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Effective Parallelisation for Machine Learning
[article]
2018
arXiv
pre-print
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This is a significant step towards a general answer to an open question on the efficient parallelisation of machine learning algorithms in the sense of Nick's Class (NC). ...
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications ...
Introduction This paper contributes a novel and provably effective parallelisation scheme for a broad class of learning algorithms. ...
arXiv:1810.03530v1
fatcat:kijomnorwzbmlpe47oxofa7qn4
ParallelPC: an R package for efficient constraint based causal exploration
[article]
2015
arXiv
pre-print
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The package is not only suitable for super-computers or clusters, but also convenient for researchers using personal computers with multi core CPUs. ...
In this paper, we present an R package, ParallelPC, that includes the parallelised versions of these causal exploration algorithms. ...
As PC-simple (PC-Select) is efficient in small datasets, we use the Adult dataset from UCI Machine Learning Repository with 48842 samples. ...
arXiv:1510.03042v1
fatcat:kjsgqivgojdihbsnet6nlfmt5y
New Machine Learning Developments in ROOT/TMVA
2019
EPJ Web of Conferences
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The Toolkit for Multivariate Analysis, TMVA, the machine learning package integrated into the ROOT data analysis framework, has recently seen improvements to its deep learning module, parallelisation of ...
Both dense and convo-lutional layers are shown to be competitive on small-scale networks suitable for high-level physics analyses in both training and in single-event evaluation. ...
TMVA is a ROOT-integrated framework for machine learning. ...
doi:10.1051/epjconf/201921406014
fatcat:zitvj5ym25dm3nobk5y4wdl2ii
Online Non-stationary Boosting
[chapter]
2010
Lecture Notes in Computer Science
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We evaluate the new algorithm against Online Boosting, using the STAGGER dataset and three challenging datasets derived from a learning problem inside a parallelising virtual machine. ...
Oza's Online Boosting algorithm provides a version of Ad-aBoost which can be trained in an online way for stationary problems. ...
We are investigating the application of ML techniques to automatic parallelisation problems, running inside a Java virtual machine. ...
doi:10.1007/978-3-642-12127-2_21
fatcat:f5mnp7vfuva5dnkifmt6gjnrom
Parallel ILP for distributed-memory architectures
2008
Machine Learning
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This has brought into focus machine learning techniques like Inductive Logic Programming (ILP) that are able to extract human-comprehensible models for complex relational data. ...
The growth of machine-generated relational databases, both in the sciences and in industry, is rapidly outpacing our ability to extract useful information from them by manual means. ...
Acknowledgements The authors would like to acknowledge the anonymous reviewers for the insightful and detailed comments that greatly improved the paper. ...
doi:10.1007/s10994-008-5094-2
fatcat:kbow55zlozcfngmzl4rz37l4k4
Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks
[article]
2018
arXiv
pre-print
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Since such systems are where some of their most useful applications lie (e.g. obstacle detection for mobile robots, vision-based medical assistive technology), significant bodies of work from both machine ...
learning and systems communities have attempted to provide optimisations that will make CNNs available to edge devices. ...
The authors are grateful to Lizhong Chen and the anonymous reviewers for their valuable contributions. ...
arXiv:1809.07196v1
fatcat:wxevr5hprveiro5lg2aie5nnem
Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks
2018
2018 IEEE International Symposium on Workload Characterization (IISWC)
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Since such systems are where some of their most useful applications lie (e.g. obstacle detection for mobile robots, vision-based medical assistive technology), significant bodies of work from both machine ...
learning and systems communities have attempted to provide optimisations that will make CNNs available to edge devices. ...
The authors are grateful to Lizhong Chen and the anonymous reviewers for their valuable contributions. ...
doi:10.1109/iiswc.2018.8573503
dblp:conf/iiswc/TurnerCRCOS18
fatcat:hxxhuovm6fhyhheg55vtwyvsoi
An Enhanced Parallelisation Model for Performance Prediction of Apache Spark on a Multinode Hadoop Cluster
2021
Big Data and Cognitive Computing
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In this paper, we proposed two distinct parallelisation models for performance prediction. ...
Our insight is that each node in a Hadoop cluster can communicate with identical nodes, and a certain function of the non-parallelisable runtime can be estimated accordingly. ...
