Paper Abstracts (2015)

Twentieth Conference of the Association of Christians in the Mathematical Science

A survey of parallel execution strategies for transitive closure and logic programs

An important feature of database technology of the nineties is the use of parallelism for speeding up the execution of complex queries. This technology is being tested in several experimental database architectures and a few commercial systems for conventional select-project-join queries. In particular, hash-based fragmentation is used to distribute data to disks under the control of different processors in order to perform selections and joins in parallel. With the development of new query languages, and in particular with the definition of transitive closure queries and of more general logic programming queries, the new dimension of recursion has been added to query processing. Recursive queries are complex; at the same time, their regular structure is particularly suited for parallel execution, and parallelism may give a high efficiency gain. We survey the approaches to parallel execution of recursive queries that have been presented in the recent literature. We observe that research on parallel execution of recursive queries is separated into two distinct subareas, one focused on the transitive closure of Relational Algebra expressions, the other one focused on optimization of more general Datalog queries. Though the subareas seem radically different because of the approach and formalism used, they have many common features. This is not surprising, because most typical Datalog queries can be solved by means of the transitive closure of simple algebraic expressions. We first analyze the relationship between the transitive closure of expressions in Relational Algebra and Datalog programs. We then review sequential methods for evaluating transitive closure, distinguishing iterative and direct methods. We address the parallelization of these methods, by discussing various forms of parallelization. Data fragmentation plays an important role in obtaining parallel execution; we describe hash-based and semantic fragmentation. Finally, we consider Datalog queries, and present general methods for parallel rule execution; we recognize the similarities between these methods and the methods reviewed previously, when the former are applied to linear Datalog queries. We also provide a quantitative analysis that shows the impact of the initial data distribution on the performance of methods

A General Framework for Static Cost Analysis of Parallel Logic Programs

The estimation and control of resource usage is now an important challenge in an increasing number of computing systems. In particular, requirements on timing and energy arise in a wide variety of applications such as internet of things, cloud computing, health, transportation, and robots. At the same time, parallel computing, with (heterogeneous) multi-core platforms in particular, has become the dominant paradigm in computer architecture. Predicting resource usage on such platforms poses a difficult challenge. Most work on static resource analysis has focused on sequential programs, and relatively little progress has been made on the analysis of parallel programs, or more specifically on parallel logic programs. We propose a novel, general, and flexible framework for setting up cost equations/relations which can be instantiated for performing resource usage analysis of parallel logic programs for a wide range of resources, platforms, and execution models. The analysis estimates both lower and upper bounds on the resource usage of a parallel program (without executing it) as functions on input data sizes. In addition, it also infers other meaningful information to better exploit and assess the potential and actual parallelism of a system. We develop a method for solving cost relations involving the max function that arise in the analysis of parallel programs. Finally, we instantiate our general framework for the analysis of logic programs with Independent AndParallelism, report on an implementation within the CiaoPP system, and provide some experimental results. To our knowledge, this is the first approach to the cost analysis of parallel logic programs

Or-Parallel Prolog Execution on Clusters of Multicores

Logic Programming languages, such as Prolog, provide an excellent framework for the parallel execution of logic programs. In particular, the inherent non-determinism in the way logic programs are structured makes Prolog very attractive for the exploitation of implicit parallelism. One of the most noticeable sources of implicit parallelism in Prolog programs is or-parallelism. Or-parallelism arises from the simultaneous evaluation of a subgoal call against the clauses that match that call. Arguably, the most successful model for or-parallelism is environment copying, that has been efficiently used in the implementation of or-parallel Prolog systems both on shared memory and distributed memory architectures. Nowadays, multicores and clusters of multicores are becoming the norm and, although, many parallel Prolog systems have been developed in the past, to the best of our knowledge, none of them was specially designed to explore the combination of shared with distributed memory architectures. Motivated by our past experience, in designing and developing parallel Prolog systems based on environment copying, we propose a novel computational model to efficiently exploit implicit parallelism from large scale real-world applications specialized for the novel architectures based on clusters of multicores

A Framework for Efficient Execution of Logic Programs.

The focus of this dissertation is to develop an efficient framework for sequential execution of logic programs. Within this framework the logic programs are executed by pruning the goal-search tree whenever applicable. Three new concepts for pruning of computation during execution of logic programs are introduced. (1) Failure-binding. A Failure-binding for a literal is a binding which when applied to the literal fails the goal obtained from the literal. Failure-bindings for a literal are identified by analyzing the goal-tree of a goal which is obtained from the literal. The failure-bindings for a literal are used for intelligent backtracking based on the generator-consumer approach. Intelligent backtracking based on failure-bindings prune the computation of search space which lead to late detection of failure. (2) Failure-solution. A Failure-solution of a goal is unacceptable to some other subgoal in the forward execution. Failure-solutions of a goal are identified by analyzing the history of computation, during execution. Failure-solutions of the goals are used for intelligent forward execution. Intelligent forward execution prunes the computation of search space which leads to repeated failure resulting from repeated successes of a goal. (3) Forward jumping. Forward jumping is a method to avoid reexecution of some subgoals after backtracking (instead of naive forward execution after backtracking). Forward jumping is based on the dynamic subgoal dependencies in a rule. Such jumping prunes the computation of the search spaces which leads to the same sequences of successes of subgoals after backtracking. To facilitate the implementation of these concepts a new data structure, called segmented-stack, is defined. The space complexity of a segmented stack is linear in the number of nodes in the stack. Depth-first search as well as breadth-first search are very easily implemented on a segmented-stack during execution of logic programs. Execution of logic programs on a segmented-stack allows association of the search space, as well as the solutions, of a goal with the frame of the goal. This enables implementation of intelligent backtracking, intelligent forward execution and forward jumping. The search based on each of these paradigms is proved to be sound and complete. It is also shown that the implementation of these paradigms preserves the order of results obtained by Prolog. The effects of the non-logical operators, in Prolog, on the paradigms are studied. The search based on the these paradigms is compared individually, and collectively, with the standard search by Prolog

Analyzing logic programs with dynamic scheduling

Traditional logic programming languages, such as Prolog, use a fixed left-to-right atom scheduling rule. Recent logic programming languages, however, usually provide more flexible scheduling in which computation generally proceeds leftto- right but in which some calis are dynamically "delayed" until their arguments are sufRciently instantiated to allow the cali to run efficiently. Such dynamic scheduling has a significant cost. We give a framework for the global analysis of logic programming languages with dynamic scheduling and show that program analysis based on this framework supports optimizations which remove much of the overhead of dynamic scheduling

Parallel execution of logic programs.

Ho-Fung Leung.Thesis (M.Ph.)--Chinese University of Hong Kong, 1988.Bibliography: leaves [2-6], 3rd group