The ScaLAPACK (or Scalable LAPACK) library includes a subset of LAPACK routines redesigned for distributed memory MIMD parallel computers. It is currently written in a Single-Program-Multiple-Data style using explicit message passing for interprocessor communication. It assumes matrices are laid out in a two-dimensional block cyclic decomposition.
As of version 2.0 the code base directly includes PBLAS and BLACS and has dropped support for PVM.
- Programming with Big Data in R fully utilizes ScaLAPACK and two-dimensional block cyclic decomposition for Big Data statistical analysis which is an extension to R.
- J. Dongarra and D. Walker. "The Design of Linear Algebra Libraries for High Performance Computers".
- J. Demmel, M. Heath, and H. van der Vorst. "Parallel Numerical Linear Algebra".
- "2d block-cyclic data layout".
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