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Flexible Coded Distributed Convolution Computing for Enhanced Straggler Resilience and Numerical Stability in Distributed CNNs

Created by
  • Haebom

Author

Shuo Tan, Rui Liu, Xuesong Han, XianLei Long, Kai Wan, Linqi Song, Yong Li

Outline

This paper proposes a Flexible Coded Distributed Convolution Computing (FCDCC) framework to address the problem of straggler nodes, which cause delays when deploying CNNs in resource-constrained environments. It extends the existing Coded Distributed Computing (CDC) with Circulant and Rotation Matrix Embedding (CRME) and applies it to high-dimensional tensor convolutions. The proposed technique, Numerically Stable Coded Tensor Convolution (NSCTC), introduces two novel coding partitioning techniques: Adaptive-Padding Coded Partitioning (APCP) for input tensors and Kernel-Channel Coded Partitioning (KCCP) for filter tensors. These strategies enable linear decomposition of tensor convolutions and encoding them into CDC subtasks, combining model parallelism and coded redundancy to deliver robust and efficient execution. Theoretical analysis identifies optimal trade-offs between communication and storage costs, and experimental results demonstrate computational efficiency, resilience to straggler nodes, and scalability across various CNN architectures.

Takeaways, Limitations

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Takeaways:
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We present a novel framework (FCDCC) to improve the efficiency and stability of distributed CNNs in resource-constrained environments.
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NSCTC technique improves computational efficiency, robustness against slow nodes, and scalability.
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Efficient partitioning and encoding of tensor convolutions is possible through new coding partitioning techniques called APCP and KCCP.
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Theoretical analysis of the optimal trade-off between communication and storage costs.
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Experimentally validated effectiveness across various CNN architectures.
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Limitations:
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Lack of details on the actual implementation and application of the proposed framework.
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Further analysis is needed to determine whether there is a dependency on specific hardware environments or CNN architectures.
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Performance evaluation of more diverse and complex CNN models is needed.
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Limitations of error correction capabilities and the need for further research.
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