Micro-architectural Analysis of SystemML

Supervisor: Pınar Tözün
Semester: Fall 2019

Apache SystemML is an open-source platform to run machine learning tasks efficiently thanks to the hardware-conscious query compilation techniques it adopts. It can be run standalone or on top of Apache Spark. It is considered to be state-of-the-art when running machine learning tasks (i.e., in ACM SIGMOD 2017, there were ~5 papers that used SystemML as a comparison point). This project aims at understanding how efficiently SystemML utilizes the resources of commodity server hardware, and how this differs from some other widely used systems used to run machine learning (e.g., Apache Spark MLlib).