A join algorithm for combining AND parallel solutions in AND/OR parallel systems

When two or more literals in the body of a Prolog clause are solved in (AND) parallel, their solutions need to bejoined to compute solutions for the clause. This is often a difficult problem in parallel Prolog systems that exploit OR and independent AND parallelism in Prolog programs. In several AND/OR parallel systems proposed recently, this problem is side-stepped at the cost of unexploited OR parallelism in the program, in part due to the complexity of the backtracking algorithm beneath AND parallel branches. In some cases, the data dependency graphs used by these systems cannot represent all the exploitable indenpendent AND parallelism known at compile time.In this paper, we describe the compile time analysis for an optimizedjoin algorithm for supporting independent AND parallelism in logic programs efficiently without leaving any OR parallelism unexploited. We then discuss how this analysis can be used to yield very efficient runtime behavior. We also discuss problems associated with a tree representation of the search space when arbitrarily complex data dependency graphs are permitted. We describe how these problems can be resolved by mapping the search space onto the data dependency graphs themselves. The algorithm has been implemented in a compiler for parallel Prolog based on the Reduce-OR process model. The algorithm is suitable for the implementation of AND/OR systems on both shared and nonshared memory machines. Performance on benchmark programs exhibiting AND and OR parallelism on one shared memory machine and one message passing machine is presented.This work was supported in part by NSF Grants CCR-87-00988 and CCR-89-02496.A shorter version of this paper appears in theProceedings of NACLP 1990.     

The muse approach to Or-parallel prolog

Muse (Multi-sequential Prolog engines) is a simple and efficient approach to Or-parallel execution of Prolog programs. It is based on having several sequential Prolog engines, each with its local address space, and some shared memory space. It is currently implemented on a 7-processors machine with local/shared memory constructed at SICS, a 16-processors Sequent Symmetry, a 96-processors BBN Butterfly I, and a 45-processors BBN Butterfly II. The sequential SICStus Prolog system has been adapted to Or-parallel implementation. Extra overhead associated with this adaptation is very low in comparison with the other approaches. The speed-up factor is very close to the number of processors in the system for a large class of problems.The goal of this paper is to present the Muse execution model, some of its implementation issues, a variant of Prolog suitable for multiprocessor implementations, and some experimental results obtained from two different multiprocessor systems.     

Improving the efficiency of nondeterministic independent and-parallel systems

We present the design and implementation of the and-parallel component of ACE. ACE is a computational model for the full Prolog language that simultaneously exploits both or-parallelism and independent and-parallelism. A high-performance implementation of the ACE model has been realized and its performance reported in this paper. We discuss how some of the standard problems which appear when implementing and-parallel systems are solved in ACE. We then propose a number of optimizations aimed at reducing the overheads and the increased memory consumption which occur in such systems when using previously proposed solutions. Finally, we present results from an implementation of ACE which includes the optimizations proposed. The results show that ACE exploits and-parallelism with high efficiency and high speedups. Furthermore, they also show that the proposed optimizations, which are applicable to many other and-parallel systems, significantly decrease memory consumption and increase speedups and absolute performance both in forward execution and during backtracking.     

Efficient unsteady high Reynolds number flow computations on unstructured grids

Despite the advances in computer power and numerical algorithms over the last decades, solutions to unsteady flow problems remain computing time intensive. Especially for high Reynolds number flows, nonlinear multigrid, which is commonly used to solve the nonlinear systems of equations, converges slowly. The stiffness induced by the high-aspect ratio cells and turbulence is not tackled well by this solution method.In this paper, it is investigated if a Jacobian-free Newton-Krylov (jfnk) solution method can speed up unsteady flow computations at high Reynolds numbers. Preconditioning of the linear systems that arise after Newton linearization is commonly performed with matrix-free preconditioners or approximate factorizations based on crude approximations of the Jacobian. Approximate factorizations based on a Jacobian that matches the target residual operator are unpopular because these preconditioners consume a large amount of memory and can suffer from robustness issues. However, these preconditioners remain appealing because they closely resemble A^-1.In this paper, it is shown that a jfnk solution method with an approximate factorization preconditioner based on a Jacobian that approximately matches the target residual operator enables a speed up of a factor 2.5-12 over nonlinear multigrid for two-dimensional high Reynolds number flows. The solution method performs equally well as nonlinear multigrid for three-dimensional laminar problems. A modest memory consumption is achieved with partly lumping the Jacobian before constructing the approximate factorization preconditioner, whereas robustness is ensured with enhanced diagonal dominance.     

