Improving LAPACK and ScaLAPACK (LAPACK)
Yozo Hida, Jason Riedy, Christof Vömel1, Xiaoye Li2, Osni Marques3, William M. Kahan, Beresford N. Parlett, James Demmel, Ming Gu4 and Vasily Volkov
Users and distributors of LAPACK and ScaLAPACK expect these libraries to represent the state of the art in high-performance dense linear algebra. Numerical algorithms, implementation techniques, and computer architectures have progressed significantly since these libraries' last major update. With collaborators around the world, we are incorporating the latest advances to address user demands as collected in an ongoing survey. We are also growing a community to support for long-term, continuous development.
The improvements address accuracy, performance, functionality, engineering, and ease of use. The prospectus  surveys the entire project. Selected Berkeley thrusts include:
- Iterative refinement for linear systems  and least square problems  to provide excellent accuracy and dependable error estimates;
- Use of IEEE-754 arithmetic features for performance and reliability ;
- Exploration of using high-performance coprocessors and graphics adapters;
- Implementing parallel ScaLAPACK routines to include more LAPACK functionality; and
- Integration of higher arithmetic precisions throughout LAPACK and ScaLAPACK.
Other functionality being incorporated includes automatic tuning of parameters and parallel data distributions, factorization updating facilities, quadratic eigenvalue problems, matrix functions, high-accuracy Jacobi SVD routines, pivoting and scaling for symmetric linear systems, recursive data layouts, fast Hessenberg QR, generalized SVDs, and many more.
- J. Demmel and J. Dongarra, "LAPACK 2005 Prospectus: Reliable and Scalable Software for Linear Algebra Computations on High End Computers," LAPACK Working Note 164, February 2005. http://www.netlib.org/lapack/lawnspdf/lawn164.pdf.
- J. Demmel, Y. Hida, W. Kahan, X. Li, S. Mukherjee, and J. Riedy, "Error Bounds from Extra Precise Iterative Refinement," ACM Trans. Mathematical Software (TOMS), Vol. 32, No. 2, June 2006, pp. 325-351. DOI: 10.1145/1141885.1141894
- J. Demmel, Y. Hida, X. Li, and J. Riedy, "Extra-Precise Iterative Refinement for Overdetermined Least Squares Problems," LAPACK Working Note 188, May 2007 (submitted).
- O. A. Marques, E. J. Riedy, and C. Vömel, "Benefits of IEEE-754 Features in Modern Symmetric Tridiagonal Eigensolvers," SIAM Journal on Scientific Computing, Vol. 28, No. 5, 2006, pp. 1613-1633. DOI: 10.1137/050641624
1NERSC / Lawrence Berkeley National Laboratory
2NERSC / Lawrence Berkeley National Laboratory
3NERSC / Lawrence Berkeley National Laboratory