This paper compares the performance of three programming paradigms for the parallelization of nested loop algorithms onto SMP clusters. More specifically, we propose three alternative models for tiled nested loop algorithms, namely a pure message passing paradigm, as well as two hybrid ones, that implement communication both through message passing and shared memory access. The hybrid models adopt an advanced hyperplane scheduling scheme, that allows both for minimal thread synchronization, as well as for pipelined execution with overlapping of computation and communication phases. We focus on the experimental evaluation of all three models, and test their performance against several iteration spaces and parallelization grains with the aid of a typical micro-kernel benchmark. We conclude that the hybrid models can in some cases be more beneficial compared to the monolithic pure message passing model, as they exploit better the configuration characteristics of an hierarchical parallel platform, such as an SMP cluster.

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This paper presents an overview of our work, concerning a complete end-to-end framework for automatically generating message passing parallel code for tiled nested for-loops. It considers general parallelepiped tiling transformations and general convex iteration spaces. We address all problems regarding both the generation of sequential tiled code and its parallelization. We have implemented our techniques in a tool which automatically generates MPI parallel code and conducted several series of experiments, concerning the compilation time of our tool, the efficiency of the generated code and the speedup attained on a cluster of PCs. Apart from confirming the value of our techniques, our experimental results show the merit of general parallelepiped tiling transformations and verify previous theoretical work on scheduling-optimal tile shapes.

Abstract.A key process in association rules mining, which has attracted a lot of interest during the last decade, is the discovery of frequent sets of items in a database of transactions. A number of sequential algorithms have been proposed that accomplish this task. In this paper we study the parallelization of the partial-support-tree approach (Goulbourne, Coenen, Leng, 2000). Results show that this method achieves a generally satisfactory speedup, while it is particularly adequate for certain types of datasets.