FRED: Flexible REduction-Distribution Interconnect and Communication Implementation for Wafer-Scale Distributed Training of DNN Models Article Swipe
Saeed Rashidi
,
William Won
,
Sudarshan K. Srinivasan
,
Puneet Gupta
,
Tushar Krishna
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.19580
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.19580
Distributed Deep Neural Network (DNN) training is a technique to reduce the training overhead by distributing the training tasks into multiple accelerators, according to a parallelization strategy. However, high-performance compute and interconnects are needed for maximum speed-up and linear scaling of the system. Wafer-scale systems are a promising technology that allows for tightly integrating high-end accelerators with high-speed wafer-scale interconnects, making it an attractive platform for distributed training. However, the wafer-scale interconnect should offer high performance and flexibility for various parallelization strategies to enable maximum optimizations for compute and memory usage. In this paper, we propose FRED, a wafer-scale interconnect that is tailored for the high-BW requirements of wafer-scale networks and can efficiently execute communication patterns of different parallelization strategies. Furthermore, FRED supports in-switch collective communication execution that reduces the network traffic by approximately 2X. Our results show that FRED can improve the average end-to-end training time of ResNet-152, Transformer-17B, GPT-3, and Transformer-1T by 1.76X, 1.87X, 1.34X, and 1.4X, respectively when compared to a baseline waferscale 2D-Mesh fabric.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.19580
- https://arxiv.org/pdf/2406.19580
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400222482
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400222482Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2406.19580Digital Object Identifier
- Title
-
FRED: Flexible REduction-Distribution Interconnect and Communication Implementation for Wafer-Scale Distributed Training of DNN ModelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-28Full publication date if available
- Authors
-
Saeed Rashidi, William Won, Sudarshan K. Srinivasan, Puneet Gupta, Tushar KrishnaList of authors in order
- Landing page
-
https://arxiv.org/abs/2406.19580Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2406.19580Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2406.19580Direct OA link when available
- Concepts
-
Reduction (mathematics), Interconnection, Scale (ratio), Computer science, Training (meteorology), Wafer, Distribution (mathematics), Distributed computing, Embedded system, Computer network, Engineering, Electrical engineering, Mathematics, Geography, Cartography, Mathematical analysis, Meteorology, GeometryTop 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.ResNet-152, | 148 |
| abstract_inverted_index.Wafer-scale | 43 |
| abstract_inverted_index.distributed | 66 |
| abstract_inverted_index.efficiently | 112 |
| abstract_inverted_index.flexibility | 77 |
| abstract_inverted_index.integrating | 53 |
| abstract_inverted_index.performance | 75 |
| abstract_inverted_index.strategies. | 119 |
| abstract_inverted_index.wafer-scale | 58, 70, 98, 108 |
| abstract_inverted_index.Furthermore, | 120 |
| abstract_inverted_index.accelerators | 55 |
| abstract_inverted_index.distributing | 15 |
| abstract_inverted_index.interconnect | 71, 99 |
| abstract_inverted_index.requirements | 106 |
| abstract_inverted_index.respectively | 159 |
| abstract_inverted_index.accelerators, | 21 |
| abstract_inverted_index.approximately | 133 |
| abstract_inverted_index.communication | 114, 125 |
| abstract_inverted_index.interconnects | 31 |
| abstract_inverted_index.optimizations | 85 |
| abstract_inverted_index.Transformer-1T | 152 |
| abstract_inverted_index.interconnects, | 59 |
| abstract_inverted_index.parallelization | 25, 80, 118 |
| abstract_inverted_index.Transformer-17B, | 149 |
| abstract_inverted_index.high-performance | 28 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |