PADDLE: Performance Analysis using a Data-Driven Learning Environment
Authors: Jayaraman Thiagarajan, Rushil Anirdh, Bhavya Kaikhura, Nikhil Jain, Tanzima Islam, Abhinav Bhatele, Jae-Seung Yeom, Todd Gamblin Venue: I EEE International Parallel and Distributed Processing Symposium (IPDPS) In the scope of HPC, machine learning is gaining increased traction to add in performance analysis and tuning. However, this approach includes a pipeline of data collection, data pre-processing, various machine learning algorithm testing, tuning, and then finally trying to understand the model. The paper states that while this process is repetitive, rarely can insights be reused from one domain to another. To address this void, the propose PADDLE. PADDLE has three key steps: deep feature extraction, model design, and visualization. The first step allows users to throw extensive amounts of data at the problem, and an automated solutions determines the key inputs, mapping them to a new feature space. The next step in paddle automatically tests a number of machine lea...