Formulae Meet Programs Over the Net: A Framework for Correct Network Aware Programming

A general framework for network aware programming is presented that consists of a language for programming mobile applications, a logic for specifying properties of the applications and an automatic tool for verifying such properties. The framework is based on X-KLAIM, eXtended KLAIM, an experimental programming language specifically designed to program distributed systems composed of several components interacting through multiple tuple spaces and mobile code. The proposed logic is a modal logic inspired by Hennessy-Milner logic and is interpreted over the same labelled structures used for the operational semantics of X-KLAIM. The automatic verification tool is based on a complete proof system that has been previously developed for the logic.     

Distributed Parallel Computing Using Navigational Programming

Message Passing (MP) and Distributed Shared Memory (DSM) are the two most common approaches to distributed parallel computing. MP is difficult to use, whereas DSM is not scalable. Performance scalability and ease of programming can be achieved at the same time by using navigational programming (NavP). This approach combines the advantages of MP and DSM, and it balances convenience and flexibility. Similar to MP, NavP suggests to its programmers the principle of pivot-computes and hence is efficient and scalable. Like DSM, NavP supports incremental parallelization and shared variable programming and is therefore easy to use. The implementation and performance analysis of real-world algorithms, namely parallel Jacobi iteration and parallel Cholesky factorization, presented in this paper supports the claim that the NavP approach is better suited for general-purpose parallel distributed programming than either MP or DSM.     

数据密集型计算编程模型研究进展

作为一种新兴的计算模式,云计算受到了学术界和产业界的广泛关注.云计算以互联网服务和应用为中心,服务提供者需要存储和分析海量数据.为了能够低成本高效率地处理Web量级数据,主要的互联网公司都在由商品化服务器组成的大规模集群系统上研发了分布式编程系统.编程模型可以降低开发人员在大规模集群上编程的难度,并让程序充分利用集群资源,但设计这样的编程模型面临巨大挑战.首先说明了数据密集型计算的特点,并指出了编程模型要解决的基本问题;接着深入介绍了国际上代表性的编程模型,并对这些编程模型的特点进行了比较和分析;最后对当前所面临的问题和今后的发展趋势进行了总结和展望.     

Fundamental Concepts in Programming Languages

This paper forms the substance of a course of lectures given at the International Summer School in Computer Programming at Copenhagen in August, 1967. The lectures were originally given from notes and the paper was written after the course was finished. In spite of this, and only partly because of the shortage of time, the paper still retains many of the shortcomings of a lecture course. The chief of these are an uncertainty of aim—it is never quite clear what sort of audience there will be for such lectures—and an associated switching from formal to informal modes of presentation which may well be less acceptable in print than it is natural in the lecture room. For these (and other) faults, I apologise to the reader.There are numerous references throughout the course to CPL [1–3]. This is a programming language which has been under development since 1962 at Cambridge and London and Oxford. It has served as a vehicle for research into both programming languages and the design of compilers. Partial implementations exist at Cambridge and London. The language is still evolving so that there is no definitive manual available yet. We hope to reach another resting point in its evolution quite soon and to produce a compiler and reference manuals for this version. The compiler will probably be written in such a way that it is relatively easyto transfer it to another machine, and in the first instance we hope to establish it on three or four machines more or less at the same time.The lack of a precise formulation for CPL should not cause much difficulty in this course, as we are primarily concerned with the ideas and concepts involved rather than with their precise representation in a programming language.     

大数据群体计算中用户主题感知的任务分配

大数据问题所固有的规模繁杂性、高速增长性、形式多样性、价值密度低等特点为传统计算处理方法带来了严峻的挑战.一方面,大数据的规模繁杂性和高速增长性带来了海量计算分析的需求;另一方面,形式多样性和价值密度低等特点使得大数据计算任务高度依赖复杂认知推理技术.针对大数据计算中海量计算分析和复杂认知推理需求并存的技术挑战,传统的基于计算机的算法已经无法满足日益苛刻的数据处理要求,而基于人机协作的群体计算是有效的解决途径.在大数据群体计算中,最基础的就是任务的分配方式.考虑到大量网络用户不同的专业背景、诚信程度,因此不能简单随机地将要处理的任务交给大众来完成.针对此问题,提出了一种基于用户主题感知的迭代式任务分配算法.利用已知答案的测试问题迭代地检测不同人群的专业背景和完成任务的准确率.在充分了解用户真实主题和准确率的情况下为他们分配合适的问题.通过和随机任务分配算法在模拟数据和真实数据上的对比,有效显示了基于主题感知任务分配算法的准确性.