Showing posts with label demo. Show all posts
Showing posts with label demo. Show all posts

Friday, 13 April 2012

NAG on the Cloud

I have been at NAG for 3 months now and one of my first tasks here was the topic of cloud computing. Customers have been inquiring as to whether they can utilize the NAG library on the hundreds of cores available on Cloud services like Microsoft's Azure and Amazons EC2. Below you will find a preliminary report of calling the NAG Library for .NET on Windows Azure.

I began with Microsoft's Cloud Numerics; a .NET analytical library that can easily be scaled out to Windows Azure for large computations. Cloud Numerics provides a library of about 400 Mathematical and Statistical functions that the user can call (in this case, from C#). Since NAG supplies the library in a .NET framework, I decided this was a good way to start.

Getting an account and all the correct software downloaded can be a challenge. I actually found this example quite useful for installation, setup, and deployment of Cloud Numerics on Azure.



To start calling NAG functions from the MSCloudNumerics example program, just add the NAG .NET dll under references and include the namespace NagLibrary. When you are ready to deploy the application to the cloud, right-click the 'AppConfigure' tab in the Solution Explorer and select 'Set as StartUp Project'. Then put in your Azure account information, create a cluster, and deploy!

Thursday, 1 March 2012

Adding functionality to Excel using the NAG Library for .NET

Much of our work at NAG is devoted to creating new implementations of our numerical libraries and attempting to make their algorithms available from as many languages and packages as possible, so that our users have access to them from whichever environment they're working in. Thus, users of packages such as MATLAB® (and similar packages such Octave), LabVIEW and Maple, and programmers working in languages like Java, Python and Visual Basic (along with, of course, more traditional languages such as C and Fortran) have all been making use of NAG algorithms to enhance their applications and solve numerical problems for a long time.

Microsoft Excel® users can easily access NAG routines from both the NAG Fortran Library and the NAG C Library, because they are distributed as Dynamic Link Libraries (DLLs). For example, my colleague Marcin Krzysztofik has recently described how to solve a nonlinear least-squares problem in Excel using the nag_opt_nlin_lsq (e04unc) routine from the NAG C Library.