What does fault tolerant Deep Learning need from MPI? Article Swipe
Vinay Amatya
,
Abhinav Vishnu
,
Charles Siegel
,
Jeff Daily
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1709.03316
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1709.03316
Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed DL implementations which use MPI require days of training (model learning) time on commonly studied datasets. Long running DL applications become susceptible to faults - requiring development of a fault tolerant system infrastructure, in addition to fault tolerant DL algorithms. This raises an important question: What is needed from MPI for de- signing fault tolerant DL implementations? In this paper, we address this problem for permanent faults. We motivate the need for a fault tolerant MPI specification by an in-depth consideration of recent innovations in DL algorithms and their properties, which drive the need for specific fault tolerance features. We present an in-depth discussion on the suitability of different parallelism types (model, data and hybrid); a need (or lack thereof) for check-pointing of any critical data structures; and most importantly, consideration for several fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI and their applicability to fault tolerant DL implementations. We leverage a distributed memory implementation of Caffe, currently available under the Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies demonstrates the effectiveness of the proposed fault tolerant DL implementation using OpenMPI based ULFM.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1709.03316
- https://arxiv.org/pdf/1709.03316
- OA Status
- green
- Cited By
- 1
- References
- 6
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2952319695
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https://openalex.org/W2952319695Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.1709.03316Digital Object Identifier
- Title
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What does fault tolerant Deep Learning need from MPI?Work title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-09-11Full publication date if available
- Authors
-
Vinay Amatya, Abhinav Vishnu, Charles Siegel, Jeff DailyList of authors in order
- Landing page
-
https://arxiv.org/abs/1709.03316Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1709.03316Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1709.03316Direct OA link when available
- Concepts
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Computer science, Implementation, Fault tolerance, Leverage (statistics), Artificial intelligence, Machine learning, Artificial neural network, Deep learning, Distributed computing, Software engineeringTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2020: 1Per-year citation counts (last 5 years)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.days | 32 |
| abstract_inverted_index.even | 24 |
| abstract_inverted_index.from | 74 |
| abstract_inverted_index.have | 4 |
| abstract_inverted_index.lack | 144 |
| abstract_inverted_index.most | 154 |
| abstract_inverted_index.need | 96, 119, 142 |
| abstract_inverted_index.this | 84, 88 |
| abstract_inverted_index.time | 37 |
| abstract_inverted_index.Scale | 194 |
| abstract_inverted_index.ULFM. | 234 |
| abstract_inverted_index.based | 233 |
| abstract_inverted_index.drive | 117 |
| abstract_inverted_index.facto | 8 |
| abstract_inverted_index.fault | 55, 62, 79, 99, 122, 159, 163, 173, 227 |
| abstract_inverted_index.large | 14 |
| abstract_inverted_index.scale | 15 |
| abstract_inverted_index.their | 114, 170 |
| abstract_inverted_index.types | 136 |
| abstract_inverted_index.under | 187 |
| abstract_inverted_index.using | 205, 210, 231 |
| abstract_inverted_index.which | 28, 116 |
| abstract_inverted_index.(model | 35 |
| abstract_inverted_index.Caffe, | 184 |
| abstract_inverted_index.become | 5, 46 |
| abstract_inverted_index.faults | 49 |
| abstract_inverted_index.memory | 181 |
| abstract_inverted_index.needed | 73 |
