PVaniM: a tool for visualization in network computing environments

Network computing has evolved into a popular and effective mode of high performance computing. Network computing environments have fundamental differences from hardware multiprocessors, involving a different approach to measuring and characterizing performance, monitoring an application's progress and understanding program behavior. In this paper, we present the design and implementation of PVaniM, an experimental visualization environment we have developed for the PVM network computing system. PVaniM supports a two-phase approach whereby on-line visualization focuses on large-grained events that are influenced by and relate to the dynamic network computing environment, and postmortem visualization provides for detailed program analysis and tuning. PVaniM's capabilities are illustrated via its use on several applications and a comparison with single-phase visualization environments developed for network computing. Our experiences indicate that, for several classes of applications, the two-phase visualization scheme can provide valuable insight into the behavior, efficiency and operation of distributed and parallel programs in network computing environments. © 1998 John Wiley & Sons, Ltd.     

Synthesis of halftone 3D images on an array processor

A fast generation of shaded images of CSG-defined objects could be accomplished by employing a general-purpose array processor. To fully exploit the computing power of such a machine, the visualization algorithm should be tailored to that specific kind of architecture. A parallel adaptation of the ray-tracing method and algorithm kernel operations is introduced. The performance analysis shows suitability of the approach for interactive CAD applications. Test pictures are presented.     

Experimenting Active Reliable Multicast on Application-Aware Grids

The ever growing needs for computation power and accesses to critical resources have launched in a very short time a large number of grid projects and many realizations have been done on dedicated network infrastructures. On Internet-based infrastructures, however, there are very few distributed or interactive applications (MPI, DIS, HLA, remote visualization) because of insufficient end-to-end performances (bandwidth, latency, for example) to support such an interactivity. For the moment, computing resources and network resources are viewed separately in the Grid architecture and we believe this is the main bottleneck for achieving end-to-end performances. In this paper, we promote the idea of a Grid infrastructure able to adapt to the applications needs and thus define the idea of application-aware Grid infrastructures where the network infrastructure is tightly involved in both the communication and processing process. We report on our early experiences in building application-aware components based on active networking technologies for providing a low latency and a low overhead multicast framework for applications running on a computational Grid. Performance results from both simulations and implementation prototypes confirm that introducing application-aware components at specific location in the network infrastructure can succeed in providing not only performances for the end-users but also new perspectives in building a communication framework for computational Grids.     

Development of a virtual aquarium system interacting with a smart device

New applications of smart devices interacting with other computing devices are recently providing interesting and feasible solutions in ubiquitous computing environments. In this study, we propose an interactive virtual aquarium system that interacts with a smart device as a user interface. We developed a virtual aquarium graphic system and a remote interaction application of a smart device for building an interactive virtual aquarium system. We performed an experiment that demonstrates the feasibility and the effectiveness of the proposed system as an example of a new type of interactive application of a smart display, where a smart device serves as a remote user interface.     

Pipelining parallel image compositing and delivery for efficient remote visualization

Scientific datasets of large volumes generated by next-generation computational sciences need to be transferred and processed for remote visualization and distributed collaboration among a geographically dispersed team of scientists. Parallel visualization using high-performance computing facilities is a typical approach to processing such increasingly large datasets. We propose an optimized image compositing scheme with linear pipeline and adaptive transport to support efficient image delivery to a remote client. The proposed scheme arranges an arbitrary number of parallel processors within a cluster in a linear order and divides the image into a carefully selected number of segments, which flow through the linear in-cluster pipeline and wide-area networks to the remote client consecutively. We analytically determine the segment size that minimizes the final image display time and derive the conditions where the proposed image compositing and delivery scheme outperforms the traditional schemes including the binary swap algorithm. In order to match the transport throughput for image delivery over wide-area networks to the pipelining rate for image compositing within the cluster, we design a class of transport protocols using stochastic approximation methods that are able to stabilize the data flow at a target rate. The experimental results from remote visualization of large-scale scientific datasets justify the correctness of our theoretical analysis and illustrate the superior performances of the proposed method.     

