Erin Carson

I am a PhD student in Computer Science at U.C. Berkeley, advised by James Demmel and Armando Fox.
I am affiliated with the Berkeley Benchmarking and Optimization Group (BeBOp) within the Parallel Computing Laboratory (ParLab).
I am also a student in the Designated Emphasis in Computational Science and Engineering Program.
My research sits at the intersection of high performance computing, parallel algorithms, scientific computing, and numerical linear algebra.

Office

586E Soda Hall Computer Science Division University of California at Berkeley Berkeley, CA 94720-1776 Email: ecc2z@cs.berkeley.edu

E. Carson, N. Knight, and J. Demmel. Avoiding communication in two-sided Krylov subspace methods. SIAM J. Sci. Comp. (to appear), 2013. E. Carson and J. Demmel. A residual replacement strategy for improving the maximum attainable accuracy of s-step Krylov subspace methods. SIAM J. Matrix Anal. Appl. (in review), 2012.

E. Carson, N. Knight, and J. Demmel. Exploiting data sparsity in parallel matrix powers computations. In Proc. ACM Symposium on Parallelism in Algorithms and Architectures (submitted), Jul 2013.

E. Carson, N. Knight, and J. Demmel. Avoiding communication in two-sided Krylov subspace methods. Technical Report UCB/EECS-2011-93, EECS Dept., U.C. Berkeley, Aug 2011. [pdf] E. Carson and J. Demmel. A residual replacement strategy for improving the maximum attainable accuracy of s-step Krylov subspace methods. Technical Report UCB/EECS-2012-197, EECS Dept., U.C. Berkeley, Sept. 2012. [pdf]

E. Carson, N. Knight, and J. Demmel. Hypergraph partitioning for computing matrix powers (extended abstract). In Proc. Fifth SIAM Workshop on Comb. Sci. Comput., pages 31–33, May 2011. [pdf] E. Carson and J. Demmel. A residual replacement strategy for improving the maximum attainable accuracy of communication-avoiding Krylov subspace methods (extended abstract). In Proc. 9th International Workshop on Accurate Solution of Eigenvalue Problems, pages 19–21, Jun 2012. E. Carson, N. Knight, and J. Demmel. Exploiting low-rank structure in computing matrix powers with applications to preconditioning (abstract). In Proc. SIAM Conference on Parallel Processing for Scientific Computing, Feb 2012. [ pdf | pptx ] E. Carson, N. Knight, and J. Demmel. Improving the stability of communication-avoiding Krylov subspace methods (abstract). In Proc. SIAM Conference on Applied Linear Algebra, Jun 2012. N. Knight, E. Carson, and J. Demmel. Avoiding communication with hierarchical matrices (abstract). In Proc. SIAM Conference on Applied Linear Algebra, Jun 2012. E. Carson and J. Demmel. Efficient deflation for communication avoiding Krylov methods (extended abstract). In Proc. Numerical Analysis and Scientific Computation with Applications (to appear), Jun 2013.

G. Ballard, E. Carson, and N. Knight, Algorithmic-based fault tolerance for matrix multiplication on Amazon EC2, (2009). [pdf]
J. Carnahan, S. Policastro, E. Carson, P. Reynolds Jr., and R. Kelly, Using flexible points in a developing simulation of selective dissolution in alloys, in Proceedings of the 39th conference on Winter simulation, IEEE Press, 2007, pp. 891-899. [ACM-DL]

U.C. Berkeley Math 54: Linear Algebra and Differential Equations, Spring 2011. Instructor: Constantin Teleman. Topics: Basic linear algebra; matrix arithmetic and determinants. Vector spaces; inner product as spaces. Eigenvalues and eigenvectors; linear transformations. Homogeneous ordinary differential equations; first-order differential equations with constant coefficients. Fourier series and partial differential equations.

CS 101: Introduction to CS, Fall 2007. Instructor: Kevin Sullivan and Greg Humphreys.
CS 101x: Introduction to CS (for non-engineers), Fall 2007. Instructor: Jim Cohoon.
CS 202: Discrete Mathematics, Spring & Fall 2008. Instructors: Paul Reynolds and John Knight.

Since 2009, I've served as a Feature Editor for ACM's XRDS Magazine.