ScaLAPACK (Scalable LAPACK) is a linear algebra library for parallel computers. Routines are available to diagonalize or solve dense and narrow band systems of linear equations.
ScaLAPACK implements block-oriented LAPACK linear algebra routines, adding a special set of communication routines to copy blocks of data between processors as required. Similar to LAPACK, a single subroutine call typically carries out the necessary computations.
ScaLAPACK installations include the following libraries: scalapack, redist, pblas, tools, blacs, blacsCinit, and blacsF77init.
An meeting with Professor Choi J. which a member of ScaLAPACK
- What is the Block-Oriented?
- Why use shared memory instead of files?
- Are there any ways (or methods) to handle to files?
- What are the relative merits and demerits of the ScaLAPACK?
- Performance Results?
And, In the last question
- What do you think about the Hama approaches?
- Is Hama a good fit with a blocked algorithms?