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Showing posts from June, 2016

Analysis of performance optimisation service requests: what kind of codes are we helping as part of POP CoE?

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by Sally Bridgwater, NAG HPC Application Analyst

NAG is a partner in the Performance Optimisation and Productivity Centre of Excellence (POP). POP was created with the aim of boosting the productivity of EU research and industry by providing free of charge services to advise on improving the performance of high performance computing (HPC) parallel software.

The POP team consists of six partner organisations from Germany, France, Spain and the UK. Over 30 codes have applied for the POP service so far since its kick-off in October 2015. I decided to have a look into the details of what types of codes POP is working with and see if any interesting themes emerge. Since this is quite early in the project it will be useful to revisit and see how it evolves over time.

First I decided to look at what languages all of the codes were written in. From my experience in Physics, I generally assumed that Fortran was the most prevalent language in academic/scientific applications.


This seems to be the…

Improved Accessibility for NAG’s Mathematical and Statistical Routines for Python Data Scientists

By John Muddle, NAG Technical Sales Support Engineer

NAG and Continuum have partnered together to provide conda packages for the NAG Library for Python (nag4py), the Python bindings for the NAG C Library. Users wishing to use the NAG Library with Anaconda can now install the bindings with a simple command (conda install -c nag nag4py) or the Anaconda Navigator GUI.

For those of us who use Anaconda, the Open Data Science platform, for package management and virtual environments, this enhancement provides immediate access to the 1,500+ numerical algorithms in the NAG Library. It also means that you can automatically download any future NAG Library updates as they are published on the NAG channel in Anaconda Cloud.

To illustrate how to use the NAG Library for Python, I have created an IPython Notebook that demonstrates the use of NAG’s implementation of the PELT algorithm to identify the changepoints of a stock whose price history has been stored in a MongoDB database. Using the example of…