In this issue
- NAG to broaden 64-bit ARMv8-A ecosystem with the NAG Library and Compiler
- New paper: Generating Realisations of Stationary Gaussian Random Fields by Circulant Embedding
- Mark 24 new functionality spotlight: New Matrix Functions in the NAG Library
- New case study: Business Intelligence software company uses NAG to add advanced solvers to their application
- More HPC Innovation Awards for NAG at SC13
- Implied Volatility using Python's Pandas Library - London Thalesians Seminar
- Events and Training
- Best of the blog
NAG to broaden 64-bit ARMv8-A ecosystem with the NAG Library and Compiler
NAG are delighted to announce a new technical collaboration with ARM®, the world's leading semiconductor intellectual property supplier. NAG's highly skilled team of HPC experts, numerical analysts and computer scientists will ensure the algorithms in the NAG Numerical Library and the facilities of the NAG Fortran Compiler are available for use on ARM's 64-bit ARMv8-A architecture-based platforms.
As part of this collaboration, NAG's trusted numerical software will be ported to the ARMv8-A architecture using the DS-5 Development Studio. NAG Library routines underpin thousands of applications all over the world that require cutting edge numerical capabilities, thus enabling fast and accurate results of complex computation. By working with NAG, ARM is greatly enhancing the strong HPC infrastructure for ARMv8-A architecture through the enablement of numerical computation at its release.
Learn more about this here.
New paper: Generating Realisations of Stationary Gaussian Random Fields by Circulant Embedding
By Catherine E. Powell, School of Mathematics, University of Manchester, UK
Random fields are families of random variables, indexed by a d-dimensional parameter x with d > 1. They are important in many applications and are used, for example, to model properties of biological tissue, velocity fields in turbulent flows and permeability coefficients of rocks. Mark 24 of the NAG Fortran Library includes new routines for generating realisations of stationary Gaussian random fields using the method of circulant embedding. This short note illustrates the main ideas behind circulant embedding and how to use the routines g05zr and g05zs in the NAG Toolbox for MATLAB. The routines g05zm, g05zn and g05zp can also be used to generate realisations of stationary Gaussian stochastic processes (the d = 1 case).
Read the new paper here.
Mark 24 functionality spotlight: New Matrix Functions in the NAG Library
In today's issue we shine the spotlight on the new matrix functions at Mark 24 of the Library. The article linked below briefly discusses some of the theoretical background concerning functions of matrices. We will then describe some of the many applications that matrix functions have found in science and engineering, due to the succinct way that they allow problems to be formulated and solutions to be expressed. Finally we will give an overview of the new functionality in the NAG Library.
Read the Matrix Functions mini-article here.
Case study: Business Intelligence software company uses NAG to add advanced solvers to their application
Mercur Solutions AB is a Swedish company with over 30 years' experience of delivering solutions for performance management and business intelligence. One of their key products, Visual Mercur, is a development platform which enables calculations within data tables and the storage, extraction and selection of data from tables in the platform's built-in database. Its functionality is thus similar to both spreadsheets and relational databases. The Business Process Management / Business Intelligence application Mercur Business Control is built with Visual Mercur and is used by hundreds of companies for decentralized budgeting, forecasting and follow-up, reporting, analysis and financial consolidations, typically in a multiuser and Internet-based environment.
Visual Mercur's Autocalc feature uses NAG software to solve the numerical problems that arise in the processing of the table data. More specifically, it calls routines from the F01 (Matrix Operations) and the C05 (Roots of Transcendental Equations) chapters of the NAG Library in order to solve large systems of linear and non-linear equations. Mr Sven Frenkel, who is in charge of development of Visual Mercur, says: "We chose NAG because the NAG routines covered our needs very well, have high quality and because NAG is well-known in the academic community from where we obtain help and advice with some of the mathematics and the numerical methods that are used in the Autocalc mechanisms."
Read the full story here.
