Scalable Algorithm for Subsequence Similarity Search in Very Large Time Series Data on Cluster of Phi KNL

Kraeva, Yana; Zymbler, Mikhail

doi:10.1007/978-3-030-23584-0_9

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…GPUs on the other hand are already widely available at workplaces, shipped along with some of the sequencers, and also available on the cloud. DTW has been parallelized in the past for various different applications on architectures including FPGAs 17,20 , Intel Xeon Phis 21 , big data clusters 22 , customized fabrics 23 and even GPUs 24,25 . HARU 17 is a recent work that implements SquiggleFilter's algorithm on a budget-constrained FPGA.…”

Section: Prior Workmentioning

confidence: 99%

See 1 more Smart Citation

Accelerated Dynamic Time Warping on GPU for Selective Nanopore Sequencing

Sadasivan

Stiffler

Tirumala

et al. 2023

Preprint

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The design and supply of RT-PCR primers for accurate virus testing is a complex process. The MinION is a revolutionary portable nanopore DNA sequencer that may be used to sequence the whole genome of a target virus in a biological sample. Human samples have more than 99% of non-target host DNA and Read Until is a protocol that enables the MinION to selectively eject reads in real-time. However, the MinION does not have any in-built compute power to select non-target reads. SquiggleFilter is a prior work that identified the accuracy and throughput challenges in performing Read Until using the state-of-the-art solution and proposed a hardware-accelerated subsequence Dynamic Time Warping (sDTW) based programmable filter on an ASIC. However, SquiggleFilter does not work for genomes larger than 100Kb. We optimize SquiggleFilter’s sDTW algorithm onto the more commonly available GPUs.DTWaxbetter uses tensor core pipes, 2X-SIMD FP16 computations and efficient data handling strategies using offline pre-processing, coalesced global memory loads, warp shuffles and shared memory buffering among other optimizations.DTWaxenables Read Until and yields 1.92X sequencing speedup and 3.64X compute speedup: costup over a sequencing workflow that does not use Read Until.

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Section: Prior Workmentioning

confidence: 99%

“…DTW has been parallelized in the past for various different applications on architectures including FPGAs 17, 20 , Intel Xeon Phis 21 , big data clusters 22 , customized fabrics 23 and even GPUs 24, 25 . HARU 17 is a recent work that implements SquiggleFilter’s algorithm on a budget-constrained FPGA.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Dynamic Time Warping on GPU for Selective Nanopore Sequencing

Sadasivan

Stiffler

Tirumala

et al. 2023

Preprint

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“…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%

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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“…Vectorization refers to the ability of the compiler to replace several scalar operations in the body of the cycle with a fixed number of repetitions in one vector operation [18]. The work in [19] presents parallel search algorithms for similar subsequences in ultra-large time series on cluster systems. Shabib et al [20] a method for parallelizing a sequential search algorithm for similar subsequences of the UCR-DTW software for a computing cluster based on the use of the spark framework.…”

Section: Introductionmentioning

confidence: 99%

A parallel time series algorithm for searching similar sub-sequences

Saeed

Ali

Al-Neama

2022

IJEECS

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<p><span>Dynamic time warping (DTW) is an important metric for measuring similarity for most time series applications. The computations of DTW cost too much especially with the gigantic of sequence databases and lead to an urgent need for accelerating these computations. However, the multi-core cluster systems, which are available now, with their scalability and performance/cost ratio, meet the need for more powerful and efficient performance. This paper proposes a highly efficient parallel vectorized algorithm with high performance for computing DTW, addressed to multi-core clusters using the Intel quad-core Xeon co-processors. It deduces an efficient architecture. Implementations employ the potential of both message passing interface (MPI) and OpenMP libraries. The implementation is based on the OpenMP parallel programming technology and offloads execution mode, where part of the code sub-sequences on the processor side, which are uploaded to the co-processor for the DTW computations. The results of experiments confirm the effectiveness of the algorithm.</span></p>

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Scalable Algorithm for Subsequence Similarity Search in Very Large Time Series Data on Cluster of Phi KNL

Cited by 8 publications

References 21 publications

Accelerated Dynamic Time Warping on GPU for Selective Nanopore Sequencing

Accelerated Dynamic Time Warping on GPU for Selective Nanopore Sequencing

High-Performance Time Series Anomaly Discovery on Graphics Processors

A parallel time series algorithm for searching similar sub-sequences

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