Blogs

Compartmental modeling on GPUs?

What is the current overlap between computational neuroscience and GPU computing? Well, not that much. Definitely smaller than in fields such as bioinformatics. I suspect that since many simulation environments for the neuroscientist are already quite mature and complex it is hard to modify them to include CUDA functionality. The experience in our lab is that existing code running on the CPU cannot be simply transformed to the GPU. Considerable re-writing of the code is required and in many cases it is easier to start from scratch. Adding CUDA fun

Presenting Aarhus University branch of GPU Computing Denmark

 

In our research group based in Aarhus, Denmark, we're excited about the possibilities that gpucomputing.net and it's local communities bring for sharing experiences on GPU computing. We kick-off by giving a small presentation of what we do in our group and who is involved.

Computer Graphics and Scientific Computing at Aarhus University

 

Research areas:

Theano: A package for efficient computation in Python

Theano is a Python library that allows you to define, optimize, and evaluate symbolic mathematical expressions involving multi-dimensional arrays efficiently. Theano features:

NVIDIA's GPU Computing Webinars starting again

I'm re-starting the Webinar series after a couple of months. They run about 1.5 hours - and a great way to get going on CUDA C and OpenCL

You can see the full schedule on:

http://developer.nvidia.com/object/gpu_computing_online.html

Anyone can attend - so send this link to everyone you know who should be using GPU computing but have been too lazy to start !

Job Posting on GPU Computing

Hey have you seen there is a job posting from the University of Basel. If anyone else has Job postings this is a great place to put them up.

http://www.gpucomputing.net/?q=node/151

 

Welcome to the Data Structures and Algorithms community!

Greetings! I'd like to introduce myself to this community and look forward to posting and conversing with you all about algorithms and data structures on the GPU. This is an area that's particularly interesting to me as a researcher; algorithms and data structures have been the core of much of our group's research.

It takes problem-domain and algorithm knowledge to be a superhero

Recently tried my hand at the CUDA Superhero Challenge 2.  Tried a quick-and-dirty brute-force attempt just to see if it  was even remotely possible in the time constraints (it wasn't), and then did a little Monte Carlo exploration, which did much better.  Still, the solutions I was getting in the time limit were scoring way below the standing leaders, and I ran out of ideas.  

GPU Revolution

I have been working with graphics processors my entire professional life. Its been an amazing experience.

We are now experiencing the fastest growth of computing capabilities that the industry has every seen.

The moment GPU computing capabilities were unleashed through the launch of NVIDIA's CUDA Architecture - it was an inflexion point that that can not be described as anything other than revolutionary.

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