Machine Learning - Based Automated Performance Tuning2014HT
Lectures (ca. 12h), student projects and/or presentations. Written exam.
Graduate (CUGS, CIS, ...) students interested in the application of machine learning techniques to advanced system performance optimization, as in compiler construction, library generation, runtime systems, parallel computing, software engineering, system simulation and optimization.
The course was last given
The course introduces fundamental techniques of machine learning and considers case studies for its application in automated system performance tuning, such as auto-tuning library generators, compilers, and runtime systems.
Linear algebra. Discrete mathematics. Data structures and algorithms. Some basic knowledge of computer architecture is assumed. For the case study presentations, some background in at least one application area, such as compiler construction, library generation, signal processing software, runtime systems, or software composition, is required.
"[Machine] learning is the process of [automatically] constructing, from
training data, a fast and/or compact surrogate function that heuristically
solves a decision, prediction or classification problem for which only
expensive or no algorithmic solutions are known. It automatically abstracts
from sample data to a total decision function."
- [Danylenko et al., Comparing Machine Learning Approaches..., SC'2011, LNCS 6708]
The course will introduce basic machine learning techniques together with principles of autotuning, and emphasize on the application of machine learning in performance autotuning (student projects and/or presentations).
Lecture block(s) (several days) and presentation session (1 day) in Linköping.
To be announced.
Christoph Kessler, Welf Löwe.
Written exam, 1.5p
Small project with presentation or presentation of a research paper, 1.5p
3hp if both examination moments are fulfilled. Admission to the exam requires attendance in 50% of the lectures and lessons.
New course 2012.
Page responsible: Director of Graduate Studies
Last updated: 2012-05-03