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Machine Learning - Based Automated Performance Tuning

2014HT

Status Cancelled
School National Graduate School in Computer Science (CUGS)
Division PELAB
Owner Christoph Kessler
Homepage http://www.ida.liu.se/~chrke/courses/MACHLEARN/

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Course plan

Organization

Lectures (ca. 12h), student projects and/or presentations. Written exam.

Recommended for

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

HT 2012.

Goals

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.

Prerequisites

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.

Contents

"[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).

Organization

Lecture block(s) (several days) and presentation session (1 day) in Linköping.

Literature

To be announced.

Lecturers

Christoph Kessler, Welf Löwe.

Examiner

Christoph Kessler.

Examination

Written exam, 1.5p
Small project with presentation or presentation of a research paper, 1.5p

Credit

3hp if both examination moments are fulfilled. Admission to the exam requires attendance in 50% of the lectures and lessons.

Organized by

CUGS

Comments

New course 2012.


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Last updated: 2012-05-03