Coarse Grained Parallel Algorithms

各种研究领域都面临着计算困难和/或需要处理大量数据的问题所造成的障碍。例如，由于性能问题，一些关于蛋白质或基因序列的计算生物化学方法不能扩展到人类健康研究所需的数据集。并行计算通过增加可解决问题的规模来实现新的研究。除了基本的并行计算研究，该项目还研究了基于结构的药物设计和蛋白质-蛋白质相互作用预测的并行算法，这将使新的生物化学研究成为可能，以及数据立方体的并行算法，这将有助于实现下一代超大型数据仓库。

Various fields of research face barriers created by problems that are computationally hard and/or require processing of large amounts of data. For example, some computational biochemistry methods on protein or gene sequences can not be scaled up to data sets required for human health research because of performance problems. Parallel computing enables new research by increasing the size of solvable problems. In addition to fundamental parallel computing research, this project studies parallel algorithms for structure-based drug design and protein-protein interaction prediction that will enable new biochemistry research, as well as parallel algorithms for data cubes that will help enable the next generation of very large data warehouses.