Options
Learning Sequential and Parallel Runtime Distributions for Randomized Algorithms
Date Issued
2016-11-08
Date Available
2016-10-12T15:53:04Z
Abstract
In cloud systems, computation time can be rented by the hour and for a given number of processors. Thus, accurate predictions of the behaviour of both sequential and parallel algorithms has become an important issue, in particular in the case of costly methods such as randomized combinatorial optimization tools. In this work, our objective is to use machine learning algorithms to predict performance of sequential and parallel local search algorithms. In addition to classical features of the instances used by other machine learning tools, we consider data on the sequential runtime distributions of a local search method. This allows us to predict with a high accuracy the parallel computation time of a large class of instances, by learning the behaviour of the sequential version of the algorithm on a small number of instances. Experiments with three solvers on SAT and TSP instances indicate that our method works well, with a correlation coefficient of up to 0.85 for SAT instances and up to 0.95 for TSP instances.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
IEEE
Keywords
Web versions
Language
English
Status of Item
Peer reviewed
Part of
Proceedings of the 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)
Conference Details
ICTAI 2016: 28th International Conference on Tools with Artificial Intelligence, San Jose, California, USA, 6-8 November 2016
This item is made available under a Creative Commons License
File(s)
Owning collection
Views
1217
Acquisition Date
Apr 16, 2024
Apr 16, 2024
Downloads
520
Last Week
7
7
Acquisition Date
Apr 16, 2024
Apr 16, 2024