#### Date of Degree

6-2022

#### Document Type

Dissertation

#### Degree Name

Ph.D.

#### Program

Computer Science

#### Advisor

Jun Li

#### Committee Members

Alexey Ovchinnikov

Victor Pan

Gueo Grantcharov

#### Subject Categories

Numerical Analysis and Scientific Computing | OS and Networks | Other Computer Sciences | Theory and Algorithms

#### Keywords

Coding Theory, Information Theory, Distributed Computing, Machine Learning, Matrix Multiplication

#### Abstract

A ubiquitous problem in computer science research is the optimization of computation on large data sets. Such computations are usually too large to be performed on one machine and therefore the task needs to be distributed amongst a network of machines. However, a common problem within distributed computing is the mitigation of delays caused by faulty machines. This can be performed by the use of coding theory to optimize the amount of redundancy needed to handle such faults. This problem differs from classical coding theory since it is concerned with the dynamic coded computation on data rather than just statically coding data without any consideration of the algorithms to be performed on said data. Of particular interest is the operation of matrix multiplication, a fundamental operation in many big data/machine learning algorithms, which is the main focus of this dissertation. Two serendipitous consequences of the (bi-)linear nature of matrix multiplication is that it is both highly parallelizable and that linear codes can be applied to it; making the coding theory approach a fruitful avenue of research for this particular optimization of distributed computing.

#### Recommended Citation

Soto, Pedro J., "Coded Distributed Function Computation" (2022). *CUNY Academic Works.*

https://academicworks.cuny.edu/gc_etds/4884

#### Included in

Numerical Analysis and Scientific Computing Commons, OS and Networks Commons, Other Computer Sciences Commons, Theory and Algorithms Commons