NAGnews 176
NAG delivers machine learning expertise via new Azure HPC & AI Collaboration Centre

We are delighted to announce NAG will provide machine learning expertise to Microsoft Azure users via the new Azure HPC & AI Collaboration Centre. In partnership with NVIDIA, this new centre will develop best practices for the deployment of scalable machine learning on the Cloud.

Over the years, NAG has partnered with Intel, AMD and Arm on the development of their high-performance software stacks. With NVIDIA, NAG has collaborated on GPU programming as a provider of CUDA consulting, training and products to customers around the globe. The deep relationship between NAG, Microsoft and NVIDIA makes for a perfect partnership for this Collaboration Centre.

NAG Machine Learning and Cloud HPC Expertise 

NAG has been at the forefront of HPC since its inception and is today leading the way in migrating large-scale workloads to the cloud. Working through the Azure Collaboration Centre, NAG’s decades of HPC experience will enable customers to harness scalable machine learning in a really easy way. As a Microsoft Azure Gold Partner, NAG is uniquely experienced in performing cloud migration whilst simultaneously optimizing the application for both cost and performance.

The Azure HPC & AI Collaboration Centre Program 

Microsoft Azure launched the Azure HPC & AI Collaboration Centre program to develop and share best practices with the HPC and AI communities. The Collaboration Centre program is delivered in partnership with AMD, Intel, and NVIDIA. See NAG’s CEO Adrian Tate talk about the Collaboration Centre here.

Fast Implied Volatility solved with very high accuracy

Very high accuracy is achieved using the new NAG Library solver for computing the implied volatility of a European option contract – even for the most difficult of volatility surfaces. The new solver features in the latest NAG Library available to download here.

High accuracy and better performance

The volatility surface can be highly curved, making it difficult to accurately compute the implied volatility. NAG’s new solver, developed in collaboration with Dr Kathrin Glau (and team) at Queen Mary University, London, gives high accuracy and better performance for large datasets. NAG Library users now have solver options for single precision or the Jäckel method for even higher accuracy. 

How to access the new NAG Library functionality

As with all new releases, we encourage NAG Library users to upgrade to the latest Mark to access the new content and performance improvements. NAG Library downloads are available here. The new Mark 27.1 functionality is also available in the NAG Library for Python.

If you don’t have access to the NAG Library and you’d like to try the new functionality, we offer full product trials. If you have any questions or need help, do get in touch with our Technical Support team

Scotiabank awarded for innovative Risk Engine – NAG at the core

Congratulations to our customer Scotiabank for winning the Technology Innovation of the Year Award for their XVA engine. NAG algorithmic differentiation software sits at the core of Scotiabank's risk engine. In a future edition of NAGnews, we will share more details about NAG’s contribution to this award-winning technology stack.

NAG Webinars
  • Calibrate models faster and improve performance 20%+ with DFO - 25 March 2021: 15:00 GMT - REGISTER 
  • A practical perspective to quantum computing - 22 April 2021: 14:00 & 20:00 BST - REGISTER