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

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.在冷冻电子显微镜（cryo-electron microscopy, EM）中，分子结构的解析基于大量单个颗粒的投影图像。为充分挖掘这类单分子信息的全部潜力，我们采用了冷冻电镜贝叶斯推断（Bayesian inference of EM, BioEM）形式体系。通过针对单张图像计算后验概率并据此对结构模型进行排序，BioEM从原理上解决了传统冷冻电镜重构难以处理的高度动态或异质性系统的分析难题。然而，针对海量颗粒与结构模型计算此类后验概率的过程计算开销极大。本文提出一款高度并行化、图形处理器（Graphics Processing Unit, GPU）加速的计算机软件，可高效完成此类计算任务。我们的灵活架构结合中央处理器（Central Processing Unit, CPU）与图形处理器计算，并采用CUDA、OpenMP及MPI并行化方案。所开发的BioEM软件在纯CPU以及CPU+GPU混合架构下均可实现近乎理想的可扩展性，因此可在合理时长内完成数万张图像的贝叶斯分析。该通用数学框架与鲁棒算法并不局限于冷冻电子显微镜领域，还可推广应用于电子断层扫描及其他成像实验。