GPU computing

GPU-accelerated computing uses a graphics processing unit (GPU) to accelerate computations in computer programs. Several applications in the SBGrid programs tree are GPU-accelerated. Some examples are RELION, GCTF, MotionCor2, Gautomatch, IMOD, AmberTools, etc. Here's what you need to take advantage of a GPU:

Requirements for GPU (CUDA) applications

NVIDIA Hardware

To use GPU-accelerated applications, you will need an NVIDIA GPU. We don't currently support other co-processors (Xeon Phi, AMD). If you think we should, let us know at .

Not all GPUs are created equal - some applications are restricted to GPUs with a certain 'Compute Capability (i.e. of 3.5 or greater)'. You can check your card's rating and find more information on GPU computing here :

Hardware Drivers

You linux workstation must have the proper drivers for your GPU. You can find drivers for your card here :

We currently support applications that rely on versions of CUDA 7.5 to 12.2 and these libraries are included with the SBGrid distribution. CUDA libraries do NOT need to be installed locally - they will not be used by SBGrid software. However, it is important to have a recent driver version if possible.

For information about GPU hardware and CUDA / driver version compatibility, see this table at

For more information on GPUs and RELION, check Erik Lindahl's blog at the Department of Biochemistry & Biophysics, Stockholm University,

GPU questions? Want to use GPU support in an application and it's not working? Please email