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

并行计算研究的目标是创建使能技术来解决数据密集型和/或计算困难的问题。处理器集群，包括多核和/或多核(GPU)处理器集群，已经成为可选的体系结构，因为它们由商用硬件构建而成，具有良好的性价比。然而，对于许多问题，目前还没有有效的并行算法和并行软件来获得令人满意的加速比和可扩展性。该研究项目致力于高效并行算法的设计和实现，并行算法的理论分析与在现有并行体系结构上观察到的性能之间的相互关系，以及高效并行算法在并行在线分析处理(OLAP)和并行计算生物学和生物信息学中的使用。

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.