Parallel line integral convolution

Line integral convolution (LIC) is a powerful method for computing directional textures from vector data. LIC textures can be animated, yielding the effect of flowing motion. Both, static images and animation sequences are of great significance in scientific visualization. Although an efficient algorithm for computing static LIC textures is known, the generation of animation sequences still requires a considerable amount of computing time. In this paper we propose an algorithm for computing animation sequences on a massively parallel distributed memory computer. With this technique it becomes possible to utilise animated LIC for interactive vector field visualization. To take advantage of the strong temporal coherence between different frames, parallelization is performed in image space rather than in time. Image space coherence is exploited using a flexible update and communication scheme. In addition algorithmic improvements on LIC are proposed that can be applied to parallel and sequential algorithms as well.     

Chromium Renderserver: scalable and open remote rendering infrastructure

Chromium Renderserver (CRRS) is software infrastructure that provides the ability for one or more users to run and view image output from unmodified, interactive OpenGL and X11 applications on a remote, parallel computational platform equipped with graphics hardware accelerators via industry-standard Layer 7 network protocols and client viewers. The new contributions of this work include a solution to the problem of synchronizing X11 and OpenGL command streams, remote delivery of parallel hardware accelerated rendering, and a performance analysis of several different optimizations that are generally applicable to a variety of rendering architectures. CRRS is fully operational, Open Source software. imagery and sending it to a remote viewer.     

RIVA: a versatile parallel rendering system for interactivescientific visualization

JPL's Remote Interactive Visualization and Analysis System (RIVA) is described in detail. The RIVA system integrates workstation graphics, massively parallel computing technology, and gigabit communication networks to provide a flexible interactive environment for scientific data perusal, analysis, and visualization, RIVA's kernel is a highly scalable parallel perspective renderer tailored especially for the demands of large datasets beyond the sensible reach of workstations. Early experience with using RIVA to interactively explore and process multivariate, multiresolution datasets is reported; several examples using data from a variety of remote sensing instruments are discussed in detail and the results shown. Particular attention is placed on describing the algorithmic details of RIVA's parallel renderer kernel, with emphasis on the key aspects of achieving the algorithm's overall scalability. The paper summarizes the performance achieved for machine sizes up to more than 500 nodes and for initial input image/terrain bases in the 2 Gbyte range     

基于GOS的远程实时可视化系统设计与实现*

提出了一个并行计算程序的远程实时可视化系统.通过实时可视化处理计算程序在计算过程中输出的数据文件,实现了对计算程序计算过程的可视化跟踪和实时分析;同时该系统采用GOS作为网格中间件,屏蔽了复杂的异构环境.     

多线程并行运算技术在环境与化学计算中的应用

描述大气环境污染演化过程仿真和模式识别计算等越来越多迫切地需要使用先进高效的计算机并行处理、交互技术、多线程和可视化计算，而学习、使用和掌握这些专门的计算机技术又常使环境和化学等领域的研究者感到困惑和无从着手。本文从非计算机专业人员的角度出发，介绍多线程的基本概念、编程技术和实现方法，并结合大气环境与化学计算的情况，给出一个较为完整的交互式并行处理可视化多线程计算的实例。     

平行视觉:基于ACP的智能视觉计算方法

在视觉计算研究中,对复杂环境的适应能力通常决定了算法能否实际应用,已经成为该领域的研究焦点之一.由人工社会（Artificial societies）、计算实验（Computational experiments）、平行执行（Parallel execution）构成的ACP理论在复杂系统建模与调控中发挥着重要作用.本文将ACP理论引入智能视觉计算领域,提出平行视觉的基本框架与关键技术.平行视觉利用人工场景来模拟和表示复杂挑战的实际场景,通过计算实验进行各种视觉模型的训练与评估,最后借助平行执行来在线优化视觉系统,实现对复杂环境的智能感知与理解.这一虚实互动的视觉计算方法结合了计算机图形学、虚拟现实、机器学习、知识自动化等技术,是视觉系统走向应用的有效途径和自然选择.     

