NVIDIA GPU Grant Scheme

I really need to get around to writing on here more often…

Anyway, the NVIDIA GPU Grant Scheme has re-opened after a little break over Christmas and is now giving away Titan V (Volta) GPUs to academics. I was lucky enough to bag one of these earlier this week, so I’m eagerly awaiting the delivery! All that’s required is a 2000 word explanation of why it would be helpful to your research, so it’s well worth your time.

 

 

The Titan V is a significant upgrade to their previous offering in that the Titan V has 640 “Tensor cores” in addition to the usual CUDA cores. The tensor cores are dedicated to performing matrix multiplies using half precision arithmetic (though the input and output matrices can be single precision). There is a more detailed explanation of their use here. Using half precision on dedicated hardware components means a dramatic increase in speed for these operations, which are at the heart of neural network training.

The downside to using half precision arithmetic is the potential loss of accuracy in the result, depending upon the accumulation of rounding errors. Whilst this isn’t a huge problem for small matrix multiplications, using half precision for other matrix problems such as inversion will – essentially – multiply this error by the condition number of the matrix. At this point we can quickly run into problems! Nick Higham recently touched upon these topics in this blog post.

Summary of the MRC / NHS Digital Roadshow, York, Jan 2018

It’s now been six months since I switched from maths to healthcare (Wow, time goes fast!) and I’m starting to understand the bigger picture of funding bodies, legislative restrictions, and government-affiliated organisations in the area.

Two of these bodies are the MRC (Medical Research Council) and NHS Digital (owners of most secondary care datasets), so I was keen to attend their “roadshow” event in York. The event outlined some of the major upcoming changes to the healthcare domain over the next few years, mainly arising from the EUs data protection law, GDPR. I’ve summarised some of the key points below whilst the slides from the event can be found here.

I managed to stock up on Betty’s tea whilst in York

A Crash Course in Health Analytics

As some of you might know, I recently made a (fairly large) career change. I’m now a Research Fellow in Health Data Analytics at The University of Leeds, UK. Since I’m coming from a maths/HPC background and have almost no prior knowledge of healthcare I’ve had a bit of catching up to do! Overall I’m aiming to apply machine learning and large-scale data analysis to large datasets arising from health and socio-economic domains, where my skills in linear algebra and HPC will undoubtedly become useful.

In this post I aim to summarise what I’ve learned in my first few weeks on the job. This is partly to archive things for myself but should also be interesting reading for anyone who wants to get involved with health data. I’ll touch upon

• the legal issues arising from using private data,
• interesting talks and papers that I’ve found, and,
• some recurring opinions on the future challenges in the field.