{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bioem-gpu-accelerated-computing-of-bayesian","title":"BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images","arxiv_id":"1609.06634","date":"2016-09-21","proceeding":null,"authors":["Pilar Cossio","David Rohr","Fabio Baruffa","Markus Rampp","Volker Lindenstruth","Gerhard Hummer"],"abstract":"In cryo-electron microscopy (EM), molecular structures are determined from\nlarge numbers of projection images of individual particles. To harness the full\npower of this single-molecule information, we use the Bayesian inference of EM\n(BioEM) formalism. By ranking structural models using posterior probabilities\ncalculated for individual images, BioEM in principle addresses the challenge of\nworking with highly dynamic or heterogeneous systems not easily handled in\ntraditional EM reconstruction. However, the calculation of these posteriors for\nlarge numbers of particles and models is computationally demanding. Here we\npresent highly parallelized, GPU-accelerated computer software that performs\nthis task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI\nparallelization combined with both CPU and GPU computing. The resulting BioEM\nsoftware scales nearly ideally both on pure CPU and on CPU+GPU architectures,\nthus enabling Bayesian analysis of tens of thousands of images in a reasonable\ntime. The general mathematical framework and robust algorithms are not limited\nto cryo-electron microscopy but can be generalized for electron tomography and\nother imaging experiments.","url_abs":"http://arxiv.org/abs/1609.06634v1","url_pdf":"http://arxiv.org/pdf/1609.06634v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bioem-gpu-accelerated-computing-of-bayesian","repo_url":"https://github.com/bio-phys/BioEM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":null,"task_name":"CPU"},{"task_slug":"electron-tomography","task_name":"Electron Tomography"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}