Perception-based animation rendering

In most currently produced computer generated animation there is significant visual information that is literally never seen because of the characteristics of the human visual system. By tailoring the rendering of animation images to match the characteristics of human visual perception, significant computational and image storage savings can be obtained while retaining perceived animation quality. Vision research indicates that the human visual system processes two information channels. The transient channel rapidly processes low spatial resolution full colour information. The sustained channel processes high spatial resolution luminance information at a much slower rate. This paper discusses how the rendering of animation images can be accomplished to match these perception characteristics and achieve significant savings while maintaining animation quality. Computation and storage savings of up to 80 per cent are possible. Several animation segments have been produced to demonstrate the viability of this approach.     

A performance study of deadlock prevention algorithms in a distributed file system

This paper describes an implementation and performance evaluation of different deadlock prevention algorithms. A deadlock prevention algorithm ensures that deadlock will never happen. The algorithms for deadlock prevention are proposed and implemented in a locally distributed system. A number of experiments were executed in a distributed system for various lengths of file operation and different numbers of files. The performance of the system and of each algorithm is evaluated and discussed. Some general results are derived for a single-host and a distributed system.     

神经模糊控制在船舶自动舵中的应用

针对常规模糊自动舵由于受船舶控制过程的非线性、时变性以及风浪干扰等因素影响,模糊控制规则和隶属函数需要校正,利用神经网络的自学习能力,用神经网络去实现模糊控制,设计自动舵神经模糊控制器,采用BP算法和最小二乘算法的混合学习算法实现对模糊规则和隶属函数的参数训练,提高控制器的自适应能力。仿真实验表明所设计的控制器有效可行,适应船舶在风浪干扰环境下的控制性能要求。     

ParSA: High-throughput scientific data analysis framework with distributed file system

Scientific data analysis and visualization have become the key component for nowadays large scale simulations. Due to the rapidly increasing data volume and awkward I/O pattern among high structured files, known serial methods/tools cannot scale well and usually lead to poor performance over traditional architectures. In this paper, we propose a new framework: ParSA (parallel scientific data analysis) for high-throughput and scalable scientific analysis, with distributed file system. ParSA presents the optimization strategies for grouping and splitting logical units to utilize distributed I/O property of distributed file system, scheduling the distribution of block replicas to reduce network reading, as well as to maximize overlapping the data reading, processing, and transferring during computation. Besides, ParSA provides the similar interfaces as the NetCDF Operator (NCO), which is used in most of climate data diagnostic packages, making it easy to use this framework. We utilize ParSA to accelerate well-known analysis methods for climate models on Hadoop Distributed File System (HDFS). Experimental results demonstrate the high efficiency and scalability of ParSA, getting the maximum 1.3 GB/s throughput on a six nodes Hadoop cluster with five disks per node. Yet, it can only get 392 MB/s throughput on a RAID-6 storage node.     

Design and implementation of a distributed file system

We introduce a new model for replication in distributed systems. The primary motivation for replication lies in fault tolerance. Although there are different kinds of replication approaches, our model combines the advantages of modular redundancy and primary-stand-by approaches to give more flexibility with respect to system configuration. To implement such a model, we select the IBM PC-net with MS-DOS environment as our base. Transparency as well as fault-tolerance file access are the highlights of our system design. To fulfil these requirements, we incorporate the idea of directory-oriented replication and extended prefix tables in the system design. The implementation consists of a command shell, a DOS manager, and a recovery manager. Through this design, we can simulate a UNIX-like distributed file system whose function is compatible with MS-DOS.     

面向Hadoop分布式文件系统的小文件存取优化方法

为提高Hadoop分布式文件系统(HDFS)的小文件处理效率,提出了一种面向HDFS的智能小文件存取优化方法--SmartFS。SmartFS通过分析小文件访问日志,获取用户访问行为,建立文件关联概率模型,并根据基于文件关联关系的合并算法将小文件组装成大文件之后存至HDFS;当从HDFS获取文件时,根据基于文件关联关系的预取算法来提高文件访问效率,并提出基于预取的缓存替换算法来管理缓存空间,从而提高文件的命中率。实验结果表明,SmartFS有效减少了HDFS中NameNode的元数据空间,减少了用户与HDFS的交互次数,提高了小文件的存储效率和访问速度。     

高性能高适应性分布式文件服务器研究与实现

随着计算机网络技术的发展以及Internet技术广泛应用,分布式技术及分布式系统应运而生.但不管是传统分布式文件系统还是面向Internet的分布式文件系统,它们在可靠性、扩展性、易用性及性能等诸多方面都很难做到兼得.研究的目标就是面向分布式计算环境下的文件存储系统,分析当前文件服务器领域的现状及不足,研究构造一个高性能、高适应性的分布式文件服务器系统所涉及到的理论和技术问题.提出了基于虚拟机的分布式文件服务器Cache系统的设计思想,并给出了详细的设计方案;将Adapter的设计思想用于文件传输协议模块的设计,使文件服务器的网络环境适应能力以及客户需求的适应能力得到加强.     

基于新型存储器件的分布式文件系统性能优化

新型存储器件的I/O性能通常比传统固态驱动器（SSD）高一个数量级,然而使用新型存储器件的分布式文件系统相对于使用SSD的分布式文件系统性能并没有显著的提高,这说明目前的分布式文件系统并不能充分发挥新型存储器件的性能。针对这个问题,对Hadoop分布式文件系统（HDFS）的数据写入流程及传输过程进行了量化分析。通过量化分析HDFS数据写入过程各阶段的时间开销,发现在写入数据的各个阶段中,节点间数据传输的时间占比较大。因此提出了对应的优化方案,通过异步写入的方式并行化数据传输与处理过程,使得不同数据包的处理阶段叠加起来,减少了数据包整体的处理时间,从而提升了HDFS的写入性能。实验结果表明,所提方案将HDFS的写入吞吐量提升了15%~24%,总体的写入执行时间降低了28%~36%。     

Interactive control of biomechanical animation

Physics-based animation can be generated by performing a complete dynamical simulation of multibody systems. This leads to the solving of a complex system of differential equations in which biomechanical results for the physics of impacts are incorporated. Motion control is achieved by interactively modifying the internal torques. Realtime response requires the distribution of the workload of the computation between a high-speed compute server and the graphics workstation by means of a remote-procedure call mechanism.     

The pulse distributed file system

A network of powerful personal computers, linked by a high-speed local area network, is being seen increasingly as an alternative to a traditional centralized time-sharing operating system. The PULSE project is investigating how such a system may be constructed to give the benefits of a self-sufficient personal computer to each user without losing the facilities for communication and sharing of data inherent in centralized systems. In particular, a distributed file system has been built which provides a single global UNIX

1 UNIX is a trademark of Bell Laboratories.

-like hierarchy, with a consistent appearance when accessed from any machine. Replicated copies of files are maintained to improve reliability, increase performance, and enable each machine to run stand-alone, albeit with reduced facilities.     

VM aware journaling: improving journaling file system performance in virtualization environments

Journaling file systems, which are widely used in modern operating systems, guarantee file system consistency and data integrity by logging file system updates to a journal, which is a reserved space on the storage, before the updates are written to the data storage. Such journal writes increase the write traffic to the storage and thus degrade the file system performance, especially in full data journaling, which logs both metadata and data updates. In this paper, a new journaling approach is proposed to eliminate journal writes in server virtualization environments, which are gaining in popularity in server platforms. Based on reliable hardware subsystems and virtual machine monitor (VMM), the proposed approach eliminates journal writes by retaining journal data (i.e. logged file system updates) in the memory of each virtual machine and ensuring the integrity of these journal data through cooperation between the journaling file systems and the VMM. We implement the proposed approach in Linux ext3 in the Xen virtualization environment. According to the performance results, a performance improvement of up to 50.9journaling approach of ext3 due to journal write elimination. In metadata‐write dominated workloads, this approach could even outperform the metadata journaling approaches of ext3, which do not guarantee data integrity. These results demonstrate that, on virtual servers with reliable VMM and hardware subsystems, the proposed approach is an effective alternative to traditional journaling approaches. Copyright © 2011 John Wiley & Sons, Ltd.     

The performance of the Amoeba distributed operating system

Amoeba is a capability-based distributed operating system designed for high-performance interactions between clients and servers using the well-known RPC model. The paper starts out by describing the architecture of the Amoeba system, which is typified by specialized components such as workstations, several services, a processor pool, and gateways that connect other Amoeba systems transparently over wide-area networks. Next the RPC interface is described. The paper presents performance measurements of the Amoeba RPC on unloaded and loaded systems. The time to perform the simplest RPC between two user processes has been measured to be 1-4 ms. Compared to SUN 3/50's RPC, Amoeba has one ninth of the delay, and over three times the throughput. Finally we describe the Amoeba file server. The Amoeba file server is so fast that it is limited by the communication bandwidth. To the best of our knowledge this is the fastest file server yet reported in the literature for this class of hardware.     

