Narrow Results

In this work we apply a methodology for the parallelization of algorithms for tridiagonal solvers. We classify tridiagonal solvers as a function of their data flows and present a unified version of the projection of these algorithms onto computers with mesh topology and distributed memory. Finally, we evaluate the algorithms and compare them through specific tests on the Fujitsu AP1000.

This paper presents a selection algorithm for finding the k-th largest element in the cartesian sum of two sets X and Y, each of size n, on a mesh connected model. The algorithm has a time complexity of , using P processors, where P < n². The algorithm is adaptive but away from cost optimality.

Finding a parallel architecture adapted to a given class of algorithms is a central problem for architects. This paper presents a methodology to realize it, and provides an illustration using image analysis. First, we show a set of common basic operations that can be used to solve most image analysis problems. Then these movements are translated to fit some natural communications in a given architecture. The considered data movements (global operations on connected pixel sets) can express a large class of algorithms. Their implementation on exemplary massively parallel architectures (arrays, hypercubes, pyramids) is discussed.

Finding a parallel architecture adapted to a given class of algorithms is a central problem for architects. This paper presents a methodology to realize it, and provides an illustration using image analysis. First, we show a set of common basic operations that can be used to solve most image analysis problems. Then these movements are translated to fit some natural communications in a given architecture. The considered data movements (global operations on connected pixel sets) can express a large class of algorithms. Their implementation on exemplary massively parallel architectures (arrays, hypercubes, pyramids) is discussed.