BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

TL;DR

BioEM introduces GPU-accelerated software for Bayesian inference in cryo-electron microscopy, enabling efficient analysis of large datasets and handling of dynamic or heterogeneous molecular structures.

Contribution

The paper presents a highly parallelized GPU-accelerated software implementation of BioEM, significantly improving computational efficiency for Bayesian EM image analysis.

Findings

Scales nearly ideally on CPU and GPU architectures

Enables Bayesian analysis of tens of thousands of images

Flexible framework applicable beyond cryo-EM

Abstract

In cryo-electron microscopy (EM), molecular structures are determined from large numbers of projection images of individual particles. To harness the full power of this single-molecule information, we use the Bayesian inference of EM (BioEM) formalism. By ranking structural models using posterior probabilities calculated for individual images, BioEM in principle addresses the challenge of working with highly dynamic or heterogeneous systems not easily handled in traditional EM reconstruction. However, the calculation of these posteriors for large numbers of particles and models is computationally demanding. Here we present highly parallelized, GPU-accelerated computer software that performs this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI parallelization combined with both CPU and GPU computing. The resulting BioEM software scales nearly ideally both on pure CPU and on CPU+GPU architectures, thus enabling Bayesian analysis of tens of thousands of images in a reasonable time. The general mathematical framework and robust algorithms are not limited to cryo-electron microscopy but can be generalized for electron tomography and other imaging experiments.