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For those of you not acquainted with OpenFOAM, it's a large open source CFD package used by a wide variety of scientists and companies to investigate a whole range of scientific and engineering problems. 

We support it on ARCHER and have a number of different versions available and in use on the machine. As part of our IPCC work we are interested in looking at the performance of OpenFOAM on the latest Xeon Phi processor, Knights Landing (KNL).

In a previous blog post I talked about ePython, the very lightweight version of Python that I have developed for the Epiphany co-processor. This co-processor is combined with a dual core ARM CPU on the Parallella single board computer, and this week an updated OS image was released for the machine which now includes ePython pre-installed.

View this post on GitHub

TensorFlow is an incredibly powerful new framework for deep learning. The “MNIST For ML Beginners” and “Deep MNIST for Experts” TensorFlow tutorials give an excellent introduction to the framework. This article acts as a follow-on tutorial which addresses the following issues:

1. The above tutorials use the MNIST dataset of hand written numbers, which pre-exists in TensorFlow TFRecord format and is loaded automatically. This can be a bit mysterious if you have no experience of data format manipulation in TensorFlow.
2. Since the MNIST dataset is fixed, there is little scope for experimentation through adjusting the images and network to get a feel for how to deal with particular aspects of real data.

Connor Walsh is a school pupil who joined us on a work experience placement last week. Here's his report.

I have been "working" at the EPCC offices at Edinburgh University under Eilidh Troup on a project using Python and Pygame to simulate how much an object would sink in water when you change the mass and the volume of the object.