Efficient Computation of Tucker Decomposition for Streaming Scientific Data Compression Article Swipe
Saibal De
,
Zitong Li
,
Hemanth Kolla
,
Eric Phipps
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.16395
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.16395
The Tucker decomposition, an extension of singular value decomposition for higher-order tensors, is a useful tool in analysis and compression of large-scale scientific data. While it has been studied extensively for static datasets, there are relatively few works addressing the computation of the Tucker factorization of streaming data tensors. In this paper we propose a new streaming Tucker algorithm tailored for scientific data, specifically for the case of a data tensor whose size increases along a single streaming mode that can grow indefinitely, which is typical of time-stepping scientific applications. At any point of this growth, we seek to compute the Tucker decomposition of the data generated thus far, without requiring storing the past tensor slices in memory. Our algorithm accomplishes this by starting with an initial Tucker decomposition and updating its components--the core tensor and factor matrices--with each new tensor slice as it becomes available, while satisfying a user-specified threshold of norm error. We present an implementation within the TuckerMPI software framework, and apply it to synthetic and combustion simulation datasets. By comparing against the standard (batch) decomposition algorithm we show that our streaming algorithm provides significant improvements in memory usage. If the tensor rank stops growing along the streaming mode, the streaming algorithm also incurs less computational time compared to the batch algorithm.
Related Topics
Concepts
Tucker decomposition
Computer science
Tensor (intrinsic definition)
Computation
Decomposition
Streaming data
Singular value decomposition
LU decomposition
Algorithm
Matrix decomposition
Theoretical computer science
Tensor decomposition
Data mining
Mathematics
Ecology
Quantum mechanics
Physics
Biology
Eigenvalues and eigenvectors
Pure mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.16395
- https://arxiv.org/pdf/2308.16395
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386397580
All OpenAlex metadata
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https://openalex.org/W4386397580Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2308.16395Digital Object Identifier
- Title
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Efficient Computation of Tucker Decomposition for Streaming Scientific Data CompressionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-31Full publication date if available
- Authors
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Saibal De, Zitong Li, Hemanth Kolla, Eric PhippsList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.16395Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.16395Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2308.16395Direct OA link when available
- Concepts
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Tucker decomposition, Computer science, Tensor (intrinsic definition), Computation, Decomposition, Streaming data, Singular value decomposition, LU decomposition, Algorithm, Matrix decomposition, Theoretical computer science, Tensor decomposition, Data mining, Mathematics, Ecology, Quantum mechanics, Physics, Biology, Eigenvalues and eigenvectors, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.starting | 123 |
| abstract_inverted_index.tailored | 59 |
| abstract_inverted_index.tensors, | 11 |
| abstract_inverted_index.tensors. | 48 |
| abstract_inverted_index.updating | 130 |
| abstract_inverted_index.TuckerMPI | 160 |
| abstract_inverted_index.algorithm | 58, 119, 179, 185, 204 |
| abstract_inverted_index.comparing | 173 |
| abstract_inverted_index.datasets, | 32 |
| abstract_inverted_index.datasets. | 171 |
| abstract_inverted_index.extension | 4 |
| abstract_inverted_index.generated | 106 |
| abstract_inverted_index.increases | 73 |
| abstract_inverted_index.requiring | 110 |
| abstract_inverted_index.streaming | 46, 56, 77, 184, 200, 203 |
| abstract_inverted_index.synthetic | 167 |
| abstract_inverted_index.threshold | 150 |
| abstract_inverted_index.addressing | 38 |
| abstract_inverted_index.algorithm. | 214 |
| abstract_inverted_index.available, | 145 |
| abstract_inverted_index.combustion | 169 |
| abstract_inverted_index.framework, | 162 |
| abstract_inverted_index.relatively | 35 |
| abstract_inverted_index.satisfying | 147 |
| abstract_inverted_index.scientific | 22, 61, 88 |
| abstract_inverted_index.simulation | 170 |
| abstract_inverted_index.compression | 19 |
| abstract_inverted_index.computation | 40 |
| abstract_inverted_index.extensively | 29 |
| abstract_inverted_index.large-scale | 21 |
| abstract_inverted_index.significant | 187 |
| abstract_inverted_index.accomplishes | 120 |
| abstract_inverted_index.higher-order | 10 |
| abstract_inverted_index.improvements | 188 |
| abstract_inverted_index.specifically | 63 |
| abstract_inverted_index.applications. | 89 |
| abstract_inverted_index.computational | 208 |
| abstract_inverted_index.decomposition | 8, 102, 128, 178 |
| abstract_inverted_index.factorization | 44 |
| abstract_inverted_index.indefinitely, | 82 |
| abstract_inverted_index.time-stepping | 87 |
| abstract_inverted_index.decomposition, | 2 |
| abstract_inverted_index.implementation | 157 |
| abstract_inverted_index.matrices--with | 137 |
| abstract_inverted_index.user-specified | 149 |
| abstract_inverted_index.components--the | 132 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |