Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully g...
Posts by Year
2024
Algebraic Geometric Rook Codes for Coded Distributed Computing
Tensors are a fundamental operation in distributed and are commonly distributed into multiple parallel tasks for large datasets. Stragglers and other failure...
Faster Groebner bases for Lie derivatives of ODE systems via monomial orderings
Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial o...
2023
Root-Squaring for Root-Finding
The root-squaring iterations of Dandelin (1826), Lobachevsky (1834), and Gräffe (1837) recursively produce the coefficients of polynomials whose zeros are th...
2022
Quantile Formulation for Optimization under a Qualitative Risk Constraint
This paper focuses on the pathologies of common gradient-based algorithms for solving optimization problems under probability constraints. These problems are...
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This pape...
Rook Coding for Batch Matrix Multiplication
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to multiply large matrices, it is common practice...
2021
Coded Matrix Chain Multiplication
Matrix multiplication is a fundamental building block in many machine learning models. As the input matrices may be too large to be multiplied on a single se...
2020
Straggler-free Coding for Concurrent Matrix Multiplications
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to compute the multiplication of large matrices, ...
Leveraging Stragglers in Coded Computing with Heterogeneous Servers
With the increasing sizes of models and datasets, it has become a common practice to split machine learning jobs as multiple tasks. However, stragglers are i...
2019
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
Matrix multiplication is a fundamental building block in various machine learning algorithms. When the matrix comes from a large dataset, the multiplication ...