dorsal/arxiv
View SchemaBasis-Spline Assisted Coded Computing: Strategies and Error Bounds
| Authors | Rimpi Borah, J. Harshan, V. Lalitha |
|---|---|
| Categories | |
| ArXiv ID | 2601.10616vv2 |
| URL | https://arxiv.org/abs/2601.10616 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Coded computing has become a key framework for reliable distributed computation over decentralized networks, effectively mitigating the impact of stragglers. Although there exists a wide range of coded computing methods to handle both polynomial and non-polynomial functions, computing methods for the latter class have received traction due its inherent challenges in reconstructing non-polynomial functions using a finite number of evaluations. Among them, the state-of-the-art method is Berrut Approximated coded computing, wherein Berrut interpolants, are used for approximating the non-polynomial function. However, since Berrut interpolants have global support characteristics, such methods are known to offer degraded accuracy when the number of stragglers is large. To address this challenge, we propose a coded computing framework based on cubic B-spline interpolation. In our approach, server-side function evaluations are reconstructed at the master node using B-splines, exploiting their local support and smoothness properties to enhance stability and accuracy. We provide a systematic methodology for integrating B-spline interpolation into coded computing and derive theoretical bounds on approximation error in terms of the number of servers and stragglers. Comparative analysis demonstrates that our framework significantly outperforms Berrut-based methods for various non-polynomial functions.
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"abstract": "Coded computing has become a key framework for reliable distributed computation over decentralized networks, effectively mitigating the impact of stragglers. Although there exists a wide range of coded computing methods to handle both polynomial and non-polynomial functions, computing methods for the latter class have received traction due its inherent challenges in reconstructing non-polynomial functions using a finite number of evaluations. Among them, the state-of-the-art method is Berrut Approximated coded computing, wherein Berrut interpolants, are used for approximating the non-polynomial function. However, since Berrut interpolants have global support characteristics, such methods are known to offer degraded accuracy when the number of stragglers is large. To address this challenge, we propose a coded computing framework based on cubic B-spline interpolation. In our approach, server-side function evaluations are reconstructed at the master node using B-splines, exploiting their local support and smoothness properties to enhance stability and accuracy. We provide a systematic methodology for integrating B-spline interpolation into coded computing and derive theoretical bounds on approximation error in terms of the number of servers and stragglers. Comparative analysis demonstrates that our framework significantly outperforms Berrut-based methods for various non-polynomial functions.",
"arxiv_id": "2601.10616",
"authors": [
"Rimpi Borah",
"J. Harshan",
"V. Lalitha"
],
"categories": [
"cs.IT",
"math.IT"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Basis-Spline Assisted Coded Computing: Strategies and Error Bounds",
"url": "https://arxiv.org/abs/2601.10616",
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