Parallel and Distributed Machine Learning Algorithms for Scalable Big Data Analytics

This editorial is for the Special Issue of the journal Future Generation Computing Systems, consisting of the selected papers of the 6th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning 2017). In this editorial, we have given a high-level overview of the 4 papers contained in this special issue, along with references to some of the related works

Real-Time Dedispersion for Fast Radio Transient Surveys, using Auto Tuning on Many-Core Accelerators

Dedispersion, the removal of deleterious smearing of impulsive signals by the interstellar matter, is one of the most intensive processing steps in any radio survey for pulsars and fast transients. We here present a study of the parallelization of this algorithm on many-core accelerators, including GPUs from AMD and NVIDIA, and the Intel Xeon Phi. We find that dedispersion is inherently memory-bound. Even in a perfect scenario, hardware limitations keep the arithmetic intensity low, thus limiting performance. We next exploit auto-tuning to adapt dedispersion to different accelerators, observations, and even telescopes. We demonstrate that the optimal settings differ between observational setups, and that auto-tuning significantly improves performance. This impacts time-domain surveys from Apertif to SKA.Comment: 8 pages, accepted for publication in Astronomy and Computin

Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural Networks

Data in time series format, such as biological signals from medical sensors or machine signals from sensors in industrial environments are rich sources of information that can give crucial insights on the present and future condition of a person or machine. The task of predicting future values of time series has been initially approached with simple machine learning methods, and lately with deep learning. Two models that have shown good performance in this task are the temporal convolutional network and the attention module. However, despite the promising results of deep learning methods, their black-box nature makes them unsuitable for real-world applications where the predictions need to be explainable in order to be trusted. In this paper we propose an architecture comprised of a temporal convolutional network with an attention mechanism that makes predictions while presenting the timesteps of the input that were most influential for future outputs. We apply it on two datasets and we show that we gain interpretability without degrading the accuracy compared to the original temporal convolutional models. We then go one step further and we combine our configuration with various machine learning methods on top, creating a pipeline that achieves interpretability both across timesteps and input features. We use it to forecast a different variable from one of the above datasets and we study how the accuracy is affected compared to the original black-box approach

08332 Executive Summary -- Distributed Verification and Grid Computing

The Dagstuhl Seminar on Distributed Verification and Grid Computing took place from 10.08.2008 to 14.08.2008 and brought together two groups of researchers to discuss their recent work and recent trends related to parallel verification of large scale computer systems on large scale grids. In total, 29 experts from 12 countries attended the seminar

08332 Abstracts Collection -- Distributed Verification and Grid Computing

From 08/10/2008 to 08/14/2008 the Dagstuhl Seminar 08332 ``Distributed Verification and Grid Computing\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS

Nowadays, with the rising number of sensors in sectors such as healthcare and industry, the problem of multivariate time series classification (MTSC) is getting increasingly relevant and is a prime target for machine and deep learning approaches. Their expanding adoption in real-world environments is causing a shift in focus from the pursuit of ever-higher prediction accuracy with complex models towards practical, deployable solutions that balance accuracy and parameters such as prediction speed. An MTSC model that has attracted attention recently is ROCKET, based on random convolutional kernels, both because of its very fast training process and its state-of-the-art accuracy. However, the large number of features it utilizes may be detrimental to inference time. Examining its theoretical background and limitations enables us to address potential drawbacks and present LightWaveS: a framework for accurate MTSC, which is fast both during training and inference. Specifically, utilizing wavelet scattering transformation and distributed feature selection, we manage to create a solution that employs just 2.5% of the ROCKET features, while achieving accuracy comparable to recent MTSC models. LightWaveS also scales well across multiple compute nodes and with the number of input channels during training. In addition, it can significantly reduce the input size and provide insight to an MTSC problem by keeping only the most useful channels. We present three versions of our algorithm and their results on distributed training time and scalability, accuracy, and inference speedup. We show that we achieve speedup ranging from 9x to 53x compared to ROCKET during inference on an edge device, on datasets with comparable accuracy.Comment: This work has been accepted as a short paper at DCOSS 202