Benchmarking Resource Usage for Efficient Distributed Deep Learning Article Swipe
Nathan C. Frey
,
Baolin Li
,
J.C. McDonald
,
Dan Zhao
,
Michael Jones
,
David Bestor
,
Devesh Tiwari
,
Vijay Gadepally
,
Siddharth Samsi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.12423
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.12423
Deep learning (DL) workflows demand an ever-increasing budget of compute and energy in order to achieve outsized gains. Neural architecture searches, hyperparameter sweeps, and rapid prototyping consume immense resources that can prevent resource-constrained researchers from experimenting with large models and carry considerable environmental impact. As such, it becomes essential to understand how different deep neural networks (DNNs) and training leverage increasing compute and energy resources -- especially specialized computationally-intensive models across different domains and applications. In this paper, we conduct over 3,400 experiments training an array of deep networks representing various domains/tasks -- natural language processing, computer vision, and chemistry -- on up to 424 graphics processing units (GPUs). During training, our experiments systematically vary compute resource characteristics and energy-saving mechanisms such as power utilization and GPU clock rate limits to capture and illustrate the different trade-offs and scaling behaviors each representative model exhibits under various resource and energy-constrained regimes. We fit power law models that describe how training time scales with available compute resources and energy constraints. We anticipate that these findings will help inform and guide high-performance computing providers in optimizing resource utilization, by selectively reducing energy consumption for different deep learning tasks/workflows with minimal impact on training.
Related Topics
Concepts
Computer science
Workflow
Deep learning
Leverage (statistics)
Benchmarking
Artificial intelligence
Resource (disambiguation)
Artificial neural network
Machine learning
Deep neural networks
Scheduling (production processes)
Energy consumption
Distributed computing
Resource allocation
Database
Ecology
Biology
Marketing
Operations management
Business
Economics
Computer network
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2201.12423
- https://arxiv.org/pdf/2201.12423
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226424739
All OpenAlex metadata
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https://openalex.org/W4226424739Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2201.12423Digital Object Identifier
- Title
-
Benchmarking Resource Usage for Efficient Distributed Deep LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-28Full publication date if available
- Authors
-
Nathan C. Frey, Baolin Li, J.C. McDonald, Dan Zhao, Michael Jones, David Bestor, Devesh Tiwari, Vijay Gadepally, Siddharth SamsiList of authors in order
- Landing page
-
https://arxiv.org/abs/2201.12423Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2201.12423Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2201.12423Direct OA link when available
- Concepts
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Computer science, Workflow, Deep learning, Leverage (statistics), Benchmarking, Artificial intelligence, Resource (disambiguation), Artificial neural network, Machine learning, Deep neural networks, Scheduling (production processes), Energy consumption, Distributed computing, Resource allocation, Database, Ecology, Biology, Marketing, Operations management, Business, Economics, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2022: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.We | 150, 168 |
| abstract_inverted_index.an | 5, 84 |
| abstract_inverted_index.as | 122 |
| abstract_inverted_index.by | 185 |
| abstract_inverted_index.in | 12, 181 |
| abstract_inverted_index.it | 46 |
| abstract_inverted_index.of | 8, 86 |
| abstract_inverted_index.on | 101, 198 |
| abstract_inverted_index.to | 14, 49, 103, 130 |
| abstract_inverted_index.up | 102 |
| abstract_inverted_index.we | 78 |
| abstract_inverted_index.424 | 104 |
| abstract_inverted_index.GPU | 126 |
| abstract_inverted_index.and | 10, 23, 39, 57, 62, 73, 98, 118, 125, 132, 137, 147, 165, 176 |
| abstract_inverted_index.can | 30 |
| abstract_inverted_index.fit | 151 |
| abstract_inverted_index.for | 190 |
| abstract_inverted_index.how | 51, 157 |
| abstract_inverted_index.law | 153 |
| abstract_inverted_index.our | 111 |
| abstract_inverted_index.the | 134 |
| abstract_inverted_index.(DL) | 2 |
| abstract_inverted_index.Deep | 0 |
| abstract_inverted_index.deep | 53, 87, 192 |
| abstract_inverted_index.each | 140 |
| abstract_inverted_index.from | 34 |
| abstract_inverted_index.help | 174 |
| abstract_inverted_index.over | 80 |
| abstract_inverted_index.rate | 128 |
| abstract_inverted_index.such | 121 |
| abstract_inverted_index.that | 29, 155, 170 |
| abstract_inverted_index.this | 76 |
