Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices Article Swipe
Shengyuan Ye
,
Liekang Zeng
,
Xiaowen Chu
,
Guoliang Xing
,
Xu Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.08015
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.08015
On-device Deep Neural Network (DNN) training has been recognized as crucial for privacy-preserving machine learning at the edge. However, the intensive training workload and limited onboard computing resources pose significant challenges to the availability and efficiency of model training. While existing works address these challenges through native resource management optimization, we instead leverage our observation that edge environments usually comprise a rich set of accompanying trusted edge devices with idle resources beyond a single terminal. We propose Asteroid, a distributed edge training system that breaks the resource walls across heterogeneous edge devices for efficient model training acceleration. Asteroid adopts a hybrid pipeline parallelism to orchestrate distributed training, along with a judicious parallelism planning for maximizing throughput under certain resource constraints. Furthermore, a fault-tolerant yet lightweight pipeline replay mechanism is developed to tame the device-level dynamics for training robustness and performance stability. We implement Asteroid on heterogeneous edge devices with both vision and language models, demonstrating up to 12.2x faster training than conventional parallelism methods and 2.1x faster than state-of-the-art hybrid parallelism methods through evaluations. Furthermore, Asteroid can recover training pipeline 14x faster than baseline methods while preserving comparable throughput despite unexpected device exiting and failure.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2408.08015
- https://arxiv.org/pdf/2408.08015
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406023331
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4406023331Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2408.08015Digital Object Identifier
- Title
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Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge DevicesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-08-15Full publication date if available
- Authors
-
Shengyuan Ye, Liekang Zeng, Xiaowen Chu, Guoliang Xing, Xu ChenList of authors in order
- Landing page
-
https://arxiv.org/abs/2408.08015Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2408.08015Direct link to full text PDF
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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/2408.08015Direct OA link when available
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Pipeline (software), Parallelism (grammar), Enhanced Data Rates for GSM Evolution, Computer science, Resource (disambiguation), Training (meteorology), Distributed computing, Parallel computing, Computer architecture, Computer network, Artificial intelligence, Operating system, Geography, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.training, | 106 |
| abstract_inverted_index.training. | 38 |
| abstract_inverted_index.challenges | 30, 44 |
| abstract_inverted_index.comparable | 187 |
| abstract_inverted_index.efficiency | 35 |
| abstract_inverted_index.management | 48 |
| abstract_inverted_index.maximizing | 114 |
| abstract_inverted_index.preserving | 186 |
| abstract_inverted_index.recognized | 8 |
| abstract_inverted_index.robustness | 137 |
| abstract_inverted_index.stability. | 140 |
| abstract_inverted_index.throughput | 115, 188 |
| abstract_inverted_index.unexpected | 190 |
| abstract_inverted_index.distributed | 79, 105 |
| abstract_inverted_index.lightweight | 124 |
| abstract_inverted_index.observation | 54 |
| abstract_inverted_index.orchestrate | 104 |
| abstract_inverted_index.parallelism | 102, 111, 162, 170 |
| abstract_inverted_index.performance | 139 |
| abstract_inverted_index.significant | 29 |
| abstract_inverted_index.Furthermore, | 120, 174 |
| abstract_inverted_index.accompanying | 64 |
| abstract_inverted_index.availability | 33 |
| abstract_inverted_index.constraints. | 119 |
| abstract_inverted_index.conventional | 161 |
| abstract_inverted_index.device-level | 133 |
| abstract_inverted_index.environments | 57 |
| abstract_inverted_index.evaluations. | 173 |
| abstract_inverted_index.acceleration. | 96 |
| abstract_inverted_index.demonstrating | 154 |
| abstract_inverted_index.heterogeneous | 89, 145 |
| abstract_inverted_index.optimization, | 49 |
| abstract_inverted_index.fault-tolerant | 122 |
| abstract_inverted_index.state-of-the-art | 168 |
| abstract_inverted_index.privacy-preserving | 12 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |