Distributed memory implementation of KLIC

Rokusawa, Kazuaki; Nakase, Akihiko; Chikayama, Takashi

doi:10.1007/bf03037484

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…Experience has shown that process-based system lose out on both the above counts. Similar accounts have been reported also in the context of committed choice languages (where the notion of process-based matches well with the view of each subgoal as an individual process which is enforced by the concurrent semantics of the language)-indeed the fastest parallel implementation of committed choice languages (e.g., [Crammond 1992;Rokusawa et al 1996]) rely on a processor-based implementation. In the context of Prolog, the presence of backtracking makes the process model too complex for non-deterministic parallel logic programming.…”

Section: Implementation and Efficiency Issuessupporting

confidence: 72%

Parallel execution of prolog programs

Gupta

Pontelli

Ali

et al. 2001

ACM Trans. Program. Lang. Syst.

116

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Since the early days of logic programming, researchers in the field realized the potential for exploitation of parallelism present in the execution of logic programs. Their high-level nature, the presence of non-determinism, and their referential transparency, among other characteristics, make logic programs interesting candidates for obtaining speedups through parallel execution. At the same time, the fact that the typical applications of logic programming frequently involve irregular computations, make heavy use of dynamic data structures with logical variables, and involve search and speculation, makes the techniques used in the corresponding parallelizing compilers and run-time systems potentially interesting even outside the field. The objective of this paper is to provide a comprehensive survey of the issues arising in parallel execution of logic programming languages along with the most relevant approaches explored to date in the field. Focus is mostly given to the challenges emerging from the parallel execution of Prolog programs. The paper describes the major techniques used for shared memory implementation of Or-parallelism, Andparallelism, and combinations of the two. We also explore some related issues, such as memory management, compile-time analysis, and execution visualization.

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Section: Implementation and Efficiency Issuessupporting

confidence: 72%

Parallel execution of prolog programs

Gupta

Pontelli

Ali

et al. 2001

ACM Trans. Program. Lang. Syst.

116

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“…KLIC achieves these goals by compiling into C. On one processor running a series of representative benchmarks, the performance of KLIC approaches that of C and C++ implementations. The distributed implementation of KLIC does distributed uni cation Rokusawa et al 1996 , including binding variables to variables. However, the algorithm has several curious properties: binding cycles can be created when binding variables to variables; inconsistencies are ignored; and a variable may be bound to di erent values on di erent sites.…”

Section: Concurrent Logic Languagesmentioning

confidence: 99%

Efficient logic variables for distributed computing

Haridi

Roy

Brand

et al. 1999

ACM Trans. Program. Lang. Syst.

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We de ne a practical algorithm for distributed rational tree uni cation and prove its correctness in both the o -line and on-line cases. We derive the distributed algorithm from a centralized one, showing clearly the trade-o s between local and distributed execution. The algorithm is used to realize logic variables in the Mozart Programming System, which implements the Oz language see http: www.mozart-oz.org. Oz appears to the programmer as a concurrent object-oriented language with data ow synchronization. Logic variables implement the data ow behavior. We show that logic variables can easily be added to the more restricted models of Java and ML, thus providing an alternative w ay to do concurrent programming in these languages. We present common distributed programming idioms in a network-transparent w ay using logic variables. We show that in common cases the algorithm maintains the same message latency as explicit message passing. In addition, it is able to handle uncommon cases that arise from the properties of latency tolerance and third-party independence. This is evidence that using logic variables in distributed computing is bene cial at both the system and language levels. At the system level, they improve latency tolerance and third-party independence. At the language level, they help make networktransparent distribution practical. Permission to make digital hard copy of all or part of this material without fee is granted provided that the copies are not made or distributed for pro t or commercial advantage, the ACM copyright server notice, the title of the publication, and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery, Inc. ACM. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior speci c permission and or a fee. Haridi et al. 1997;Smolka et al. 1995 . Logic variables express dependencies between computations without imposing an execution order. This property can be exploited in distributed computing: |Two basic concerns in distributed computing are latency tolerance and thirdparty independence. We s a y a program is third-party independent if its execution is una ected by sites that are not currently involved in the execution. We show that using logic variables instead of explicit message passing can reduce the e ect of both concerns with little programming e ort. |With logic variables we can express common distributed programming idioms in a network-transparent manner that results in optimal or near-optimal message latency. That is, the same idiom that works well in a centralized setting also works well in a distributed setting. The main contribution of this article is a practical algorithm for distributed rational tree uni cation that realizes these bene ts. The algorithm is used to implement logic variables in the Mozart system. We formally de ne the algorithm and prove that it satis es safety and liveness properties in both the o -line and on-line cases.From the programmer's point of view,...

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“…More recent work includes the ICOT KLIC system [3], which translates KL1 code into portable C code, achieving excellent performance. Uniprocessor and distributed-memory versions [13] have been released. The jc Janus system is a similar uniproeessor-based, high-performance implementation [7].…”

Section: Related Workmentioning

confidence: 99%

Super Monaco: Its portable and efficient parallel runtime system

Larson

Massey

Tick

1995

Lecture Notes in Computer Science

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Super Monaco" is the successor to Monaco, a shared-memory multiprocessor implementation of a flat concurrent logic programming language. While the system retains, by-and-large, the older Monaco compiler and intermediate abstract machine, the intermediate code translator and the runtime system have been completely replaced, incorporating a number of new features intended to improve robustness, flexibility, maintainability, and performance. There are currently two native-code backends for 80x86-based and MIPS-based multiprocessors. The runtime system, written in C, improves upon its predecessor with better memory utilization and garbage collection, and includes new features such as an efficient termination scheme and a novel variable binding and hooking mechanism. The result of this organization is a portable system 1 which is robust, extensible, and has performance competitive with Cbased systems. This paper describes the design choices made in building the system and the interfaces between the components.

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Distributed memory implementation of KLIC

Cited by 9 publications

References 7 publications

Parallel execution of prolog programs

Parallel execution of prolog programs

Efficient logic variables for distributed computing

Super Monaco: Its portable and efficient parallel runtime system

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