We introduce a new reusable component for parallel programming, the double-scan skeleton. For this skeleton, we formulate and formally prove sufficient conditions under which the double-scan can be parallelized, and develop its efficient MPI implementation. The solution of a tridiagonal system of equations is considered as our case study. We describe how this application can be developed using the double-scan and report experimental results for both absolute performance and performance predictability of the skeleton-based solution.
We derive a provably correct, architecture-independent family of parallel implementations for a class of data-parallel algorithms, called DH (distributable homomorphisms). The implementations are well-structured SPMD programs with group-wise personalized all-to-all exchange, directly realizable in MPI. As a case study, we systematically adjust the mathematical specification of the Fast Fourier Transform (FFT) to the DH format and, thereby, obtain a generic SPMD implementation for FFT. The target program includes FFT solutions used in practice – the binary-exchange and the 2D- and SD-transpose – as special cases.
We address the challenging problem of algorithm and program design for the Computational Grid by providing the application user with a set of high-level, parameterised components called skeletons. We descrile a Java-based Grid programming system in which algorithmns are composed of skeletons and the computational resources for executing individual skeletons are chosen using performance prediction. The advantage of our approach is that skeletons are reusable for different applications and that skeletons' implementation can be tuned to particular machines. The focus of this paper is on predicting performance for Grid applications constructed using skeletons.
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