| abstract_inverted_index.neural | 218 |
| abstract_inverted_index.paper, | 85 |
| abstract_inverted_index.raises | 67 |
| abstract_inverted_index.recent | 108 |
| abstract_inverted_index.system | 57 |
| abstract_inverted_index.(ULFM), | 165 |
| abstract_inverted_index.(model, | 137 |
| abstract_inverted_index.Extreme | 193 |
| abstract_inverted_index.Machine | 9, 189 |
| abstract_inverted_index.OpenMPI | 232 |
| abstract_inverted_index.Reinit) | 166 |
| abstract_inverted_index.Toolkit | 191 |
| abstract_inverted_index.address | 87 |
| abstract_inverted_index.dataset | 213 |
| abstract_inverted_index.faults. | 92 |
| abstract_inverted_index.network | 219 |
| abstract_inverted_index.present | 126 |
| abstract_inverted_index.problem | 89 |
| abstract_inverted_index.require | 31 |
| abstract_inverted_index.running | 43 |
| abstract_inverted_index.several | 158 |
| abstract_inverted_index.signing | 78 |
| abstract_inverted_index.studied | 40 |
| abstract_inverted_index.tending | 202 |
| abstract_inverted_index.(MaTEx). | 195 |
| abstract_inverted_index.AlexNet, | 215 |
| abstract_inverted_index.ImageNet | 212 |
| abstract_inverted_index.Learning | 1, 10, 190 |
| abstract_inverted_index.addition | 60 |
| abstract_inverted_index.commonly | 39 |
| abstract_inverted_index.critical | 150 |
| abstract_inverted_index.hybrid); | 140 |
| abstract_inverted_index.in-depth | 105, 128 |
| abstract_inverted_index.leverage | 178 |
| abstract_inverted_index.motivate | 94 |
| abstract_inverted_index.proposed | 226 |
| abstract_inverted_index.specific | 121 |
| abstract_inverted_index.thereof) | 145 |
| abstract_inverted_index.tolerant | 56, 63, 80, 100, 174, 228 |
| abstract_inverted_index.training | 34 |
| abstract_inverted_index.GoogLeNet | 217 |
| abstract_inverted_index.algorithm | 12 |
| abstract_inverted_index.analysis. | 17 |
| abstract_inverted_index.available | 186 |
| abstract_inverted_index.currently | 185 |
| abstract_inverted_index.datasets. | 41 |
| abstract_inverted_index.different | 134 |
| abstract_inverted_index.expensive | 22 |
| abstract_inverted_index.features. | 124 |
| abstract_inverted_index.implement | 197 |
| abstract_inverted_index.important | 69 |
| abstract_inverted_index.learning) | 36 |
| abstract_inverted_index.permanent | 91 |
| abstract_inverted_index.proposals | 161 |
| abstract_inverted_index.question: | 70 |
| abstract_inverted_index.requiring | 51 |
| abstract_inverted_index.tolerance | 123, 160 |
| abstract_inverted_index.ULFM-based | 206 |
| abstract_inverted_index.algorithms | 3, 19, 112 |
| abstract_inverted_index.approaches | 199 |
| abstract_inverted_index.discussion | 129 |
| abstract_inverted_index.evaluation | 209 |
| abstract_inverted_index.mitigation | 164 |
| abstract_inverted_index.topologies | 220 |
| abstract_inverted_index.(user-level | 162 |
| abstract_inverted_index.MaTEx-Caffe | 203 |
| abstract_inverted_index.algorithms. | 65 |
| abstract_inverted_index.development | 52 |
| abstract_inverted_index.distributed | 25, 180 |
| abstract_inverted_index.innovations | 109 |
| abstract_inverted_index.parallelism | 135 |
| abstract_inverted_index.properties, | 115 |
| abstract_inverted_index.structures; | 152 |
| abstract_inverted_index.suitability | 132 |
| abstract_inverted_index.susceptible | 47 |
| abstract_inverted_index.applications | 45 |
| abstract_inverted_index.demonstrates | 221 |
| abstract_inverted_index.importantly, | 155 |
| abstract_inverted_index.applicability | 171 |
| abstract_inverted_index.consideration | 106, 156 |
| abstract_inverted_index.effectiveness | 223 |
| abstract_inverted_index.specification | 102 |
| abstract_inverted_index.check-pointing | 147 |
| abstract_inverted_index.implementation | 182, 230 |
| abstract_inverted_index.computationally | 21 |
| abstract_inverted_index.implementation. | 207 |
| abstract_inverted_index.implementations | 27 |
| abstract_inverted_index.infrastructure, | 58 |
| abstract_inverted_index.implementations. | 176 |
| abstract_inverted_index.implementations? | 82 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |