“The Benefits of Using Rigorously Tested Routines from Numerical Libraries” white paper is geared to help developers of new electronics products understand how and why to incorporate use of extensively documented numerical libraries into their application development practices.
The subject matter discussed in the NAG Library Guide white paper is of growing importance to a wide range of finance, industrial, business, scientific research, and engineering applications because of recent multicore processor developments and the emergence of GPU chips and/or widespread access to high performance computing (HPC) resources.
Rob Meyer, NAG CEO and author of this white paper explains, “This white paper–“The Benefits of Using Rigorously Tested Routines from Numerical Libraries—Electronics Engineering Edition” speaks to matters affecting computationally-intensive new product simulations of electronic components and the mathematical and statistical methods that ultimately affect time-to-market. To the extent that electrical and electronics engineers’ work involves significant numerical computations, it is timely to re-examine how computational frameworks are or are not designed for maximum performance.”
Meyer continues, “The Numerical Algorithms Group (NAG), a global not-for-profit numerical software development organization that collaborates with world-leading researchers and practitioners in academia and industry, devotes considerable resources to ongoing development of what is arguably the world’s most extensive and rigorously tested numerical library—the NAG Library-- available to application developers in C+, C#, F#, FORTRAN, MATLAB, R, Maple and other environment including routines tuned for multi-core and parallel hardware configurations. In recent years, it became clear that many researchers in a wide range of disciplines have yet to grasp that we have entered a period where investments in software, not hardware, matter most. We penned this white paper to help educate a wide range of technical application developers on how they can use numerical libraries to develop software on par with the processing capabilities of multicore systems and HPC computing environments.”