ECS289K (Fall 2003) References

General reference books

• Sourcebook of Parallel Computing, J. Dongarra et al editors, Morgan Kaufmann Publishers, ISBN 1-55860-871-0, 2003

Uniprocessor performance optimization and benchmarking

• David Parello and Olivier Temam and Jean-Marie Verdun, On increasing architecture awareness in program optimizations to bridge the gap between peak and sustained processor performance---matrix multiply revisited. Supercomputing, 2002
• R. Clint Whaley and Antoine Petitet and Jack Dongarra, Automated Empirical Optimizations of Software and the ATLAS Project, Parallel Computing, vol.27, pp3-25, 2001.
• Richard Vuduc, James W. Demmel, Katherine A. Yelick, Shoaib Kamil, Rajesh Nishtala, Benjamin Lee, Performance Optimizations and Bounds for Sparse Matrix-Vector Multiply , Supercomputing 2002.
• Richard Vuduc, Attila Gyulassy, James W. Demmel, Katherine A. Yelick, Memory Hierarchy Optimizations and Performance Bounds for Sparse ATA*x, ICCS 2003.
• Matteo Frigo and Stephen Johnson, FFTW: An Adaptive Software Architecture for the FFT, 1998.
• ATLAS: Automatically Tuned Linear Algebra Software (ATLAS)
• BEBOP: Berkeley benchmarking and optimization group

Cluster programming

• MPI home page
• Short MPI tutorial
• More detailed MPI tutorial
• P. Pacheco, A user's guide to MPI
• Norm Matloff's MPI web page
• MPICH implementation of MPI
• R. Choy, Building a Beowulf: Perspective and experience. (History and background on Beowulf clusters, and a description of one built in 2002.)

Parallel dense matrix computing

• R. van de Geijn and J. Watts, SUMMA: Scalable Universal Matrix Multiplication Algorithms, LAPACK Working Notes #96
• E. Elmorth et al: Recursive blocked algorithms and hybrid data structures for dense matrix library software
• ScaLAPACK, a subset of LAPACK routines redesigned for distributed memory MIMD parallel computers.

Sparse matrix computing

• R. Vuduc et al, Performance optimizations and bounds for sparse matrix-vector multiply
• Michele Benzi, Preconditioning techniques for large linear systems: A survey, J. of Comput. Physics, pp.418-477, vol.182, 2002.
• Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods is a hyper-text book on iterative methods for solving systems of linear equations.
• Templates for the Solution of Algebraic Eigenvalue Porblems: A Practical Guide is a hyper-text book on iterative methods for solving eigenvalue problems

Sparse matrix computing

• A. Pothen, H. Simon and K.-P. Liou, Partitioning sparse matrices with eigenvectors of graphs, SIAM J. Mat. Anal. Appl. 11:430-452, 1990.
• D. A. Spielman and S.-H. Teng, Spectral Partitioning Works: Planar graphs and finite element meshes