MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866916131668754432 |
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| author | Yoon, Daegun Oh, Sangyoon |
| author_facet | Yoon, Daegun Oh, Sangyoon |
| contents | Gradient sparsification is a communication optimisation technique for scaling and accelerating distributed deep neural network (DNN) training. It reduces the increasing communication traffic for gradient aggregation. However, existing sparsifiers have poor scalability because of the high computational cost of gradient selection and/or increase in communication traffic. In particular, an increase in communication traffic is caused by gradient build-up and inappropriate threshold for gradient selection.
To address these challenges, we propose a novel gradient sparsification method called MiCRO. In MiCRO, the gradient vector is partitioned, and each partition is assigned to the corresponding worker. Each worker then selects gradients from its partition, and the aggregated gradients are free from gradient build-up. Moreover, MiCRO estimates the accurate threshold to maintain the communication traffic as per user requirement by minimising the compression ratio error. MiCRO enables near-zero cost gradient sparsification by solving existing problems that hinder the scalability and acceleration of distributed DNN training. In our extensive experiments, MiCRO outperformed state-of-the-art sparsifiers with an outstanding convergence rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00967 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training Yoon, Daegun Oh, Sangyoon Machine Learning Distributed, Parallel, and Cluster Computing Gradient sparsification is a communication optimisation technique for scaling and accelerating distributed deep neural network (DNN) training. It reduces the increasing communication traffic for gradient aggregation. However, existing sparsifiers have poor scalability because of the high computational cost of gradient selection and/or increase in communication traffic. In particular, an increase in communication traffic is caused by gradient build-up and inappropriate threshold for gradient selection. To address these challenges, we propose a novel gradient sparsification method called MiCRO. In MiCRO, the gradient vector is partitioned, and each partition is assigned to the corresponding worker. Each worker then selects gradients from its partition, and the aggregated gradients are free from gradient build-up. Moreover, MiCRO estimates the accurate threshold to maintain the communication traffic as per user requirement by minimising the compression ratio error. MiCRO enables near-zero cost gradient sparsification by solving existing problems that hinder the scalability and acceleration of distributed DNN training. In our extensive experiments, MiCRO outperformed state-of-the-art sparsifiers with an outstanding convergence rate. |
| title | MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2310.00967 |