Conference Publication Details
Mandatory Fields
Arbelaez, Alejandro; Truchet, Charlotte; O'Sullivan, Barry
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)
Learning sequential and parallel runtime distributions for randomized algorithms
Optional Fields
Cloud computing Computability Learning Artificial intelligence Parallel algorithms Randomised algorithms Search problems Travelling salesman problems SAT instances TSP instances Cloud systems Correlation coefficient Machine learning Parallel computation time Parallel local search algorithms Randomized algorithms Sequential local search algorithms Sequential runtime distributions Algorithm design and analysis Approximation algorithms Machine learning algorithms Prediction algorithms Runtime
San Jose, CA, USA
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 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.
Grant Details