DPSKEL: A Skeleton Based Tool for Parallel Dynamic Programming

Peláez, Ignacio; Almeida, Francisco; Suárez, Fernando

doi:10.1007/978-3-540-68111-3_117

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…For object-oriented programming languages, the MALLBA [23] and DPSKEL [24] frameworks also showed relevant speedups in the parallel evaluation of combinatorial optimization benchmarks. MALLBA tackles the resolution of combinatorial optimization problems using algorithmic skeletons implemented in C++.…”

Section: Related Workmentioning

confidence: 99%

On scaling dynamic programming problems with a multithreaded tabling Prolog system

Areias

Rocha

2017

Journal of Systems and Software

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Tabling is a powerful implementation technique that improves the declarativeness and expressiveness of traditional Prolog systems in dealing with recursion and redundant computations. It can be viewed as a natural tool to implement dynamic programming problems, where a general recursive strategy divides a problem in simple sub-problems that are often the same. When tabling is combined with multithreading, we have the best of both worlds, since we can exploit the combination of higher declarative semantics with higher procedural control. However, at the engine level, such combination for dynamic programming problems is very difficult to exploit in order to achieve execution scalability as we increase the number of running threads. In this work, we focus on two well-known dynamic programming problems, the Knapsack and the Longest Common Subsequence problems, and we discuss how we were able to scale their execution by using the multithreaded tabling engine of the Yap Prolog system. To the best of our knowledge, this is the first work showing a Prolog system to be able to scale the execution of multithreaded dynamic programming problems. Our experiments also show that our system can achieve comparable or even better speedup results than other parallel implementations of the same problems.

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Section: Related Workmentioning

confidence: 99%

On scaling dynamic programming problems with a multithreaded tabling Prolog system

Areias

Rocha

2017

Journal of Systems and Software

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“…There are also interesting software approaches derived from laboratories that apply tools such as LINGO to DP problems using specific methods. Dynamic Programming Skeletons (DPSKEL) is presented in . This is a parallel skeleton, where a large variety of optimal specifications are defined for DP problems in various architectures.…”

Section: Dynamic Programming Technique: Proof Of Conceptmentioning

confidence: 99%

“…In , we proposed the use of DPSKEL skeletons to offset the dearth of general software DP (sequential and parallel) tools. Our aim was to bridge the obvious gap existing between general methods and DP applications.…”

Section: Design Of a Dynamic Programming Frameworkmentioning

confidence: 99%

High‐level specifications for automatically generating parallel code

Acosta

Almeida

Peláez

2012

Concurrency and Computation

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SUMMARYThe arrival of multicore systems, along with the speed‐up potential available in graphics processing units, has given us unprecedented low‐cost computing power. These systems address some of the known architecture problems but at the expense of considerably increased programming complexity. Heterogeneity, at both the architectural and programming levels, poses a great challenge to programmers. Many proposals have been put forth to facilitate the job of programmers. Leaving aside proposals based on the development of new programming languages because of the effort this represents for the user (effort to learn and reuse code), the remaining proposals are based on transforming sequential code into parallel code, or on transforming parallel code designed for one architecture into parallel code designed for another. A different approach relies on the use of skeletons. The programmer has available set of parallel standards that comprise the basis for developing parallel code while programming sequential code. In this context, we propose a methodology for developing an automatic source‐to‐source transformation in a specific domain. This methodology is instantiated in a framework aimed at solving dynamic programming problems. Using this framework, the final user (a physician, mathematician, biologist, etc.) can express her problem using an equation in Latex, and the system will automatically generate the optimal parallel code for homogeneous or heterogeneous architectures. This approach allows for great portability toward these new emerging architectures and for great productivity, as evidenced by the computational results.Copyright © 2012 John Wiley & Sons, Ltd.

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“…For functional programming languages, the Eden (Loogen et al 2005) and HDC (Herrmann and Lengauer 2000) Haskell-based frameworks allow the users to express their programs using polymorphic higher order functions. For objectoriented programming languages, MALLBA (Alba et al 2002) and DPSKEL (Peláez et al 2007) frameworks also showed relevant speedups in the parallel evaluation of combinatorial optimization benchmarks.…”

Section: Introductionmentioning

confidence: 98%

Table space designs for implicit and explicit concurrent tabled evaluation

Areias¹,

Rocha²

2018

Theory and Practice of Logic Programming

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One of the main advantages of Prolog is its potential for the implicit exploitation of parallelism and, as a high-level language, Prolog is also often used as a means to explicitly control concurrent tasks. Tabling is a powerful implementation technique that overcomes some limitations of traditional Prolog systems in dealing with recursion and redundant subcomputations. Given these advantages, the question that arises is if tabling has also the potential for the exploitation of concurrency/parallelism. On one hand, tabling still exploits a search space as traditional Prolog but, on the other hand, the concurrent model of tabling is necessarily far more complex, since it also introduces concurrency on the access to the tables. In this paper, we summarize Yap's main contributions to concurrent tabled evaluation and we describe the design and implementation challenges of several alternative table space designs for implicit and explicit concurrent tabled evaluation that represent different tradeoffs between concurrency and memory usage. We also motivate for the advantages of using fixed-size and lock-free data structures, elaborate on the key role that the engine's memory allocator plays on such environments, and discuss how Yap's mode-directed tabling support can be extended to concurrent evaluation. Finally, we present our future perspectives toward an efficient and novel concurrent framework which integrates both implicit and explicit concurrent tabled evaluation in a single Prolog engine.

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DPSKEL: A Skeleton Based Tool for Parallel Dynamic Programming

Cited by 8 publications

References 12 publications

On scaling dynamic programming problems with a multithreaded tabling Prolog system

On scaling dynamic programming problems with a multithreaded tabling Prolog system

High‐level specifications for automatically generating parallel code

Table space designs for implicit and explicit concurrent tabled evaluation

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