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 nondeterminism, 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 article 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 article describes the major techniques used for shared memory implementation of Or-parallelism, And-parallelism, 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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Parallel execution of prolog programs: a survey

This dissertation presents the design, implementation, and evaluation of a system that exploits fine-grain implicit parallelism in concurrent constraint programming language. The system is able to outperform a C implementation of an algorithm with complex dependencies without any user annotations. The concurrent constraint programming language AKL is used as a source programming language. A program is divided during runtime into tasks that are distributed over available processors. The system is unique in that it handles both and-parallel execution of goals as well as or-parallel execution of encapsulated search. A parallel binding scheme for a hierarchical constraint store is presented. The binding scheme allows encapsulated search to be performed in parallel. The design is justified with empirical data from the implementation. The scheme is the most efficient parallel scheme yet presented for deep concurrent constraint systems. The system was implemented on a high-performance shared-memory multiprocessor. Extensive measurements were done on the system using both smaller benchmarks as well as real-life programs. The evaluation includes detailed instruction-level simulation, including cache-performance, to explain the behavior of the system.

Exploiting Fine-Grain Parallelism in Concurrent Constraint Languages

Irregular computations pose some of the most interesting and challenging problems in automatic parallelization. Irregularity appears in certain kinds of numerical problems and is pervasive in symbolic applications. Such computations often use dynamic data structures, which make heavy use of pointers. This complicates all the steps of a parallelizing compiler, from independence detection to task partitioning and placement. Starting in the mid 80s there has been significant progress in the development of parallelizing compilers for logic programming (and more recently, constraint programming) resulting in quite capable parallelizers. The typical applications of these paradigms frequently involve irregular computations, and make heavy use of dynamic data structures with pointers, since logical variables represent in practice a well-behaved form of pointers. This arguably makes the techniques used in these compilers potentially interesting. In this paper, we introduce in a tutorial way, some of the problems faced by parallelizing compilers for logic and constraint programs and provide pointers to some of the significant progress made in the area. In particular, this work has resulted in a series of achievements in the areas of inter-procedural pointer aliasing analysis for independence detection, cost models and cost analysis, cactus-stack memory management, techniques for managing speculative and irregular computations through task granularity control and dynamic task allocation (such as work-stealing schedulers), etc.

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Parallelizing irregular and pointer-based computations automatically: perpectives from logic and constraint programming

Relating data-parallelism and (and-) parallelism in logic programs

We present a technique to estimate accurate speedups for parallel logic programs with relative independence from characteristics of a given implementation or underlying parallel hardware. The proposed technique is based on gathering accurate data describing one execution at run-time, which is fed to a simulator. Alternative schedulings are then simulated and estimates computed for the corresponding speedups. A tool implementing the aforementioned techniques is presented, and its predictions are compared to the performance of real systems, showing good correlation.

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IDRA (IDeal Resource Allocation): Computing Ideal Speedups in Parallel Logic Programming

Most of the research, to date, on optimizing program transformations for declarative languages has focused on sequential execution strategies. In this paper, we consider a class of commonly encountered computations whose \natural" speciica-tion is essentially sequential, and show how algebraic properties of the operators involved can be used to transform them into divide-and-conquer programs that are considerably more eecient, both in theory and in practice, on parallel machines.

Mudita Jain

Papers

A simple program transformation for parallelism