The following information is out of date. It will be updated during the first two weeks of October. Computational models in science and engineering make a significant impact on the advancement of many fields of research. Parallel computing has enhanced the ability to address these problems by developing new applications and paradigms.
Scientific problems are often irregular, large and computationally intensive.
Parallel algorithms that are employed in finding solutions to these problems and their efficient implementations play an important role in the advancement of science. Highly scalable implementations can now make simulations of computational models possible. Theoretical and experimental research in algorithms for parallel computing machines has as an objective to evaluate the effectiveness of scalable parallel computing systems across a very broad range of scientific applications and to identify the nature and degree of factors that impede efficiency. The goal of algorithm mapping is to minimize the factors that contribute to performance degradation, such as communication, load imbalance and overhead. Furthermore, the goal of minimizing communication may be modeled as a graph partitioning problem.