NAG adds to its Mac OS X product offering with platform availability for the Parallel Library
Parallel computation using NAG Library now available for Mac users
June 2006. The Numerical Algorithms Group increase their Mac OS X product offering to include the NAG Parallel Library. Designed specifically to enable applications to take advantage of distributed memory paralleled computers, the Parallel Library interfaces have been designed to be as close as possible to equivalent routines in the NAG Fortran Library in order to ease the parallelization of existing applications.
The addition of the NAG Parallel Library for Mac OS X adds to an already wide range of software components and tools available for the Mac user community. It joins the likes of NAG's IRIS Explorer, a sophisticated data visualization toolkit and the globally renowned NAG C Library, NAG Fortran Library, NAG Fortran 90 Library, NAG Data Mining Components and NAG's f95 compiler. The use of NAG software components and tools can significantly reduce development time as well as improve performance and robustness.
Rob Meyer, CEO of NAG further adds, "We are very committed to creating Mac platform versions of the full range of NAG technical software products. In addition to this release of the Parallel Library, NAG offers Mac users a complete range of tools for producing scientific and analytical applications, including mathematical, statistical, and data mining component libraries and Fortran compilers."
The NAG Parallel Library is aimed at typical applications required by industrial, commercial and research environments. The majority of the routines in the NAG Parallel Library was developed and tested in industrial applications by organizations such as Piaggio, British Aerospace, Thomson LCR, IBM, SEMEA and the Danish Hydraulic Institute.
The NAG Parallel Library delivers excellent performance and scalability across a wide range of systems, including SMP platforms, and its routines can be easily called from other languages.
NAG Parallel Library Key Features
- Solution of dense, banded and sparse linear equations
- Solution of eigenvalue problems
- Minimization of unconstrained nonlinear least squares and general nonlinear problems
- Fast Fourier transforms
- Numerical integration
- Random number generation
- Matrix operations and data distribution utilities
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