Operations planning and scheduling problems in an FMS: An integrated approach

Operations planning and scheduling (OPS) problems in advanced manufacturing systems, such as flexible manufacturing systems, are composed of a set of interrelated problems, such as part-type batching, machine grouping, tool loading, routing, part input sequencing and on-line scheduling. In this paper, an integrated, simulation-based approach to the OPS problems is discussed. A detailed simulation model is developed using FORTRAN and SLAM II which integrates loading, part inputting, routeing and dispatching issues of the OPS. An experimental frame is developed which provides statistical analysis of the simulation output by developing an experimental design. Statistical analyses concerning a number of system parameters (e.g. loading strategies and scheduling rules) are performed on a set of performance measures.     

Visual Prolog的搜索控制机制分析

回溯机制是逻辑程序设计的重要设施。回溯本身是一种获得目标所有可能解的良好方法。然而回溯也有副作用,一是它可能导致Visual Prolog给出多余的答案,而Visual Prolog自己不能区分实质上相同的两个解,因此会降低效率;二是尽管一个特殊的目标已被满足,但是回溯机制可能还会强迫Visual Prolog继续手找另外的解,因此会增加系统开销。在这些情况下,必须仔细控制目标搜索求解的回溯过程。本文在揭示Visual Prolog回溯机制所存在问题的基础上,通过实例,对Visual Prolog的静态截断机制、失败谓词fail与否定谓词not等控制谓词,以及动态截断机制等所构成的完整的目标搜索求解控制机制进行了详细分析,从而揭示出回溯机制和搜索求解控制机制的本质特性及应用机理。     

Full prolog and scheduling or-parallelism in muse

Muse is a simple and efficient approach to Or-parallel implementation of the full Prolog language. It is based on havingmultiplesequential Prolog engines, each with its local address space, and some shared memory space. It is currently implemented on a number of bus-based and switch-based multiprocessors. The sequential SICStus Prolog system has been adapted to Or-parallel implementation with very low extra overhead in comparison with other approaches. The Muse performanhce results are very encouraging in absolute and relative terms.The Muse execution model and its performance results on two different multiprocessor machines for a parallel version of Prolog, named Commit Prolog, have been presented in previous papers. This paper discusses supporting the full Prolog language and describes mechanisms being developed for scheduling Or-parallelism in Muse. It also presents performance results of the Muse implementation on Sequent Symmetry after supporting full Prolog. The results show that the extra overhead associated with supporting the full Prolog language is negligible.     

Some Issues in Analysis and Specialization of Modular Ciao-Prolog Programs

Separating programs into modules is a well-known technique which has proven very useful in program development and maintenance. Starting by introducing a number of possible scenarios, in this paper we study different issues which appear when developing analysis and specialization techniques for modular logic programming. We discuss a number of design alternatives and their consequences for the different scenarios considered and describe where applicable the decisions made in the Ciao system analyzer and specializer. In our discussion we use the module system of Ciao Prolog. This is both for concreteness and because Ciao Prolog is a second-generation Prolog system which has been designed with global analysis and specialization in mind, and which has a strict module system. The aim of this work is not to provide a theoretical basis on modular analysis and specialization, but rather to discuss some interesting practical issues.The authors would like to thank Francisco Bueno for many interesting discussions on analysis of modular programs, and the anonymous referees for their constructive comments. This work was funded in part by Spanish CICYT projects TIC99-1151 EDIPIA and TIC97-1640-CE.     

Machine independent AND and OR parallel execution of logicprograms. II. Compiled execution

For pt.I. see ibid., p. 170-80. In pt.I, we presented a binding environment for the AND and OR parallel execution of logic programs. This environment was instrumental in rendering a compiler for the AND and OR parallel execution of logic programs machine independent. In this paper, we describe a compiler based on the Reduce-OR process model (ROPM) for the parallel execution of Prolog programs, and provide performance of the compiler on five parallel machines: the Encore Multimax, the Sequent Symmetry, the NCUBE 2, the Intel i860 hypercube and a network of Sun workstations. The compiler is part of a machine independent parallel Prolog development system built on top of a run time environment for parallel programming called the Chare kernel, and runs unchanged on these multiprocessors. In keeping with the objectives behind the ROPM, the compiler supports both on and independent AND parallelism in Prolog programs and is suitable for execution on both shared and nonshared memory machines. We discuss the performance of the Prolog compiler in some detail and describe how grain size can be used to deliver performance that is within 10% of the underlying sequential Prolog compiler on one processor, and scale linearly with increasing number of processors on problems exhibiting sufficient parallelism. The loose coupling between parallel and sequential components makes it possible to use the best available sequential compiler as the sequential component of our compiler     

