Parallel Framework for Data-Intensive Computing with XSEDE
Event Type
Student Poster
TimeTuesday, July 306:30pm - 8:30pm
LocationCrystal Foyer and Crystal B
DescriptionWith the increase in data-driven analytics, the demand for high performing computing resources has risen. There are many high-performance computing centers providing cyberinfrastructure (CI) for academic research. However, there exists access barriers in bringing these resources to a broad range of users. Users who are new to data analytics field are not yet equipped to take advantage of the tools offered by CI. In this project, we propose a framework to lower the access barriers that exist in bringing the high-performance computing resources to users that do not have the training to utilize the capability of CI. The framework uses divide-and-conquer (DC) paradigm for data-intensive computing tasks. It consists of three layers – user interface (UI), parallel scripts generator (PSG) and underlying cyberinfrastructure (CI). The goal of the framework is to provide a user-friendly method for parallelizing data-intensive computing tasks with minimal user intervention. Some of the key design goals are usability, scalability and reproducibility. The users can focus on their problem and leave the parallelization details to the framework. This paper will outline the rationale behind this framework, its detailed implementation and demonstrate its usage with practical use cases.