Parallel, distributed and GPU computing technologies in single-particle electron microscopy

Abstract

Most known methods for the determination of the structure of macromolecular complexes are limited or at least restricted at some point by their computational demands. Recent developments in information technology such as multicore, parallel and GPU processing can be used to overcome these limitations. In particular, graphics processing units (GPUs), which were originally developed for rendering real-time effects in computer games, are now ubiquitous and provide unprecedented computational power for scientific applications. Each parallel-processing paradigm alone can improve overall performance; the increased computational performance obtained by combining all paradigms, unleashing the full power of today's technology, makes certain applications feasible that were previously virtually impossible. In this article, state-of-the-art paradigms are introduced, the tools and infrastructure needed to apply these paradigms are presented and a state-of-the-art infrastructure and solution strategy for moving scientific applications to the next generation of computer hardware is outlined.

Dynamo: A flexible, user-friendly development tool for subtomogram averaging of cryo-EM data in high-performance computing environments

“…In Electron Microscopy, this interest has already translated into several packages, notably for Single Particle techniques (Li et al, 2010;Schmeisser et al, 2009;Tagare et al, 2010;Zhang and Zhou, 2010) but also in tomography for procedures as iterative reconstruction (Castaño-Díez et al, 2007;Palenstijn et al, 2011;Xu et al, 2010;Zheng et al, 2011), markerless alignment of tilt series (Castano-Diez et al, 2010) and other generic three-dimensional image processing tasks (Castano-Diez et al, 2008;Gipson et al, 2011). In spite of this progression, the currently available offer in GPU-enabled software packages is still modest in comparison to the CPU-based counterparts.…”

A matrix approach to tomographic reconstruction and its implementation on GPUs

“…High performance computing has traditionally been used to cope with the computational requirements (Fernández, 2008). Graphics Processing Units (GPUs) are receiving great interest in 3D electron microscopy because many image processing procedures are well suited for the SIMD (single instruction, multiple data) parallelism massively exploited by GPU (Castano-Diez et al, 2007, 2008Schmeisser et al, 2009). Programming interfaces, such as CUDA, facilitate the development of applications targeted at GPUs (CastanoDiez et al, 2008;Nickolls et al, 2008).…”

High-performance iterative electron tomography reconstruction with long-object compensation using graphics processing units (GPUs)

“…The high potential of GPUs for accelerating Computed Tomography (CT) has been recognized for quite some time in the field of X-ray CT (Cabral et al, 1994; Chidlow and Möller, 2003; Kole and Beekman, 2006; Mueller and Xu, 2006; Schiwietz et al, 2006; Wang et al, 2005; Xu and Mueller, 2005; Xu and Mueller, 2007; Xu et al, 2010, Xue et al, 2006), and more recently also for ET (Castano-Diez et al, 2007; Castano-Diez et al, 2008; Lawrence et al, 2009; Schoenmakers et al, 2005; Schmeisser et al, 2009). The majority of GPU algorithms developed for X-ray CT have focused on 3D reconstruction from data acquired in perspective (cone- and fan-beam) viewing geometries, using flat-panel X-ray detectors in conjunction with X-ray point sources.…”