Optimizing Data-Intensive Applications Automatically By Leveraging Parallel Data Processing Frameworks
Maaz Bin Safeer Ahmad, Alvin Cheung · 2017
In this demonstration we will showcase CASPER, a novel tool that enables sequential data-intensive programs to automatically leverage the optimizations provided by parallel data processing frameworks. The goal of CASPER is to reduce the inertia against adaptation of new data processing frameworks---particularly for non-expert users---by automatically re-writing sequential programs written in general purpose languages to the high-level DSLs or APIs of these frameworks. Through CASPER's browser-based interface, users can enter the source code of their Java applications and have it automatically retargeted to execute on Apache Spark. In our interactive presentation, we will use CASPER to optimize sequential implementations of data visualization programs as well as image processing kernels. The optimized Spark implementations along with the original sequential implementations will then be executed simultaneously on the cloud to allow the demo to audience compare the runtime performances and outputs in real-time.