Coded Distributed Computing With Pre-Set Data Placement and Output Functions Assignment

Coded distributed computing can reduce the communication load for distributed computing systems by introducing redundant computation and creating multicasting opportunities. However, the existing schemes require delicate data placement and output function assignment, which is not feasible when distributed nodes fetch data without the orchestration of a master node. In this paper, we consider the general systems where the data placement and output function assignment are arbitrary but pre-set. We propose two coded computing schemes, One-shot Coded Transmission (OSCT) and Few-shot Coded Transmission (FSCT), to reduce the communication load. Both schemes first group the nodes into clusters and divide the transmission of each cluster into multiple rounds, and then design coded transmission in each round to maximize the multicast gain. The key difference between OSCT and FSCT is that the former uses a one-shot transmission where each encoded message can be decoded independently by the intended nodes, while the latter allows each node to jointly decode multiple received symbols to achieve potentially larger multicast gains. Furthermore, based on the lower bound proposed by Yu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , we derive sufficient conditions for the optimality of OSCT and FSCT, respectively. This not only recovers the existing optimality results but also includes some cases where our schemes are optimal while others are not.

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Publication Details

Journal
IEEE Transactions on Information Theory
Published
2025-01-10
DOI
https://doi.org/10.1109/tit.2025.3528083
Citations
1
Primary Topic
Sparse and Compressive Sensing Techniques
Type
article
Field-Weighted Citation Impact
1.33

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article

Coded Distributed Computing With Pre-Set Data Placement and Output Functions Assignment

Youlong Wu, Yuhan Wang
1 citations
IEEE Transactions on Information Theory
Sparse and Compressive Sensing Techniques
1.33
article

Coded Distributed Computing With Pre-Set Data Placement and Output Functions Assignment

Youlong Wu, Yuhan Wang
article en
1 citations

Abstract

Coded distributed computing can reduce the communication load for distributed computing systems by introducing redundant computation and creating multicasting opportunities. However, the existing schemes require delicate data placement and output function assignment, which is not feasible when distributed nodes fetch data without the orchestration of a master node. In this paper, we consider the general systems where the data placement and output function assignment are arbitrary but pre-set. We propose two coded computing schemes, One-shot Coded Transmission (OSCT) and Few-shot Coded Transmission (FSCT), to reduce the communication load. Both schemes first group the nodes into clusters and divide the transmission of each cluster into multiple rounds, and then design coded transmission in each round to maximize the multicast gain. The key difference between OSCT and FSCT is that the former uses a one-shot transmission where each encoded message can be decoded independently by the intended nodes, while the latter allows each node to jointly decode multiple received symbols to achieve potentially larger multicast gains. Furthermore, based on the lower bound proposed by Yu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , we derive sufficient conditions for the optimality of OSCT and FSCT, respectively. This not only recovers the existing optimality results but also includes some cases where our schemes are optimal while others are not.

IEEE Transactions on Information TheoryVol. 71(3)
Chinese University of Hong Kong (HK), ShanghaiTech University (CN)
National Natural Science Foundation of China
Decent work and economic growth
Openalex Percentile: Top 28%
Sparse and Compressive Sensing Techniques
1.33
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Coded Distributed Computing With Pre-Set Data Placement and Output Functions Assignment — Youlong Wu, Yuhan Wang · IEEE Transactions on Information Theory (2025) | TGRS Research Map | TGRS