Parallel Algorithms and Applications 

About: Parallel Algorithms and Applications is an academic journal. The journal publishes majorly in the area(s): Parallel algorithm & Speedup. It has an ISSN identifier of 1026-7689. Over the lifetime, 374 publications have been published receiving 2318 citations.

A Library for doing polyhedral operations

Abstract: The design and implementation of a library of C-code procedures to perform operations on rational polyhedra is described. The library supports intersection, union, difference, simplification in context, convex hull, affine image, affine preimage, and computation of dual forms. Since not all of these functions are closed over polyhedra, the library is extended to operate on finite unions of polyhedra. The major design decisions made during the implementation of the library are discussed. The data structure used for representing finite unions of polyhedra is developed and validity rules for the representation of polyhedra are derived. And finally, the algorithms used to implement the various functions in the library are presented.

Explicit de-coupled group iterative methods and their parallel implementations

Abstract: In this paper we extend the 4-point explicit de-coupled group (EDG) iterative method, Abdullah (1991), to the 6 and 9-poinl EDG methods for the solution of elliptic partial differential equations. We will show graphically the technique of implementing the new grouping. Performance results for the algorithms are presented and a comparison with the 4-point scheme confirm the new groups to be computationally superior. Further, the implementations of the parallel 4, 6 and 9-point EDG methods on the Sequent Balance 8000 multiprocessor arc discussed and results from experiments performed are presented.

Matrix operations using arrays with reconfigurable optical buses

Abstract: This paper examines the possibility of implementing matrix operations on an array with reconfigurable optical buses (AROB). The AROB combines some of the advantages and characteristics of reconfigurable meshes and meshes with optical pipelined buses. This model is extremely flexible, as demonstrated by its ability to efficiently simulate CREW PRAMs and reconfigurable networks. A number of applications arc investigated and it is shown that many matrix operations can be implemented efficiently, reducing the time complexity and/or the cost of existing algorithms which are given for other models of parallel computation.

Convergence analysis of stochastic diffusion search

Abstract: In this paper we present a connectionist searching technique - the Stochastic Diffusion Search (SDS), capable of rapidly locating a specified pattern in a noisy search space. In operation SDS finds the position of the pre-specified pattern or if it does not exist - its best instantiation in the search space. This is achieved via parallel exploration of the whole search space by an ensemble of agents searching in a competitive cooperative manner. We prove mathematically the convergence of stochastic diffusion search. SDS converges to a statistical equilibrium when it locates the best instantiation of the object in the search space. Experiments presented in this paper indicate the high robustness of SDS and show good scalability with problem size. The convergence characteristic of SDS makes it a fully adaptive algorithm and suggests applications in dynamically changing environments.

Nested algorithmic skeletons from higher order functions

Abstract: Algorithmic skeletons provide a promising basis for the automatic utilisation of parallelism at sites of higher order function use through static program analysis. However, decisions about whether or not to realise particular higher order function instances as skeletons must be based on information about processing resources available at runtime In principle, nested higher order functions may be realised as nested skeletons. However, where higher order function arguments result from partially applied functions, free-variable bindings must be identified and communicated through the corresponding skeleton hierarchy to where those arguments are actually applied Here, a skeleton based parallelising compiler for Standard ML is presented. Hybrid skeletons, which can change from parallel to serial evaluation at runtime, are considered and mechanisms for their nesting are discussed. The main compilation stages are illustrated for simple examples. A nested higher order function based algorithm for multiplying matr...