而测试了计算网格的效率。结果表明,所构建的计算网格在保持原机群运行稳定、可靠的前提下,改进了系统资源的分配管理方式以及用户提交作业的方法,从整体上提高了网络计算资源的利用率,也同时方便了系统的管理。     

The Protein Structure Prediction Problem: A Constraint Optimization Approach using a New Lower Bound

The protein structure prediction problem is one of the most (if not the most) important problem in computational biology. This problem consists of finding the conformation of a protein with minimal energy. Because of the complexity of this problem, simplified models like Dill's HP-lattice model [15], [16] have become a major tool for investigating general properties of protein folding. Even for this simplified model, the structure prediction problem has been shown to be NP-complete [5], [7]. We describe a constraint formulation of the HP-model structure prediction problem, and present the basic constraints and search strategy. Of course, the simple formulation would not lead to an efficient algorithm. We therefore describe redundant constraints to prune the search tree. Furthermore, we need bounding function for the energy of an HP-protein. We introduce a new lower bound based on partial knowledge about the final conformation (namely the distribution of H-monomers to layers).     

Improvements to the Control Techniques of Sequential Inference Machines——from Instructions to Hardware Organization

Nondeterminism of PROLOG execution requires that a block of control information or a choice point for each procedure call be stored when there are other candidate clauses to be used.When the currently selected clause fails,the bindings made by the clause must be undone and the stored choice point is reactivated,and then another clause of the candidate ones is chosen to run on it.Storing and reactivating choice points and undoing account for the great overhead are required to control PROLOG execution,which is quite different from conventional programs.This paper focuses on the techniques used in Sequential PROLOG Engine (SPE) to reduce the overhead of control operations.The control instructions of SPE store no more choice points than the necessary.Its architecture takes the approaches of analysing the potential parallelism in the control operations and developing a fraction of it due to the cost-effect consideration.The results of executing two sample programs on SPE in the form of hand timings are presented,which favor the approach.     

网格体系结构及关键技术分析

网格计算是以大规模的资源协作共享、创新的应用以度高性能计算为特点，诞生的一个全新领域。本文对网格的特点和体系结构进行了介绍，并详细分析了网格计算的关键技术。