Proceedings ArticleDOI
Runtime prediction on new architectures
Alexey Sidnev
- pp 17
TLDR
The paper examines peculiarities of software algorithms and suggests a method for processing experimental data provided by computational systems and a two-step method of problem solution using linear and non-linear machine learning algorithms.Abstract:
This paper formulates the program runtime prediction problem subject to algorithm parameters and characteristics of a computational system to be used to run the algorithm. It is suggested to build a model representing runtime as a function of algorithm parameters and computational system characteristics. This is followed by determination of features to be used for functional dependence recovery. A two-step method of problem solution using linear and non-linear machine learning algorithms is proposed. The paper examines peculiarities of software algorithms and suggests a method for processing experimental data provided by computational systems. It also features a comparative analysis of runtime prediction results for solution of several linear algebra problems on 84 personal computers and servers using a number of machine learning algorithms. Use of a random forest combined with the linear least square method shows an error of less than 15% for most computational systems of similar architecture.read more
Citations
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Dynamic Algorithm Portfolios.
TL;DR: This work reformulates algorithm selection as a time allocation problem: all candidate algorithms are run in parallel, and their relative priorities are continually updated based on runtime information, with the aim of minimizing the time to reach a desired performance level.
Book ChapterDOI
Hardware-Specific Selection the Most Fast-Running Software Components
TL;DR: The paper presents a comparative analysis of runtime prediction results for solving several linear algebra problems on 84 personal computers and servers and shows an error of less than 22% for computational systems represented in the training data set.
References
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Random Forests
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TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
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TL;DR: This chapter provides an overview of the fundamentals of algorithms and their links to self-organization, exploration, and exploitation.
Journal Article
Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks
David S. Broomhead,David Lowe +1 more
TL;DR: The relationship between 'learning' in adaptive layered networks and the fitting of data with high dimensional surfaces is discussed, leading naturally to a picture of 'generalization in terms of interpolation between known data points and suggests a rational approach to the theory of such networks.
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