Parallel algorithms for multi-core & many-core processor clusters and applications in online analytical processing (OLAP) and computational biology

并行计算研究的目的是为解决数据密集型和/或计算困难的问题创造有利的技术。处理器集群，包括多核和/或众核（GPU）处理器的集群，已经成为首选架构，因为它们是由商用硬件构建的，并且具有良好的性价比。然而，对于许多问题，还没有有效的并行算法和并行软件，实现令人满意的加速比和可扩展性。该研究计划的重点是高效并行算法的设计和实现，并行算法的理论分析和当前并行架构上观察到的性能之间的相互关系，以及使用高效并行算法进行并行在线分析处理（OLAP）和并行计算生物学与生物信息学。

The aim of parallel computing research is to create enabling technology for solving data intensive and/or computationally hard problems. Processor clusters, including clusters of multi-core and/or many-core (GPU) processors, have emerged as architectures of choice because they are built from commodity hardware and have a good price performance ratio. However, for many problems there exist no efficient parallel algorithms and parallel software that achieve satisfactory speedup and are scalable. This research program is focused on the design and implementation of efficient parallel algorithms, the interrelationship between the theoretical analysis of parallel algorithms and the performance observed on current parallel architectures, and the use of efficient parallel algorithms for parallel online analytical processing (OLAP) and parallel computational biology & bioinformatics.