海量小文件系统的可移植操作系统接口兼容技术

基于Hadoop分布式文件系统（HDFS）研发的海量小文件系统（SMDFS）遗留了HDFS不兼容可移植操作系统接口（POSIX）约束的问题,为解决SMDFS的这一问题,提出基于本地缓存的POSIX兼容技术和基于数据暂存区的元数据高效管理技术。首先,通过设置数据暂存区来实现读写模式文件流的重定向,然后建立异步线程池模型,实现数据暂存区镜像文件的同步,从而完成用户层到存储层的所有POSIX相关的文件操作。此外,借助跳表结构的元数据缓存实现List目录等元数据操作效率优化。测试表明,相较于HDFS的Linux客户端,基于技术成果实现的SMDFS3.0的随机读性能有10倍以上的性能提升,顺序读和顺序写性能有约3~4倍的提升,随机写性能可以达到本地文件系统的20%,基于目录的元数据缓存的设计使目录的List操作效率提升近10倍。但是,由于用户空间文件系统（FUSE）挂载的客户端会引入额外的内核态和用户态切换等带来的开销,因此SMDFS3.0的Linux客户端相对于系统的Java接口会有大约50%的性能损耗。     

NTFS文件系统剖析

针对Windows操作系统所使用的NTFS与FAT文件系统的簇、容量、安全、字符集等,分析了两种文件系统的区别与优缺点.分析了NTFS文件系统使用的数据结构,特别针对其主控文件表MFT、MFT记录、文件结构、目录结构进行了深入的剖析,给出了NTFS文件系统中文件与目录的MFT记录结构.     

分布式并行文件系统中锁管理的研究

分析了传统的分布式锁管理中范围锁的实现及其局限,给出了一种用于支持交叉访问模式的新的分布式锁管理算法--DBM算法.分布式锁管理是分布式并行文件系统的关键组成部分之一.软件仿真实验表明新算法在交叉访问模式下大大提高了系统并发度.     

Modeling of multiple copy update costs for file allocation in distributed databases

The file allocation problem for distributed databases has been extensively studied in the literature and the objective is to minimize total costs consisting of storage, query and update communication costs. Current modeling of update communication costs is simplistic and does not capture the working of most of the protocols that have been proposed. This paper shows that more accurate modeling of update costs can be achieved fairly easily without an undue increase in the complexity of the formulation. In particular, formulations for two classes of update protocols are shown. Existing heuristics can be used on these formulations to obtain good solutions.     

Overcoming data locality: An in-memory runtime file system with symmetrical data distribution

In many-task computing (MTC), applications such as scientific workflows or parameter sweeps communicate via intermediate files; application performance strongly depends on the file system in use. The state of the art uses runtime systems providing in-memory file storage that is designed for data locality: files are placed on those nodes that write or read them. With data locality, however, task distribution conflicts with data distribution, leading to application slowdown, and worse, to prohibitive storage imbalance. To overcome these limitations, we present MemFS, a fully symmetrical, in-memory runtime file system that stripes files across all compute nodes, based on a distributed hash function. Our cluster experiments with Montage and BLAST workflows, using up to 512 cores, show that MemFS has both better performance and better scalability than the state-of-the-art, locality-based file system, AMFS. Furthermore, our evaluation on a public commercial cloud validates our cluster results. On this platform MemFS shows excellent scalability up to 1024 cores and is able to saturate the 10G Ethernet bandwidth when running BLAST and Montage.     

分布式文件系统数据块聚类存储节能策略

针对分布式文件系统中由于数据块随机放置而导致的服务器利用率低、能耗管理复杂的问题,建立了数据块访问特征向量模型描述用户对数据块的随机访问,运用K-means算法对数据块进行聚类计算,根据计算结果将数据节点划分为多个区域以存储不同聚类簇的数据块,在系统负载较低时进行数据块动态重配置,关闭不必要节点达到节能的目的。为使得策略适用于对能耗和资源利用率有不同要求的场景,算法中聚类簇间隔参数可灵活设置。实验通过和冷热区划分算法进行比较表明:按照聚类结果进行数据块重配置后,能耗节省效率优于冷热区划分算法,节省能耗35%~38%。     

一种嵌入式P2P文件共享系统

论述Windows CE嵌入式平台上一种P2P文件共享系统的实现。并针对嵌入式平台的特点,对该文件共享系统进行了优化,提出两种优化方法：充分利用内存资源和改善排队算法。前者解决嵌入式平台I/O操作速度慢的问题,后者解决嵌入式系统Socket资源受限的问题。测试结果表明,这些优化方法有效地改善了文件共享系统的性能,提高了文件下载速率。