Automatic Restructuring for Parallel and Vector Computers

A problem which confronts the developers of concurrent logic programming (CLP) systems concerns the design of the programming environment, particularly the provision of debugging tools. Debugging tools are useful for many activities besides identifying bugs: they can help in program testing and demonstration, in software experiments, and in teaching the language semantics. For CLP languages the questions of what debugging tools should be provided, and of how they should be used, are still open. Although the languages are closely related to other logic programming and concurrent programming languages, they are sufficiently different that new debugging techniques are required. This paper describes a primarily channel-oriented debugging methodology and a set of debugging tools that we have developed in the light of our experience in using and teaching the CLP language Parlog. With these tools a programmer can test a program by observing communication on channels, opening up a process to examine the activity on internal channels and, if necessary, to check in detail the execution steps of a process. Almost all of the ideas, which are equally applicable to other CLP languages, have been implemented in two commercial Parlog systems. As well as fulfilling the needs of existing users of CLP languages, we believe that the tools emphasise the attractions of CLP as a concurrent programming paradigm, since they are made possible by the unique attributes of CLP.

This article discusses a serial and a parallel version of a heuristic algorithm for a solution to the traveling salesman problem, implementation of these algorithms, and their performance in terms of finding the most favorable solution, and an average solution. The algorithms implemented in C sequential programming language, and in Occam concurrent programming language were run on different computing platforms to study their impact on the comparative value of the solutions each platform provided. The programs were tested on reference data from TSPLIB data files, a collection of sample instances of traveling salesman problem. Experimental results, using asymmetric cost matrix, indicate good performance compared to the optimal solutions provided in the TSPLIB reference library.

Programming languages play an important role in the development of computer science. Just imagine programming an expert system in machine language! Any revision would require an enormous additional coding effort. It has been clear for some time that complex programming tasks require powerful programming environments on the conceptual level of the user. Such environments support abstract tools for numeric and symbolic computation. This trend led to the development of high-level programming languages like LISP and other languages like PLANNER (Hewitt 1972), CONNIVER (Sussman and McDermott 1972), QA4 (Rulifson et al. 1973), QLISP (Reboh and Sacerdoti 1973), and Prolog (Colmerauer et al. 1972). These more powerful languages are often collectively known as Programming in the AI languages differs substantially from programming in more standard environments. Edward Feigenbaum has characterized this difference by noting that a given language may be plotted along the what-to-how spectrum. At the end of the spectrum, programs specify step by step precisely how the desired computation is to be carried out. In contrast, at the end of the spectrum, programs are required merely to specify what conditions the desired result must satisfy. The AI languages are usually nearer the end of the spectrum than the more standard languages. A computation within an AI language is taken as the process of proving a goal: the user provides a goal and the system tries to provide a proof, relying on a knowledge base and built-in proof mechanisms. Therefore these languages are also known. as goal-directed computation languages. Robert Kowalski's 1974 paper Predicate Logic as Programming Language introduced logic programming to the computing community as a technique for specifying goal-directed computations. Prior to Kowalski's 1974 paper, various research efforts during the late 1960s and early 1970s influenced the development of logic programming. Its theoretical foundations were established through research in automatic theorem proving dating back to the early 1960s. The first implementation of a logic programming system was described in Colmerauer et al. (1972). These developments occurred concurrently with, and partly in response to, the development of PLANNER (Hewitt 1972) and CONNIVER (Sussman and McDermott 1972) at M.I.T. This logic programming technique consists of formulating a set of rules and facts (collectively called clauses), each of which is a simple statement of some truth that one wishes to record in a knowledge base. A statement has the form

Multi-core processors are present in everyone's daily life. Consequently, concurrent programming has reemerged as a pressing concern for everyone interested in exploring all the potential computational power in these machines. But, the emergence of new concurrency models and programming languages also brings new challenges in terms of how one can deal with abnormal occurrences, much due to the heterogenous parallel control flow. Unexpectedly, sequential Exception Handling models remain as the most used tool for robustness, even in the most recent concurrent programming languages. Though, the appearance of more complex models, such as programming languages with implicit concurrency, might pose a challenge too big for these sequential mechanisms. In this article we will provide evidences why such models are not generally suited to deal with faults in programs with implicit concurrency and, in the light of more recent advances in concurrent Exception Handling, we will discuss the attributes of a model for addressing this problem.

A practical formulation of parallel programming languages is presented based on an analogy with sequential programming languages. The essential components are a machine model, a programming model and a programming language. For the parallel case, the CTA is used as the machine model, Phase Abstractions are used as the programming model, and the Orca family of languages are used to illustrate language design.

We address the problem of specifying concurrent processes that can make local nondeterministic decisions without affecting global system behavior—the sequence of events communicated along each inter-process communication channel. Such nondeterminism can be used to cope with unpredictable execution rates and communication delays. Our model resembles Kahn’s, but does not include unbounded buffered communication, so it is much simpler to reason about and implement. After formally characterizing these so-called confluent processes, we propose a collection of operators, including sequencing, parallel, and our own creation, confluent choice, that guarantee confluence by construction. The result is a set of primitive constructs that form the formal basis of a concurrent programming language for both hardware and software systems that gives deterministic behavior regardless of the relative execution rates of the processes. Such a language greatly simplifies the verification task because any correct implementation of such a system is guaranteed to have the same behavior, a property rarely found in concurrent programming environments.

This paper describes the design and the implementation of OOPE, the graphical programming environment of the prototype version of O2, an object-oriented database system. One of the distinguishing features of this environment is that it mixes the functionalities of programming environments, of both databases and programming languages. Thus, it facilitates and fastens not only the schema design, but also the development of application programs. Another interesting characteristic is that it is being developed using as much as possible the functionalities provided by the O2 system, namely the programming language, the database and the graphics functionalities.

