Topic
Graphics processing unit
About: Graphics processing unit is a research topic. Over the lifetime, 6068 publications have been published within this topic receiving 93478 citations. The topic is also known as: GPU & visual processing unit.
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TL;DR: The background, hardware, and programming model for GPU computing is described, the state of the art in tools and techniques are summarized, and four GPU computing successes in game physics and computational biophysics that deliver order-of-magnitude performance gains over optimized CPU applications are presented.
Abstract: The graphics processing unit (GPU) has become an integral part of today's mainstream computing systems. Over the past six years, there has been a marked increase in the performance and capabilities of GPUs. The modern GPU is not only a powerful graphics engine but also a highly parallel programmable processor featuring peak arithmetic and memory bandwidth that substantially outpaces its CPU counterpart. The GPU's rapid increase in both programmability and capability has spawned a research community that has successfully mapped a broad range of computationally demanding, complex problems to the GPU. This effort in general-purpose computing on the GPU, also known as GPU computing, has positioned the GPU as a compelling alternative to traditional microprocessors in high-performance computer systems of the future. We describe the background, hardware, and programming model for GPU computing, summarize the state of the art in tools and techniques, and present four GPU computing successes in game physics and computational biophysics that deliver order-of-magnitude performance gains over optimized CPU applications.
1,570 citations
TL;DR: ColabFold as discussed by the authors combines the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold for protein folding and achieves 40-60fold faster search and optimized model utilization.
Abstract: ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com .
1,553 citations
TL;DR: This paper develops a general purpose molecular dynamics code that runs entirely on a single GPU and shows that the GPU implementation provides a performance equivalent to that of fast 30 processor core distributed memory cluster.
1,514 citations
TL;DR: To enable flexible, programmable graphics and high-performance computing, NVIDIA has developed the Tesla scalable unified graphics and parallel computing architecture, which is massively multithreaded and programmable in C or via graphics APIs.
Abstract: To enable flexible, programmable graphics and high-performance computing, NVIDIA has developed the Tesla scalable unified graphics and parallel computing architecture. Its scalable parallel array of processors is massively multithreaded and programmable in C or via graphics APIs.
1,492 citations
TL;DR: This paper presents a parallel-friendly formulation of the free-form deformation algorithm suitable for graphics processing unit execution and performs registration of T1-weighted MR images in less than 1 min and shows the same level of accuracy as a classical serial implementation when performing segmentation propagation.
998 citations