Coded Matrix Computation: Numerical Stability, Partial Stragglers, and Sparse Input Matrices

Explore high-dimensional matrix computations in distributed computing environments through this 43-minute talk by Aditya Ramamoorthy from Iowa State University. Delve into the challenges of large-scale distributed computing clusters, focusing on the problem of "stragglers" and their impact on job execution time. Learn how coding theory concepts are being adapted to address these issues, enabling result recovery with a threshold of completed worker node tasks. Examine the intersection of application-driven coding theory and matrix computations, considering aspects such as numerical stability, partial stragglers, and sparse input matrices. Gain insights into innovative approaches for enhancing the efficiency and reliability of matrix computations in machine learning and scientific computing applications.

Coded matrix computation: numerical stability, partial stragglers and sparse input matrices