Request for Review: blacs, scalapack, R-RScaLAPACK

[Tom 'spot' Callaway]

All packages described can be found here:
http://www.auroralinux.org/people/spot/R/

Second chunk of R related packages for review, this time, I've got 
lapack's cousins, blacs, and scalapack, along with the first of many
R modules, RScaLAPACK. The R modules are so similar that the spec 
template is virtually identical for each module.

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blacs:
URL: http://www.netlib.org/blacs
SRPM: http://www.auroralinux.org/people/spot/R/blacs-1.1-3.src.rpm
SPEC: http://www.auroralinux.org/people/spot/R/blacs.spec

The BLACS (Basic Linear Algebra Communication Subprograms) project is
an ongoing investigation whose purpose is to create a linear algebra
oriented message passing interface that may be implemented efficiently
and uniformly across a large range of distributed memory platforms.

The length of time required to implement efficient distributed memory
algorithms makes it impractical to rewrite programs for every new
parallel machine. The BLACS exist in order to make linear algebra
applications both easier to program and more portable.

scalapack:
URL: http://www.netlib.org/scalapack/scalapack_home.html
SRPM: http://www.auroralinux.org/people/spot/R/scalapack-1.7-1.src.rpm
SPEC: http://www.auroralinux.org/people/spot/R/scalapack.spec

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.

ScaLAPACK is designed for heterogeneous computing and is
portable on any computer that supports MPI or PVM.

Like LAPACK, the ScaLAPACK routines are based on
block-partitioned algorithms in order to minimize the frequency
of data movement between different levels of the memory hierarchy.
(For such machines, the memory hierarchy includes the off-processor
memory of other processors, in addition to the hierarchy of registers,
cache, and local memory on each processor.) The fundamental building
blocks of the ScaLAPACK library are distributed memory versions (PBLAS)
of the Level 1, 2 and 3 BLAS, and a set of Basic Linear Algebra
Communication Subprograms (BLACS) for communication tasks that arise
frequently in parallel linear algebra computations. In the ScaLAPACK
routines, all interprocessor communication occurs within the PBLAS and the
BLACS. One of the design goals of ScaLAPACK was to have the ScaLAPACK
routines resemble their LAPACK equivalents as much as possible.

R-RScaLAPACK:
URL: http://cran.r-project.org/contrib
SRPM: http://www.auroralinux.org/people/spot/R/R-RScaLAPACK-0.4.0-1.src.rpm
SPEC: http://www.auroralinux.org/people/spot/R/R-RScaLAPACK.spec

R package:
An R add-on package capable of carrying out parallel computation through
a single function call from the R environment. It uses the high-performance
ScaLAPACK library for the linear algebra computations.

====

Thanks in advance,

~spot
-- 
Tom "spot" Callaway: Red Hat Sales Engineer || GPG Fingerprint: 93054260
Fedora Extras Steering Committee Member (RPM Standards and Practices)
Aurora Linux Project Leader: http://auroralinux.org
Lemurs, llamas, and sparcs, oh my!