AccelerEyes + Mathworks for a better parallel computing toolbox

It seems that at last, the two main communities that design commercial GPU accelerated toolboxes for Matlab, Mathworks & AccelerEyes, have agreed to share knowledge and expertise. Quoting from the AccelerEyes blog We are pleased to announce today that MathWorks and AccelerEyes have started working together to provide the best overall solution for GPU computing in MATLAB® through the Parallel Computing Toolbox™ and MATLAB Distributed Computing Server™ from MathWorks. … Continue reading AccelerEyes + Mathworks for a better parallel computing toolbox

KostisDecember 13, 2012December 14, 2012

Sciency stuff

More random stuff …

So, this post is mainly for the people who took the Math697SC course, Computational methods for stochastic systems this semester from the maths department at UMass, but then I thought the info might be of interest to more people than that, so instead of writing an email, I wrote everything directly here. We talked about a couple of things during my presentation and I thought … Continue reading More random stuff …

KostisDecember 9, 2012December 9, 2012

My notes, Sciency stuff

How to use printf in device/global code in CUDA

If your card has 2.0 or higher compute capability, you can use printf inside your device/global code to print variables for debugging purposes. You may get an error though. If that happens, add -arch=sm_20 to the end of your compile code. For example nvcc a_cool_code.cu -arch=sm_20 This way your code will take into account the compute capability you have in your card. For compatibility reasons, … Continue reading How to use printf in device/global code in CUDA

KostisApril 24, 2012February 25, 2013