NAGnews 162

In this issue:

New online HPC TCO Calculator - how can you make HPC decisions if you haven’t mapped the costs?

NAG’s Andrew Jones is well known for his impartial tutorials on the business aspects of HPC, including TCO, at various conferences such as Supercomputing each November. At SC18, Andrew shared a simple TCO model with the attendees and from the audience crowdsourced values to populate the model. Andrew and the audience then used the model to explore a few typical HPC decisions, such as cloud vs on-premise.

The NAG team took Andrew’s model and have turned it into a free online calculator. Our aim was to provide this new impartial tool so that anyone could calculate and explore costs of different options for HPC, including cloud vs on-premise.

We thought it would be fun to see what NAG Technical Evangelist, Mike Croucher thought of the TCO calculator, so asked him to make a short 6 minute video review for our YouTube channel. Check it out here.

Please do contact us for advice on using the tool, help with building your own model appropriate to your situation, or for anything else we might be able to help with. 

Team Sonnenwagen Optimize Solar Racing Car Aerodynamics with NAG’s dco/c++

Team Sonnenwagen is a group of students from the Aachen Universities. They participated in the ‘World Solar Challenge’ (WSC) for the first time in 2017. The WSC is considered the most important race for solar-car teams on the international circuit. 40 teams from five continents compete against each other, all trying to complete a gruelling 3022 km stretch through the Australian outback -all the way from Darwin to Adelaide. The 2019 WSC sees the team bring an additional car to the race in the ‘Challenger’ class of vehicles, in their quest to capture a Top 3 podium finish. A very intensive development period is currently underway in the hope of the team achieving their mission.

Of course, the aerodynamics of the team’s cars are of paramount importance, and as such, this area has been put under a strict process. In early 2018, the team began a concept study. For validating the CFD (computational fluid dynamics) simulations, wind tunnel tests were conducted on the older car. After a nerve-wracking decision phase involving the concepts, a parameter study was initiated based on the validated simulation model. Various parameters, including for instance the radii of the edge fillets, or the angle of the trailing edge were observed in isolation. This allowed the team to perform the optimization step-by-step.

Learn how the Team Sonnenwagen used NAG’s Algorithmic Differentiation Tool dco/c++ to optimize the car’s aerodynamics here

NAG Algorithmic Differentiation Tool dco/c++ now with Vectorization Support

We were delighted to announce the latest update to dco/c++, an Algorithmic Differentiation (AD) software tool for computing sensitivities of C++ codes, at the QuantMinds Vienna conference recently. Learn more about the new features, including Vectorization Support here

Latest AD Blog: On Vectorization of Algorithmic Adjoint C++ Code

NAG and Machine Learning Institute in Partnership

We are delighted to announce our partnership with the Machine Learning Institute (MLI) , part of WBS (World Business Strategies Ltd) . NAG’s Mike Croucher has joined the MLI faculty and will deliver lectures to future cohorts of students.

Find out more information via The Machine Learning Institute Certificate in Finance  

Celebrating the new partnership MLI are providing additional reductions off their early bird discount rates for the next cohort. Use NAGAlgo on the Booking page, enter this code in the “Special Discount Code” box and “Check Code” to get the special rates. This will then provide further reductions. [Standard discount rates for early booking are 25% until 21st June 2019 and 15% until 13th September 2019]

PS MLI students also have access to the NAG Library for Python

Out & About with NAG

Exhibitions, Conferences, Trade Shows and Webinars

ISC High Performance
Frankfurt, 16-20 June 2019

NAG Fortran Modernization Workshop
Rome, 26-27 June 2019

The 15th WBS Quantitative Finance Conference
Rome, 16-18 October 2019

Denver, 17-19 November 2019


Best of the Blog

The three levels of interface in the NAG Library for Python

In an earlier blog post, we discussed the technology we use at NAG that makes use of our XML documentation to create interfaces to the NAG Library Engine. In this way, we can semi-automatically create idiomatic interfaces to our core algorithms, which are mainly written in Fortran and C, in languages such as MATLAB® and Python. It is also the technology behind our in-development C++ interface. Read the blog

Using the NAG Library for Python with Kdb+ and PyQ

In this paper, NAG's Christopher Brandt illustrates how to use the NAG Library for Python with kdb+ and PyQ. PyQ is an extension to kdb+ featuring zero-copy sharing of data between Python and the q programming language. Included in the paper are examples that illustrate how to access routines within the NAG Library for Python using data stored in kdb+. Read it here