非完全互连同构系统上的静态任务调度

在分布式内存多处理机DMM(distributed memory multiprocessor)系统中,不同处理机上运行的任务之间的通信开销仍然很大,有时甚至抵消了多处理机并行所带来的好处.为了使并行程序在DMM系统上能得以高效的执行,必须采用合理的调度技术将任务分配给处理机.文章首先分别给出了任务调度系统中的任务模型、处理机模型以及调度问题的形式化描述,然后在此基础上研究了任务调度中3个最重要的问题,即(1) 如何顺序选择参与调度的任务,(2) 如何选择路由,(3) 如何分配任务给处理机.其中,路由选择     

Parallel programming with logic languages: A survey

Formal properties of logic languages are largely studied; however, their impact on the practice of software design and programming is currently minimal. In this paper we survey some interesting representatives of the family of logic languages aiming at comparing the different capabilities they offer for designing and programming parallel systems. The logic languages Prolog, Aurora, Flat Concurrent Prolog, Parlog, GHC, and DeltaProlog were chosen, because a suitable set of relevant examples has been published, mostly by the language designers themselves. A number of sample programs is used to expose and compare the languages with respect to their object oriented programming capabilities for multiprocess coordination, interprocess communication, and resource management. Special attention is devoted also to metaprogramming as well, seen as a useful technique for specifying and building the operating environments of the languages themselves. The paper ends with a discussion on positive and negative features found comparing these languages, and indicates some guidelines to be followed in the design of new logic languages.     

Parallel execution of Prolog with granularity control

This paper presents a system for parallel execution of Prolog supporting both independent conjunctive and disjunctive parallelism. The system is intended for distributed memory architecture and is composed of a set of workers with a hierarchical structure scheduler. The execution model has been designed in such a way that each worker's environment does not contain references to terms in other environments, thus reducing communication overhead. In order to guarantee the improvement of the performance by the parallelism exploitation, a granularity control has been introduced for each kind of parallelism. For conjunctive parallelism PDP applies a control based on the estimation provided by CASLOG. The features of the system allow to introduce this control without adding overhead. For disjunctive parallelism PDP controls granularity by applying a heuristic-based method, which can be adapted to other parallel Prolog systems. Different scheduling policies have also been tested. The system has been implemented on a transputer network and performance results show that it provides a high speedup for coarse grain parallel programs.     

The Aurora or-parallel Prolog system

Aurora is a prototype or-parallel implementation of the full Prolog language for shared-memory multiprocessors, developed as part of an informal research collaboration known as the “Gigalips Project”. It currently runs on Sequent and Encore machines. It has been constructed by adapting Sicstus Prolog, a fast, portable, sequential Prolog system. The techniques for constructing a portable multiprocessor version follow those pioneered in a predecessor system, ANL-WAM. The SRI model was adopted as the means to extend the Sicstus Prolog engine for or-parallel operation. We describe the design and main implementation features of the current Aurora system, and present some experimental results. For a range of benchmarks, Aurora on a 20-processor Sequent Symmetry is 4 to 7 times faster than Quintus Prolog on a Sun 3/75. Good performance is also reported on some large-scale Prolog applications.     

An optimal resources scheduling strategy on multimedia cloud computing under multi- device constraint

In view of the problem of inaccurate scheduling by using traditional resource scheduling method, because the method is mainly based on extracting and classifying the resource features to make scheduling, ignoring the effect of the feature relevance between the resources on the scheduling results. This paper presents a model for multimedia cloud resource scheduling based on multi- device constraint. In this method the objective function is no longer constrained only by the CPU computing capacity and the minimized completion time, but to achieve a minimum time-consuming of CPU, memory and other peripherals operation are considered as the scheduling objectives. Then the utilization of solving constrained jointly is employed to obtain the mapping relationship of the optimal virtual and physical machine. Moreover, a regressive dimensionality reduction algorithm is designed for this scheduling model to solve the high dimensional problems aroused by multi-device constraints. Simulation results show that the improved algorithm has a better performance than the traditional algorithm, has a good efficiency and has a certain convergence.     

