> My inclination would be to, whenever possible, replace the core scalar > libraries with compatible parallel versions (lapack -> scalapack), > rather than make it an add-on package. If the R client code is general > enough, and the make file can automatically find the parallel version, > then its a simple matter of compiling with the parallel libs. (Don't > know if this is possible at run-time.) No rewriting (high level) R code > at all. I tried to contact the plapack folks here at UT about > integrating with R, but it appears the project is no longer active.
Unfortunately, there is a major complication to this approach: the distribution of data. ScaLAPACK (and PLAPACK) requires the data to be distributed in a special way before calculation functions can be called. Given a generic R matrix, we have to distribute the data before we can call ScaLAPACK functions on it. We then have to collect the answer before we can return it to R. Because of this serious overhead, replacing all LAPACK calls with ScaLAPACK calls would not be recommended. Future versions of our package [1] may include some type of automatic benchmarking to decide when problems are large enough to be worth sending to ScaLAPACK. David Bauer [1] http://www.aspect-sdm.org/Parallel-R/ ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-devel