| abstract_inverted_index.time | 159 |
| abstract_inverted_index.vary | 114 |
| abstract_inverted_index.will | 173 |
| abstract_inverted_index.with | 36, 161, 195 |
| abstract_inverted_index.3,400 | 81 |
| abstract_inverted_index.array | 85 |
| abstract_inverted_index.carry | 40 |
| abstract_inverted_index.clock | 127 |
| abstract_inverted_index.guide | 177 |
| abstract_inverted_index.large | 37 |
| abstract_inverted_index.model | 142 |
| abstract_inverted_index.order | 13 |
| abstract_inverted_index.power | 123, 152 |
| abstract_inverted_index.rapid | 24 |
| abstract_inverted_index.such, | 45 |
| abstract_inverted_index.these | 171 |
| abstract_inverted_index.under | 144 |
| abstract_inverted_index.units | 107 |
| abstract_inverted_index.(DNNs) | 56 |
| abstract_inverted_index.During | 109 |
| abstract_inverted_index.Neural | 18 |
| abstract_inverted_index.across | 70 |
| abstract_inverted_index.budget | 7 |
| abstract_inverted_index.demand | 4 |
| abstract_inverted_index.energy | 11, 63, 166, 188 |
| abstract_inverted_index.gains. | 17 |
| abstract_inverted_index.impact | 197 |
| abstract_inverted_index.inform | 175 |
| abstract_inverted_index.limits | 129 |
| abstract_inverted_index.models | 38, 69, 154 |
| abstract_inverted_index.neural | 54 |
| abstract_inverted_index.paper, | 77 |
| abstract_inverted_index.scales | 160 |
| abstract_inverted_index.(GPUs). | 108 |
| abstract_inverted_index.achieve | 15 |
| abstract_inverted_index.becomes | 47 |
| abstract_inverted_index.capture | 131 |
| abstract_inverted_index.compute | 9, 61, 115, 163 |
| abstract_inverted_index.conduct | 79 |
| abstract_inverted_index.consume | 26 |
| abstract_inverted_index.domains | 72 |
| abstract_inverted_index.immense | 27 |
| abstract_inverted_index.impact. | 43 |
| abstract_inverted_index.minimal | 196 |
| abstract_inverted_index.natural | 93 |
| abstract_inverted_index.prevent | 31 |
| abstract_inverted_index.scaling | 138 |
| abstract_inverted_index.sweeps, | 22 |
| abstract_inverted_index.various | 90, 145 |
| abstract_inverted_index.vision, | 97 |
| abstract_inverted_index.computer | 96 |
| abstract_inverted_index.describe | 156 |
| abstract_inverted_index.exhibits | 143 |
| abstract_inverted_index.findings | 172 |
| abstract_inverted_index.graphics | 105 |
| abstract_inverted_index.language | 94 |
| abstract_inverted_index.learning | 1, 193 |
| abstract_inverted_index.leverage | 59 |
| abstract_inverted_index.networks | 55, 88 |
| abstract_inverted_index.outsized | 16 |
| abstract_inverted_index.reducing | 187 |
| abstract_inverted_index.regimes. | 149 |
| abstract_inverted_index.resource | 116, 146, 183 |
| abstract_inverted_index.training | 58, 83, 158 |
| abstract_inverted_index.available | 162 |
| abstract_inverted_index.behaviors | 139 |
| abstract_inverted_index.chemistry | 99 |
| abstract_inverted_index.computing | 179 |
| abstract_inverted_index.different | 52, 71, 135, 191 |
| abstract_inverted_index.essential | 48 |
| abstract_inverted_index.providers | 180 |
| abstract_inverted_index.resources | 28, 64, 164 |
| abstract_inverted_index.searches, | 20 |
| abstract_inverted_index.training, | 110 |
| abstract_inverted_index.training. | 199 |
| abstract_inverted_index.workflows | 3 |
| abstract_inverted_index.anticipate | 169 |
| abstract_inverted_index.especially | 66 |
| abstract_inverted_index.illustrate | 133 |
| abstract_inverted_index.increasing | 60 |
| abstract_inverted_index.mechanisms | 120 |
| abstract_inverted_index.optimizing | 182 |
| abstract_inverted_index.processing | 106 |
| abstract_inverted_index.trade-offs | 136 |
| abstract_inverted_index.understand | 50 |
| abstract_inverted_index.consumption | 189 |
| abstract_inverted_index.experiments | 82, 112 |
| abstract_inverted_index.processing, | 95 |
| abstract_inverted_index.prototyping | 25 |
| abstract_inverted_index.researchers | 33 |
| abstract_inverted_index.selectively | 186 |
| abstract_inverted_index.specialized | 67 |
| abstract_inverted_index.utilization | 124 |
| abstract_inverted_index.architecture | 19 |
| abstract_inverted_index.considerable | 41 |
| abstract_inverted_index.constraints. | 167 |
| abstract_inverted_index.representing | 89 |
| abstract_inverted_index.utilization, | 184 |
| abstract_inverted_index.applications. | 74 |
| abstract_inverted_index.domains/tasks | 91 |
| abstract_inverted_index.energy-saving | 119 |
| abstract_inverted_index.environmental | 42 |
| abstract_inverted_index.experimenting | 35 |
| abstract_inverted_index.hyperparameter | 21 |
| abstract_inverted_index.representative | 141 |
| abstract_inverted_index.systematically | 113 |
| abstract_inverted_index.characteristics | 117 |
| abstract_inverted_index.ever-increasing | 6 |
| abstract_inverted_index.tasks/workflows | 194 |
| abstract_inverted_index.high-performance | 178 |
| abstract_inverted_index.energy-constrained | 148 |
| abstract_inverted_index.resource-constrained | 32 |
| abstract_inverted_index.computationally-intensive | 68 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |