Machine Learning

Machine learning for oil & gas exploration

Author: Nick Brown
Posted: 27 Jul 2018 | 15:25

We are working on a machine-learning project with Rock Solid Images (RSI), a geoscience consulting firm that provides borehole characterisation with the goal of reducing exploration drilling risk for oil and gas companies.

RSI is one of the main players in the interpretation of seismic data with well log data and it has built its business on using advanced rock physics methods combined with sophisticated geologic models to deliver highly reliable predictions of where oil and gas might be found.

Planning for high performance in MPI

Author: Daniel Holmes
Posted: 25 Jan 2018 | 14:36

Many HPC applications contain some sort of iterative algorithm and so do the same steps repeatedly, over and over again, with the data gradually converging to a stable solution. There are examples of this archetype in structural engineering, fluid flow, and all manner of other physical simulation codes.

The experience of a lifetime :  ISC’17 Student Cluster Competition

Author: Guest blogger
Posted: 13 Jul 2017 | 23:49

A team of students from EPCC's MSc programmes took part in this year's Student Cluster Competition at the International Supercomputing Conference (ISC) in Germany. The competition requires teams to design and configure a cluster on which they optimise and run benchmarks and applications within a power budget of 3000 watts.

Here Team EPCC and its coach Emmanouil Farsarakis tell us about their hard work and its rewards.

EPCC PhD opportunity in micro-core architectures

Author: Nick Brown
Posted: 4 Apr 2017 | 14:51

At EPCC we are currently advertising a number of funded PhD opportunities (see our PhDs in HPC webpage). I am proposing a project entitled Improving the programmability of micro-core architectures, which builds on some of the work I first discussed in a previous blog post on ePython.

Demystifying data input to TensorFlow for deep learning

Author: Alan Gray
Posted: 29 Nov 2016 | 10:07

Shape SorterView 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.

Self racing cars

Author: Adrian Jackson
Posted: 16 Sep 2016 | 11:34

Roborace DevBot on the trackAutonomous racing

Recently EPCC's Alan Gray and I attended a workshop at Donington Park held by Roborace.  For those who've not heard of Roborace, it's a project to build and race autonomous cars, along the lines of Formula 1 but without any drivers or human control of the cars.  Actually, it's more like Formula E but without drivers, as the plan is for the cars to be electric.

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