gEMfitter: A Highly Parallel FFT-Based 3D Density Fitting Tool With GPU Texture Memory Acceleration

Thai V. Hoang, Xavier Cavin, David W. Ritchie
Inria Nancy – Grand Est, 54600 Villers-les-Nancy, France
Journal of Structural Biology, 2013


   title={gEMfitter: A Highly Parallel FFT-Based 3D Density Fitting Tool With GPU Texture Memory Acceleration: gEMfitter: A GPU-Accelerated 3D Density Fitting Tool},

   author={Hoang, Thai V and Cavin, Xavier and Ritchie, David W},

   journal={Journal of Structural Biology},




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Fitting high resolution protein structures into low resolution cryo-electron microscopy (cryo-EM) density maps is an important technique for modeling the atomic structures of very large macromolecular assemblies. This article presents "gEMfitter", a highly parallel fast Fourier transform (FFT) EM density fitting program which can exploit the special hardware properties of modern graphics processor units (GPUs) to accelerate both the translational and rotational parts of the correlation search. In particular, by using the GPU’s special texture memory hardware to rotate 3D voxel grids, the cost of rotating large 3D density maps is almost completely eliminated. Compared to performing 3D correlations on one core of a contemporary central processor unit (CPU), running gEMfitter on a modern GPU gives up to 26-fold speed-up. Furthermore, using our parallel processing framework, this speed-up increases linearly with the number of CPUs or GPUs used. Thus, it is now possible to use routinely more robust but more expensive 3D correlation techniques. When tested on low resolution experimental cryo-EM data for the GroEL-GroES complex, we demonstrate the satisfactory fitting results that may be achieved by using a locally normalised cross-correlation with a Laplacian pre-filter, while still being up to three orders of magnitude faster than the well-known COLORES program.
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