Tessellated Distributed Computing
Ali Khalesi, Petros Elia

TL;DR
This paper introduces a novel approach to distributed computing that optimizes function reconstruction, communication, and computation costs by leveraging tessellated and SVD-based matrix factorization methods for sparse matrix decomposition.
Contribution
It proposes a new framework for distributed function computation using tessellated and SVD-based matrix factorization to minimize distortion, communication, and computing costs.
Findings
Developed tessellated-based matrix factorization method.
Designed SVD-based fixed support matrix factorization.
Achieved reduced reconstruction error and communication costs.
Abstract
The work considers the -server distributed computing scenario with users requesting functions that are linearly-decomposable over an arbitrary basis of real (potentially non-linear) subfunctions. In our problem, the aim is for each user to receive their function outputs, allowing for reduced reconstruction error (distortion) , reduced computing cost (; the fraction of subfunctions each server must compute), and reduced communication cost (; the fraction of users each server must connect to). For any given set of requested functions -- which is here represented by a coefficient matrix -- our problem is made equivalent to the open problem of sparse matrix factorization that seeks -- for a given parameter , representing the number of shots for each server -- to minimize the reconstruction distortion…
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Taxonomy
TopicsComputability, Logic, AI Algorithms
