Time Series Discord Discovery on Intel Many-Core Systems

Zymbler, Mikhail; Polyakov, A. F.; Kipnis, M. M.

doi:10.1007/978-3-030-28163-2_12

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…Research that addresses the problem of discord discovery parallelization includes the following (see Table 2 for the summary). In [10,13], Zymbler et al proposed a parallelization schema for HOTSAX to discover discords with Intel many-core processors or GPUs through the OpenMP [37] or OpenACC [38] technologies, respectively. The algorithm employs a matrix data layout to organize calculations with as many vectorizable loops as possible.…”

Section: Algorithm Year Ancestor (If Any) and Approachmentioning

confidence: 99%

“…In this study, we address the problem of parallelization of the MERLIN algorithm for the discovery of arbitrary length discords on a GPU, continuing our research on accelerating various time series mining tasks with parallel architectures and in-database time series analysis [9][10][11][12][13][14][15][16]. The article's contribution can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

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High-Performance Time Series Anomaly Discovery on Graphics Processors

Zymbler

Kraeva

2023

Mathematics

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Currently, discovering subsequence anomalies in time series remains one of the most topical research problems. A subsequence anomaly refers to successive points in time that are collectively abnormal, although each point is not necessarily an outlier. Among numerous approaches to discovering subsequence anomalies, the discord concept is considered one of the best. A time series discord is intuitively defined as a subsequence of a given length that is maximally far away from its non-overlapping nearest neighbor. Recently introduced, the MERLIN algorithm discovers time series discords of every possible length in a specified range, thereby eliminating the need to set even that sole parameter to discover discords in a time series. However, MERLIN is serial, and its parallelization could increase the performance of discord discovery. In this article, we introduce a novel parallelization scheme for GPUs called PALMAD, parallel arbitrary length MERLIN-based anomaly discovery. As opposed to its serial predecessor, PALMAD employs recurrent formulas we have derived to avoid redundant calculations, and advanced data structures for the efficient implementation of parallel processing. Experimental evaluation over real-world and synthetic time series shows that our algorithm outperforms parallel analogs. We also apply PALMAD to discover anomalies in a real-world time series, employing our proposed discord heatmap technique to illustrate the results.

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Section: Algorithm Year Ancestor (If Any) and Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Performance Time Series Anomaly Discovery on Graphics Processors

Zymbler

Kraeva

2023

Mathematics

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“…Research addressed the problem of parallelization the discord discovery include the following. In [58,65], Zymbler et al proposed a parallelization schema for HOTSAX to discover discords with Intel many-core processors or GPUs through the OpenMP [44] or OpenACC [39] technology, respectively. The algorithm employs matrix data layout to organize calculations with as many vectorizable loops as possible.…”

Section: Related Workmentioning

confidence: 99%

“…In this study, we address the problem of parallelization the MERLIN algorithm for discovery of arbitrary length discords on GPU continuing our research on accelerating various time series mining tasks with parallel architectures and in-database time series analysis [29,[58][59][60][61][62][63]65]. The article's contribution can be summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

Fast Summarization of Long Time Series with Graphics Processor

Zymbler

Goglachev

2022

Mathematics

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Summarization of a long time series often occurs in analytical applications related to decision-making, modeling, planning, and so on. Informally, summarization aims at discovering a small-sized set of typical patterns (subsequences) to briefly represent the long time series. Apparent approaches to summarization like motifs, shapelets, cluster centroids, and so on, either require training data or do not provide an analyst with information regarding the fraction of the time series that a typical subsequence found corresponds to. Recently introduced, the time series snippet concept overcomes the above-mentioned limitations. A snippet is a subsequence that is similar to many other subsequences of the time series with respect to a specially defined similarity measure based on the Euclidean distance. However, the original Snippet-Finder algorithm has cubic time complexity concerning the lengths of the time series and the snippet. In this article, we propose the PSF (Parallel Snippet-Finder) algorithm that accelerates the original snippet discovery schema with GPU and ensures acceptable performance over very long time series. As opposed to the original algorithm, PSF splits the calculation of the similarity of all the time series subsequences to a snippet into several steps, each of which is performed in parallel. Experimental evaluation over real-world time series shows that PSF outruns both the original algorithm and a straightforward parallelization.

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“…A first route that can be followed in order to speed up the calculation regards parallelizing the existing algorithms, as was done for example by Zymbler et al (2019), where a version of HOT SAX has been developed for Intel Many-Core Systems. The fastest algorithm involving graphic card accelerators is SCAMP of the Matrix Profile series (Zimmerman et al, 2019).…”

Section: Introduction and Related Workmentioning

confidence: 99%

A fast algorithm for complex discord searches in time series: HOT SAX Time

Avogadro¹,

Dominoni²

2021

Preprint

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Time series analysis is quickly proceeding towards long and complex tasks. In recent years, fast approximate algorithms for discord search have been proposed in order to compensate for the increasing size of the time series. It is more interesting, however, to find quick exact solutions. In this research, we improved HOT SAX by exploiting two main ideas: the warm-up process, and the similarity between sequences close in time. The resulting algorithm, called HOT SAX Time (HST), has been validated with real and synthetic time series, and successfully compared with HOT SAX, RRA, SCAMP, and DADD. The complexity of a discord search has been evaluated with a new indicator, the cost per sequence (cps), which allows one to compare searches on time series of different lengths. Numerical evidence suggests that two conditions are involved in determining the complexity of a discord search in a non-trivial way: the length of the discords, and the noise/signal ratio. In the case of complex searches, HST can be more than 100 times faster than HOT SAX, thus being at the forefront of the exact discord search.

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Time Series Discord Discovery on Intel Many-Core Systems

Cited by 12 publications

References 22 publications

High-Performance Time Series Anomaly Discovery on Graphics Processors

High-Performance Time Series Anomaly Discovery on Graphics Processors

Fast Summarization of Long Time Series with Graphics Processor

A fast algorithm for complex discord searches in time series: HOT SAX Time

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