More HPC Innovation Awards for NAG at SC13
NAG is delighted to have received another two HPC Innovation Excellence Awards - this time at the global supercomputing conference, SC13, in Denver recently. The awards recognised NAG's HPC software innovations on two projects, CABARET and INCOMPACT3D, undertaken by NAG's Computational Science and Engineering (CSE) Service, part of the UK's HECToR national supercomputing service. The judging panel - the HPC User Forum Steering Committee on behalf of awarding body IDC- agreed that NAG's contribution to both CABARET and INCOMPACT3D demonstrated high levels of HPC innovation. These two projects recognised by the HPC Innovation Awards at the world's largest supercomputing conference are among over 50 similarly successful application performance improvement projects within NAG's HECToR CSE Service. Further information can found below:
- Capability of CABARET Aeroacoustics and Geophysical Fluid Dynamics Solvers Improved by HECToR CSE Team with Queen Mary University of London; and
- Performance and Capability of Turbulence Flow Solver, Incompact3D Further Improved by HECToR CSE Team with Imperial College London.
Implied Volatility using Python's Pandas Library - London Thalesians Seminar
ABSTRACT: Python has some nice packages such as numpy, scipy and matplotlib for numerical computing and data visualization. Another package that deserves a mention that we have seen increasingly is Python's pandas library. Pandas has fast and efficient data analysis tools to store and process large amounts of data. We present an example using NAG's Python bindings and the pandas library to calculate the implied volatility of options prices. Additionally we will fit varying degrees of polynomials to the curves, examine the volatility surface, and look at the limitations of numerical computing in Python.
SPEAKER: Brian Spector is a Technical Consultant at Numerical Algorithms Group in Lisle, Illinois, USA. He works closely with NAG's customers very often with those from financial services providing technical support and/or delivering services. Brian has worked on number of projects including different methods of portfolio optimization, implied volatility, and a Gaussian mixture model algorithm. Recently, he has been developing the Python bindings for the NAG Library. Brian has a Master's degree in Math from Carnegie Mellon University with concentration in Applied Analysis and is currently working on his Master's in Financial Math from the University of Chicago.
WHEN: Wednesday, January 15, 2014 - 7:30 PM
WHERE: Dockmaster's House
1 Hertsmere Road, Canary Wharf, London E14 8JJ, London (map)
Price: GBP15.00/per person
Note registration is required because there is a charge for the event and the hosts (Thalesians) need to recover the cost of the venue. If you are not yet a Thalesian then you will need to join before registration. Thalesian membership is free of charge.
Events & Training Courses
- Actuarial and Financial Mathematics Conference
6-7 February 2014, Brussels
- HPCFinance Conference
12-14 February 2014, London
- 11th German Probability and Statistics Days
4-7 March 2014, University of Ulm
Training Courses Provided by NAG's HECToR Team*
7-9 January 2014, Imperial College London
- Parallel Programming with MPI
21-23 January 2014, Imperial College London
- Debugging, Profiling and Optimizing
21-23 January 2014, University of Leeds
*These HPC training courses are provided free of charge to HECToR users and UK academics whose work is covered by the remit of one of the participating research councils (EPSRC, NERC and BBSRC). The courses are also open to non-eligible people but will require payment of a course fee. Please see the eligibility page for more details.
The Best of the Blog
Using the NAG Compiler with the NAG Fortran Library (Mark 24) on Windows
Blog snippet: The NAG Fortran Compiler is an excellent compiler for checking and running your Fortran code. We use it extensively here at NAG to ensure that our code for the library complies with the current Fortran standards.
Personally whenever I have a user problem report that I can't resolve by inspection my first instinct is to run the user's code with the Compiler. Frequently this identifies the error immediately.
As I am a Windows user, I am able to make use of the Integrated Development Environment (IDE) for the compiler that is provided to our Windows users. We call this IDE 'NAG Fortran Builder'. One of the nice features of this IDE is its ease of use with the prevailing NAG libraries. To do this the user normally specifies a 'NAG Library Project' at creation time; thereafter the relevant settings are made to the compiler, so that either the Windows 64-bit or Windows 32-bit DLLs are used.
Read the full post by David Sayers here.
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