The application visualization system: a computational environmentfor scientific visualization

A software system for developing interactive scientific visualization applications quickly, with a minimum of programming effort, is described. This application visualization system (AVS) is an application framework targeted at scientists and engineers. The goal of the system is to make applications that combine interactive graphics and high computational requirements easier to develop for both programmers and nonprogrammers. AVS is designed around the concept of software building blocks, or modules, which can be interconnected to form visualization applications. AVS allows flow networks of existing modules to be constructed using a direct-manipulation user interface, and it automatically generates a simple user interface to each module     

基于Spark框架的乘潮水位计算与可视化平台

乘潮水位计算是海洋环境信息处理的重要组成部分,具有计算量大、计算复杂度高、计算时间长等特性。采用传统集群计算模式实现乘潮水位计算业务,存在计算成本高、计算伸缩性和交互性差的问题。针对以上问题,提出一种基于Spark框架的乘潮水位计算和可视化平台。结合对Spark任务调度算法的研究,设计和实现了一种基于节点计算能力的任务调度算法,实现了长时间序列的多任务乘潮水位数据的检索、获取、数值计算、特征可视化的并行处理,达到了海量海洋环境数据计算和可视化处理的目的。实验结果表明,提出的基于Spark的乘潮水位计算和可视化平台可以有效地提高海量乘潮水位数据的分布式并行处理的效率,为更加快速和高效的乘潮水位计算提供了一种新的方法。     

一种消息驱动的SOA 系统集成方法

针对企业SOA平台异构系统和服务的集成问题,提出了一种以Web服务为封装形式,消息队列为联系机制,软件管道为并行计算方案的的支持交互方式扩展的高性能松散耦合SOA系统集成方法。在应用层结合RPC原理、滑动窗口机制,引入商业并行计算软件管道技术,以消息驱动的方式实现了平台各系统间的服务同步/异步调用、并行事务处理以及拥塞控制,满足了业务交互和共享的需求。     

Trustworthy remote compiling services for grid-based scientific applications

Grid computing, which is characterized by large-scale sharing and collaboration of dynamic resources, is becoming an emerging computing platform on a global scale for data-intensive and computation-intensive scientific application. However, the complications of large-scale scientific computations and simulations harnessing massive computing resources are compounded by extensive heterogeneity in environments arising from “the Grid.” Scientists and engineers lack an intuitive grid-based compilation tool, which has contributed to the difficulty of exploiting these diverse resources and developing their applications on the grid. While manual configuration of various toolkits simplifying the end-to-end completion of a job is adequate for a computational grid with a limited number of nodes, the compilation procedure becomes inefficient for a computational grid with an increasing number of heterogeneous computational service providers. On the other hand, a global-scale computational grid is a potentially untrustworthy computing environment. How to take advantage of the potentially untrustworthy grid resources to provide trustworthy computational services for large-scale scientific applications is another critical issue. In this article, a remote compiling service for a heterogeneous computational grid is developed. In addition to running compilation tasks, the remote compiling service provides security enforcement and validation facilities, including intermediate value checking, secure source program submission, restricted compilation, and binary inspection, to support trustworthy compilation and execution of grid-based scientific applications. Overall, it is expected that our remote compiling services on the grid can tackle the heterogeneity problem of the grid and provide a secure, trustworthy, reliable, and state-of-the-art mechanism to develop grid-aware scientific applications.