This is a description of the single-user operating system Solo written in the programming language Concurrent Pascal. It supports the development of Sequential and Concurrent Pascal programs for the PDP 11/45 computer. Input/output are handled by concurrent processes. Pascal programs can call one another recursively and pass arbitrary parameters among themselves. This makes it possible to use Pascal as a job control language. Solo is the first major example of a hierarchical concurrent program implemented in terms of abstract data types (classes, monitors and processes) with compile-time control of most access rights. It is described here from the user’s point of view as an introduction to another paper describing its internal structure.

Concurrency has been with us almost from the beginning of computing. Managing and programming for concurrency is a difficult problem and various solutions have been suggested over the years. Debates on message passing vs. remote procedure call, synchronous vs. asynchronous message passing, bounded vs. unbounded buffers, active vs. passive objects etc. still continue. No solution is entirely satisfactory. Concurrent programming usually depends heavily on the nature of the problem at hand and the architecture of the target machine.

Defects in software cost money and sometimes lives. Even with rigorous testing, there are countless ways for programs to go wrong. Testing does not guarantee that a given program is correct for every input. Concurrent program testing does not guarantee that a program is correct for the example inputs. Formal program verification has been used as a technique to ensure program correctness for several years. It analyses the properties of the code and ensures that nothing goes wrong. In this thesis, a formal verification tool is designed and implemented based on Boogie IVL (Intermediate Verification Language) for a multi-threaded and object-oriented language named HARPO (HARdware Parallel Objects). We have designed the specific annotations intended only for use in verification. These annotations help the HARPO verifier to translate the program into an equivalent Boogie code. The resulting Boogie code is checked with theorem provers to verify the correctness of the HARPO program. Boogie code generation is tested using unit testing, and some of the case studies are reported. Consequently, programmers can use the HARPO verifier for verification of their concurrent programs.

Saraswat recently introduced the framework of concurrent constraint programming [14]. The essence of the framework is that computations consist of concurrent agents interacting by communicating constraints. Several concurrent constraint programming languages have been defined. They differ in the kinds of constraints that can be used as well as the kinds of operations on constraints which are available. In this paper we introduce a very simple concurrent constraint language we call Lucy, designed to closely mimic the actor model of computation. Agents can communicate only by the posting of constraints upon bags (un-ordered collections possibly with duplicate elements). This very impoverished concurrent constraint language is a syntactic subset of Janus, a concurrent constraint language which closely resembles concurrent logic programming languages such as Guarded Horn Clauses [21], Strand [5], Parlog [2] and Flat Concurrent Prolog [13]. By identifying the subset of Janus which is an actor language, we elucidate the relationship between actors and concurrent logic programming (and its generalization as concurrent constraint programming). Lucy is best not thought of as a unification of logic and constraint programming with actor and object-oriented programming, but as the missing link between these programming language genera.

article Free Access Share on Software support for heterogeneous computing Authors: Howard Jay Siegel Purdue University, West Lafayette, Indiana Purdue University, West Lafayette, IndianaView Profile , Henry G. Dietz Purdue University, West Lafayette, Indiana Purdue University, West Lafayette, IndianaView Profile , John K. Antonio Texas Tech University, Lubbock Texas Tech University, LubbockView Profile Authors Info & Claims ACM Computing SurveysVolume 28Issue 1March 1996 pp 237–239https://doi.org/10.1145/234313.234411Published:01 March 1996Publication History 21citation711DownloadsMetricsTotal Citations21Total Downloads711Last 12 Months67Last 6 weeks17 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Verification is routinely used when developing computer hardware and concurrent programs. A sequential program can always be tested and retested, but the nondeterministic nature of hardware and concurrent programs limits the effectiveness of testing as a method to demonstrate that the system is correct. Slight variations in timing, perhaps caused by congestion on a network, mean that two executions of the same program might give different results. Even if a bug is found by testing and then fixed, we have no way of knowing if the next test runs correctly because we fixed the bug or because the execution followed a different scenario, one in which the bug cannot occur.

Programming models and environments play the essential roles in high performance computing of enabling the conception, design, implementation and execution of science and engineering application codes. Programmer productivity is strongly influenced by the effectiveness of our programming models and environments, as is software sustainability since our codes have lifespans measured in decades, so the advent of new computing architectures, increased concurrency, concerns for resilience, and the increasing demands for high-fidelity, multi-physics, multi-scale and data-intensive computations mean that we have new challenges to address as part of our fundamental R&D requirements. Fortunately, we also have new tools and environments that make design, prototyping and delivery of new programming models easier than ever. The combination of new and challenging requirements and new, powerful toolsets enables significant synergies for the next generation of programming models and environments R&D. This report presents the topics discussed and results from the 2014 DOE Office of Science Advanced Scientific Computing Research (ASCR) Programming Models & Environments Summit, and subsequent discussions among the summit participants and contributors to topics in this report.

Prograph is a programming environment and language which has been available on the Apple Macintosh platform for more than 5 years. It provides a sophisticated application builder, together with a visual programming language, supported by a powerful program development environment. The programming language uses an object-oriented model for data abstraction and the logic is based on a dataflow model of computation, specified graphically.Graphical dataflow gives programmers a clear view of the potential for exploitation of concurrency and so the Prograph language appears to give some leverage for the programming of parallel or distributed systems. This still leaves many issues unresolved, however, since for example dataflow is usually associated with fine-grain parallelism, but expected target architectures are unlikely to support fine-grain parallelism efficiently. This paper discusses the preliminary investigation of the issues of Prograph and parallelism.KeywordsParallel EvaluationApple MacintoshDataflow GraphDataflow ModelRemote ExecutionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.