嵌入式DSP系统中SDF模型的层次化存储优化方法

在同步数据流模型(SDF)描述的嵌入式数字信号处理(DSP)系统中,计算体单一出现调度(SAS)算法对于存在反馈环和数据密集处理的应用不可解或内存优化效果很差.文中提出了将SAS和Non-SAS类型调度算法相结合的层次化的存储优化方法,定义了数据密集分量和强连通分量来描述环和数据密集处理结构,并依据数据优先消耗原则设计了启发式的Non-SAS调度算法对分量进行存储优化.该方法适用于任意SDF模型,并有良好的存储优化效果.实验结果证明了其有效性.     

Algebraic computational models of OR-parallel execution of Prolog

A specification of the OR-parallel execution of Prolog programs, using CHOCS (calculus of higher order communicating systems) [24], is presented in the paper. A translation is defined from Prolog programs and goals to CHOCS processes: the execution of the CHOCS process corresponding to a goal mimics the OR-parallel execution of the original Prolog goal. In the translation, clauses and predicate definitions of a Prolog program correspond to processes. To model OR-parallelism, the processes , corresponding to clauses (having the same head predicate ) start their execution concurrently, but, in order to respect the depth-first search rule, each is guarded by the termination of the executions of processes 's, . The computational model is proved correct with respect to the semantics of Prolog, as given in [4, 5]. Our model, because of its algebraic specification, can be easily used to prove properties of the parallel execution of Prolog programs. Moreover, the model exploits the maximum degree of parallelism, by giving the Prolog solutions in parallel, without any order among them. However, this model, being close to the Prolog semantics definition, contains sources of inefficiency which make it unpractical as a guide for the implementation. To overcome these problems, a new computational model is defined. This model is obtained by modifications of the basic one and thus its correctness can be easily proved. Finally, we show how to obtain models of different real implementations of OR-parallel Prolog by slight modification of the new model. The relations among all these models, in terms of parallelism degree, are studied by using the concepts of bisimulation and simulation, developed for concurrent calculi. Received: 5 May 1995 / 28 May 1996     

Communication infrastructure in distributed scheduling

The emergence of distributed artificial intelligent (DAI) introduced a new approach to solve scheduling problems by a set of scheduling systems that interact with each other in the problem-solving process. In this paper, we describe a communication infrastructure to handle connection and communication between distributed Internet scheduling systems for distributed applications. First, we present an agent model of distributed scheduling systems where agents can communicate and coordinate activities with each other via an agent communication language. Then, we define the syntax and semantics for the agent communication languages, and negotiation mechanism. Following that, we discuss the design and development of the prototype for the multi-agent scheduling systems. We conclude with a discussion of communication issues for heterogeneous agent-based scheduling systems to solve distributed scheduling problems.     

Prolog/Rex-a way to extend Prolog for better knowledgerepresentation

Prolog/Rex represents a powerful amalgamation of the latest techniques for knowledge representation and processing, rich in semantic features that ease the difficult task of encoding heterogeneous knowledge of real-world applications. The Prolog/Rex concept mechanism lets a user represent domain entities in terms of their structural and behavioral properties, including multiple inheritance, arbitrary user-defined relations among entities, annotated values (demons), incomplete knowledge, etc. A flexible rule language helps the knowledge engineer capture human expertise and provide flexible control of the reasoning process. Additional Prolog/Rex strength that cannot be found in any other hybrid language made on top of Prolog is language level support for keeping many potentially contradictory solutions to a problem, allowing possible solutions and their implications to be automatically generated and completely explored before they are committed. The same mechanism is used to model time-states, which are useful in planning and scheduling applications of Prolog/Rex     

Linux操作系统的实时化分析

随着实时操作系统的广泛应用和Linux的迅速发展，人们更加关注实时Linux的开发问题。文中，我们讨论了调度策略、内核的可重入性、中断处理以及内存管理机制等关键问题。这些问题与Linux扩展到实时操作系统密切相关。然后，我们详细分析了两个有代表性的实时Linux，即RT Linux和KURT Linux的主要实现。我们还介绍了它们自己的特性以及它们之间的基本差异。最后提出了未来的研究工作。