The major advantage of Apache Spark for machine learning is its end-to-end capabilities. ...
doi:10.3390/bdcc5040065
fatcat:pc4q65uwzfdv5lfmlfhftohaeq
The Need for Low Bias Algorithms in Classification Learning from Large Data Sets
[chapter]
2002
Lecture Notes in Computer Science
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Sampling and parallelisation have proved useful means for reducing computation time when learning from large data sets. ...
This paper reviews the appropriateness for application to large data sets of standard machine learning algorithms, which were mainly developed in the context of small data sets. ...
Bias and Variance What other fundamental properties of machine learning algorithms are required for learning from large data sets? ...
doi:10.1007/3-540-45681-3_6
fatcat:dw6wrhtz3jd27l77w4pudeyb4u
A parallel genetic algorithms framework based on Hadoop MapReduce
2015
Proceedings of the 30th Annual ACM Symposium on Applied Computing - SAC '15
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This paper describes a framework for developing parallel Genetic Algorithms (GAs) on the Hadoop platform, following the paradigm of MapReduce. ...
Subject The "Chicago Crime" dataset (from the UCI Machine Learning Repository was used. The dataset has 13 features and 10000 instances. ...
GAs are usually executed on single machines as sequential programs, so scalability issues prevent that they are effectively applied to real-world problems. ...
doi:10.1145/2695664.2696060
dblp:conf/sac/FerrucciSKS15
fatcat:tbr3ib3y6jb2ze7iux3mukuci4
Nonlinear Behaviour in the MPI-Parallelised Model of the Rat Somatosensory Cortex
2008
Informatica
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Because of a high degree of complexity effective parallelisation of algorithms is required. ...
We propose method of parallelisation for the network and the results of simulations using GENESIS parallelised for MPI environment are presented. An occurrence of nonlinear behaviour is demonstrated. ...
Acknowledgements This work has been supported by the Maria Curie-Sklodowska University, Lublin, Poland (under the grant of UMCS Vice President 2007) and Polish State Committee for Scientific Research under ...
doi:10.15388/informatica.2008.224
fatcat:oubkm3lyd5gepepqo2mvy5kxoa
Decision tree building on multi-core using FastFlow
2013
Concurrency and Computation
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TORQUATI Quinlan [3], a cornerstone in data mining and machine learning (see e.g., [4] ). ...
Nevertheless, the potential for improvements is vast, and it resides in the idle CPU cores on the user's machine. ...
We thank the Competence Center Gateway for HPC of the IT Center, University of Pisa, for the use of the Magny-Cours box. ...
doi:10.1002/cpe.3063
fatcat:cejcokjvmrg5dg4e53e3qpbesq
Scaling up classification rule induction through parallel processing
2012
Knowledge engineering review (Print)
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Parallelisation seems to be a natural and cost effective way to scale up data mining technologies. ...
This paper surveys advances in parallelisation in the field of classification rule induction. ...
of the n machines there is a learning algorithm L installed that learns a local concept out of the data samples locally stored on each machine. ...
doi:10.1017/s0269888912000355
fatcat:wbm4gvuu4jbsxoe2qmvl5ae6ne
Page 26 of Journal of Research and Practice in Information Technology Vol. 26, Issue 1
[page]
1994
Journal of Research and Practice in Information Technology
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It also describes an experimental methodology for use in measuring the effects of speed up learning Chapter 3, On Integrating Machine Learning with Planning by Gerald DeJong et al, and Chapter 4, The Role ...
Although the reports do mention a variety of fronts of machine learning research, the book as a whole does not provide a general picture of the machine learning area. ...
Measuring and Evaluating Parallel State-Space Exploration Algorithms
2008
Electronical Notes in Theoretical Computer Science
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We discuss and answer these questions based on our experience with parallelising Saturation -a symbolic algorithm for generating state-spaces of asynchronous system models -on a shared-memory architecture ...
Doing so will hopefully spare newcomers to the growing PDMC community from having to learn these lessons the hard way, as we did over a painful period of almost three years. ...
Larger shared-memory machines can offer more processors for performance evaluation but are less readily available. ...
doi:10.1016/j.entcs.2007.10.020
fatcat:zk5odpq7cnhmpgfqwlyuweqrqy
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