
On the Optimal Recovery Threshold of Coded Matrix Multiplication
We provide novel coded computation strategies for distributed matrixmat...
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Morphological Matrices as a Tool for Crowdsourced Ideation
Designing a novel product is a difficult task not well suited for nonex...
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Adaptive Private Distributed Matrix Multiplication
We consider the problem of designing codes with flexible rate (referred ...
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Private and Secure Distributed Matrix Multiplication with Flexible Communication Load
Large matrix multiplications are central to largescale machine learning...
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Secure Coded MultiParty Computation for Massive Matrix Operations
In this paper, we consider a secure multiparty computation problem (MPC...
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Distributed and Private Coded Matrix Computation with Flexible Communication Load
Tensor operations, such as matrix multiplication, are central to larges...
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Analog Lagrange Coded Computing
A distributed computing scenario is considered, where the computational ...
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ParityChecked Strassen Algorithm
To multiply astronomic matrices using parallel workers subject to straggling, we recommend interleaving checksums with some fast matrix multiplication algorithms. Nesting the paritychecked algorithms, we weave a product code flavor protection. Two demonstrative configurations are as follows: (A) 9 workers multiply two 2× 2 matrices; each worker multiplies two linear combinations of entries therein. Then the entry products sent from any 8 workers suffice to assemble the matrix product. (B) 754 workers multiply two 9× 9 matrices. With empirical frequency 99.8%, 729 workers suffice, wherein 729 is the complexity of the schoolbook algorithm. In general, we propose probabilitywisely favorable configurations whose numbers of workers are close to, if not less than, the thresholds of other codes (e.g., entangled polynomial code and PolyDot code). Our proposed scheme applies recursively, respects worker locality, incurs moderate pre and postprocesses, and extends over small finite fields.
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