[R-pkgs] SPRINT 1.0.5 release (parallelised R functions for complex analysis and large data sets)

[SPRINT Project]

Dear All

We have recently released SPRINT v1.0.5. SPRINT provides parallelised  
versions of existing R functions (e.g. statistical, machine learning,  
utility) that often exceed computational limits (speed or memory) with  
large or complex data sets.


SPRINT 1.0.5 now:

-> runs on Mac OSX multi-cores (i.e. you may not need to obtain access  
to compute clusters)

-> provides a parallelised version of the Hamming distance (based on  
original version contained in stringdistmatrix() by M van der Loo, and  
parallelised differently from the option provided therein)

-> compliant with MPI3 (and the latest version of the mpich package)


Download and instructions available at www.r-sprint.org.

SPRINT 1.0.5 is not available on CRAN as we have not yet completed  
testing against the latest major release of R (3.0.x). However, we  
will release a small incremental update of SPRINT as version 1.0.6  
within the next few weeks that will be available via CRAN and will  
work with R3.


Notes:
- With the availability of SPRINT for Mac users, we hope to address  
computational issues that are too large or too slow for these users,  
but still small enough to not require a much larger number of nodes  
and cores (i.e. compute clusters or supercomputers like HECToR or  
ARCHER).
- We have parallelised the Hamming distance as from collaborators and  
course participants we know this to have potential in use for  
Next-Gen-Sequencing, e.g. measuring pairwise distances between  
nucleotide sequences. In principle, though, Hamming is a distance  
useful for string or binary data vectors.



SPRINT Project

sprint at ed.ac.uk
www.r-sprint.org

The University of Edinburgh

-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.