A note on data-parallelism and (and-parallel) prolog

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

Much work has been done in the áreas of and-parallelism and data parallelism in Logic Programs. Such work has proceeded to a certain extent in an independent fashion. Both types of parallelism offer advantages and disadvantages. Traditional (and-) parallel models offer generality, being able to exploit parallelism in a large class of programs (including that exploited by data parallelism techniques). Data parallelism techniques on the other hand offer increased performance for a restricted class of programs. The thesis of this paper is that these two forms of parallelism are not fundamentally different and that relating them opens the possibility of obtaining the advantages of both within the same system. Some relevant issues are discussed and solutions proposed. The discussion is illustrated through visualizations of actual parallel executions implementing the ideas proposed

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

Irregular computations pose sorne 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 tutoríal way, sorne of the problems faced by parallelizing compilers for logic and constraint programs and provide pointers to sorne 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

Exploiting Recursion-Parallelism in Prolog

We exploit parallelism across recursion levels in a deterministic subset of Prolog. The implementation extends a convential Prolog machine with support for data sharing and process managment. Extensive global dataflow analysis is employed to facilitate parallelization. Promising performance figures, showing high parallel efficiency and low overhead for parallelization, have been obtained on a 24 processor shared-memory multiprocessor. 1 INTRODUCTION The Single Program Multiple Data (SPMD) model of parallel computation has recently received a lot of attention (see e.g. the article by Bell [1]). The model is characterized by each parallel process running the same program but with different data. 1 The attraction of this model is that it does not require a dynamic network of parallel processes: this facilitates efficient implementation and makes the parallel control-flow comprehensible for the programmer. We are concerned here with the SPMD model in the context of logic programming. F..