Ray casting architectures for volume visualization

Real-time visualization of large volume data sets demands high-performance computation, pushing the storage, processing and data communication requirements to the limits of current technology. General-purpose parallel processors have been used to visualize moderate-size data sets at interactive frame rates; however, the cost and size of these supercomputers inhibits the widespread use for real-time visualization. This paper surveys several special-purpose architectures that seek to render volumes at interactive rates. These specialized visualization accelerators have cost, performance and size advantages over parallel processors. All architectures implement ray casting using parallel and pipelined hardware. We introduce a new metric that normalizes performance to compare these architectures. The architectures included in this survey are VOGUE, VIRIM, Array-Based Ray Casting, EM-Cube and VIZARD II. We also discuss future applications of special-purpose accelerators     

Value of information based scheduling of cloud computing resources

Traditionally, heavy computational tasks were performed on a dedicated infrastructure requiring a heavy initial investment, such as a supercomputer or a data center. Grid computing relaxed the assumptions of the fixed infrastructure, allowing the sharing of remote computational resources. Cloud computing brought these ideas into the commercial realm and allows users to request on demand an essentially unlimited amount of computing power. However, in contrast to previous assumptions, this computing power is metered and billed on an hour-by-hour basis.In this paper, we are considering applications where the output quality increases with the deployed computational power, a large class including applications ranging from weather prediction to financial modeling. We are proposing a computation scheduling that considers both the financial cost of the computation and the predicted financial benefit of the output, that is, its value of information (VoI). We model the proposed approach for an example of analyzing real-estate investment opportunities in a competitive environment. We show that by using the VoI-based scheduling algorithm, we can outperform minimalistic computing approaches, large but fixedly allocated data centers and cloud computing approaches that do not consider the VoI.     

Towards implementation of a novel scheme for data prefetching on distributed shared memory systems

High speed networks and rapidly improving microprocessor performance make the network of workstations an extremely important tool for parallel computing in order to speedup the execution of scientific applications. Shared memory is an attractive programming model for designing parallel and distributed applications, where the programmer can focus on algorithmic development rather than data partition and communication. Based on this important characteristic, the design of systems to provide the shared memory abstraction on physically distributed memory machines has been developed, known as Distributed Shared Memory (DSM). DSM is built using specific software to combine a number of computer hardware resources into one computing environment. Such an environment not only provides an easy way to execute parallel applications, but also combines available computational resources with the purpose of speeding up execution of these applications. DSM systems need to maintain data consistency in memory, which usually leads to communication overhead. Therefore, there exists a number of strategies that can be used to overcome this overhead issue and improve overall performance. Strategies as prefetching have been proven to show great performance in DSM systems, since they can reduce data access communication latencies from remote nodes. On the other hand, these strategies also transfer unnecessary prefetching pages to remote nodes. In this research paper, we focus on the access pattern during execution of a parallel application, and then analyze the data type and behavior of parallel applications. We propose an adaptive data classification scheme to improve prefetching strategy with the goal to improve overall performance. Adaptive data classification scheme classifies data according to the accessing sequence of pages, so that the home node uses past history access patterns of remote nodes to decide whether it needs to transfer related pages to remote nodes. From experimental results, we can observe that our proposed method can increase the accuracy of data access in effective prefetch strategy by reducing the number of page faults and misprefetching. Experimental results using our proposed classification scheme show a performance improvement of about 9–25% over the same benchmark applications running on top of an original JIAJIA DSM system.

大规模油藏数值模拟软件并行计算技术及在Beowulf系统上的应用进展

本文主要介绍了大规模油藏数值模拟并行计算技术在国内的研究进展,提供了精细油藏模拟在国产Beowulf系统上的计算实例和应用效果,给出了百万网格点规模的油藏应用算例在不同处理器规模下的数值模拟计算结果与性能分析,并实现了一个针对海量数据可视化的三维图、二维图、表格显示的后处理显示系统．     

Coinductive Models of Finite Computing Agents

This paper explores the role of coinductive methods in modeling finite interactive computing agents. The computational extension of computing agents from algorithms to interaction parallels the mathematical extension of set theory and algebra from inductive to coinductive models. Maximal fixed points are shown to play a role in models of observation that parallels minimal fixed points in inductive mathematics. The impact of interactive (coinductive) models on Church's thesis and the connection between incompleteness and greater expressiveness are examined. A final section shows that actual software systems are interactive rather than algorithmic. Coinductive models could become as important as inductive models for software technology as computer applications become increasingly interactive.