Parallel Algorithms and Systems for Applications in Data Analytics

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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 DataModern 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 YARNMany 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 AnalyticsProtein-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).