Sustainable Computing

Sustainable Computing

Date: Wednesday 24 March 2021, 1.00PM - 3.00PM
Location: Online
Online - joining instructions will be sent to those registered
Section Group Meeting
Book now


Share this event

Advances in computing hardware and software along with the ever-increasing availability of huge amounts of data are enabling the deployment of machine learning and statistical model algorithms to solve a range of complex problems and to underpin more and more real-life applications. As our societies become more reliant on technology-enabled and computer devices in general, how can we ensure that this does not increase our power consumption? In this workshop, we will discuss the energy costs associated with running more and more computer-intensive, data-greedy models, possible model optimisation approaches to make best-use on new computer infrastructures, and the development of low-power computer chips designed specifically with AI applications in mind.
 
 
Programme:

1pm - 1.05pm Welcome and introduction  - Dr Camille Szmaragd Harrison

1.05pm - 1.35 pm Dr Chris Maynard - “How to train your supercomputer

1.40pm - 2.10pm Partha Maji - “Accelerating Your ML Algorithms: A Hardware-Centric View

2.15pm- 2.45pm Jacob Tomlinson - “GPU accelerated Python data science

2.45pm - 3pm Q&A - and discussion
 
Dr Camille Szmaragd Harrison (Office for National Statistics)
Statistical Methodologist at the UK Office for National Statistics

Dr Chris Maynard (Met Office, University of Reading)
Met Office Acting HPC optimisation manager and Associate Professor of Computer Science, University of Reading

Dr Partha Maji (Arm Machine Learning Research Lab)
Principal Research Scientist at Arm’s Machine Learning Research Lab

Jacob Tomlinson (NVIDIA)
Senior Software Engineer at NVIDIA

 
 
Dr Camille Szmaragd Harrison for the RSS Computational Statistics & Machine Learning Section
 
 
Book now