Parallel Algorithms and Systems for Applications in Data Analytics

并行计算研究的主要目标是为解决自然科学、工程科学、医学科学、商业科学和社会科学中的数据密集型和/或计算难题创造支持技术。我建议的研究课题是：***(1)高速数据实时数据聚合的并行算法和系统***现代数据分析系统严重依赖于数据聚合，通常以二进制关联聚合查询（例如，sum或max）对存储在数据库中的特定数据项子集实现。传统事务处理系统的查询通常只访问数据库的一小部分（例如，更新客户记录），与此相反，数据分析的聚合查询可能需要聚合数据库的大部分（例如，计算某类商品的总销售额作为时间的函数）。对于大型数据集，这可能会导致严重的性能问题。此外，在高速数据流（例如股票交易数据流）中持续监控新事件的应用程序需要能够在流数据到达时实时分析流数据。我们建议研究混合并行架构（由具有cpu和gpu的计算节点组成的集群/云）在高速数据上的实时数据聚合的使用。***(2)自动调优YARN***许多数据分析应用程序都是建立在关键技术之上的，比如Hadoop map-reduce和Spark，它们都使用YARN作为资源管理器。在给定的硬件平台上安装这样的系统需要调优许多参数。手动调优通常会导致次优和脆弱的性能，因为对一个作业（输入数据集）最优的参数可能不太适合另一个作业。自动调优并行系统是可移植的系统，可以自动适应不同和/或不断变化的硬件配置和输入数据集。我们建议研究如何为云架构自动调优YARN。***(3)蛋白质分析的并行算法和系统***蛋白质-蛋白质相互作用（PPIs）是定义细胞生物学的基本分子相互作用。PPIs被认为涉及药物发现的重要靶点，并与许多细胞状况和疾病有关。用一组给定的PPIs（药物靶点）设计合成蛋白质被称为蛋白质工程。我们提出为大规模混合并行架构（由cpu和gpu组成的计算节点组成的集群/云）开发一种新的并行蛋白质工程系统。

The main goal of parallel computing research is to create enabling technology for solving data intensive and/or computationally hard problems in the Natural Sciences, Engineering, Medical Sciences, Business, and Social Sciences. My proposed research topics are: ***(1) Parallel Algorithms And Systems For Real-Time Data Aggregation On High Velocity Data***Modern data analytics systems rely heavily on data aggregation typically implemented as binary associative aggregation queries (for example, sum or max) over a specified subset of the data items stored in the database. In contrast to queries for traditional transaction processing systems which typically access only a small portion of the database (e.g. update a customer record), aggregation queries for data analytics may need to aggregate large portions of the database (e.g. calculate the total sales of a certain type of items as a function of time). This can lead to significant performance issues for large data sets. In addition, applications that continuously monitor new events in high velocity data streams (e.g. stock exchange data streams) require the ability to analyze streaming data as it arrives, in real-time. We propose to study the use of hybrid parallel architectures (clusters/clouds comprised of compute nodes with CPUs and GPUs) for real-time data aggregation on high velocity data.***(2) Auto-Tuning YARN***Many data analytics applications are built on top of key technologies such as Hadoop map-reduce and Spark, both of which use YARN as resource manager. The installation of such systems on a given hardware platform involves tuning many parameters. Manual tuning often results in sub-optimal and brittle performance because parameters that are optimal for one job (input data set) may not be well suited to another. Auto-tuned parallel systems are portable systems that adapt automatically to different and/or changing hardware configurations and input data sets. We propose to study how to auto-tune YARN for cloud architectures. ***(3) Parallel Algorithms And Systems For Protein Analytics***Protein-protein interactions (PPIs) are essential molecular interactions that define the biology of a cell. PPIs are thought to involve important targets for drug discovery and are linked to a number of cellular conditions and diseases. Designing synthetic proteins with a given set of PPIs (drug targets) is called protein engineering. We propose to develop a new parallel protein engineering system for large scale hybrid parallel architectures (clusters/clouds comprised of compute nodes with CPUs and GPUs).