Organic Chemistry Animations loadScript j2s/core/package.js loadScript j2s/core/corejmol.z.js loadScript j2s/core/corescript.z.js JSmol exec jmolApplet0 start applet null Jmol JavaScript applet jmolApplet0__264761300712015__ initializing Jmol getValue debug null Jmol getValue logLevel null Jmol getValue allowjavascript true AppletRegistry.checkIn(jmolApplet0__264761300712015__) vwrOptions: setting document base to " (C) 2015 Jmol Development Jmol Version: 14.20.8 2017-10-07 09:29 java.vendor: Java2Script (HTML5) java.version: 2017-07-06 02:22:33 (JSmol/j2s) os.name: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:17.0) Gecko/20100101 Firefox/17.0 Access: ALL memory: 0.0/0.0 processors available: 1 useCommandThread: false appletId:jmolApplet0 (signed) Jmol getValue emulate null defaults = "Jmol" Jmol getValue boxbgcolor null Jmol getValue bgcolor white backgroundColor = "white" Jmol getValue ANIMFRAMECallback null Jmol getValue APPLETREADYCallback Jmol. APPLETREADYCallback = "Jmol. language=en_US
distributed.net Climateprediction.net | The world's largest climate forecasting Binding Database BindingDB is a public, web-accessible database of measured binding affinities, focusing chiefly on the interactions of protein considered to be drug-targets with small, drug-like molecules. BindingDB contains 1,009,290 binding data, for 6,589 protein targets and 427,325 small molecules. There are 2046 protein-ligand crystal structures with BindingDB affinity measurements for proteins with 100% sequence identity, and 5815 crystal structures allowing proteins to 85% sequence identity. BindingDB News November 2013. September 2013. September 2013. June 2013. Try the new AffyNet tool for visualization networks of Ligand-Target binding affinities. March 2013. March 2013.
CUDA Zone The first GPUs were designed as graphics accelerators, becoming more programmable over the 90s, culminating in NVIDIA's first GPU in 1999. Researchers and scientists rapidly began to apply the excellent floating point performance of this GPU for general purpose computing. In 2003, a team of researchers led by Ian Buck unveiled Brook, the first widely adopted programming model to extend C with data-parallel constructs. Ian Buck later joined NVIDIA and led the launch of CUDA in 2006, the world's first solution for general-computing on GPUs. Since its inception, the CUDA ecosystem has grown rapidly to include software development tools, services and partner-based solutions.
AQUA@home ChemSpider - Database of Chemical Structures and Property Predictions Traitement parallèle CUDA | Wiki Qu’est-ce que CUDA ? CUDA est une architecture de traitement parallèle développée par NVIDIA permettant de décupler les performances de calcul du système en exploitant la puissance des processeurs graphiques (GPU). Alors que des millions de GPU compatibles avec CUDA ont été vendus, des milliers de développeurs de logiciels, de scientifiques et de chercheurs utilisent CUDA dans une grande gamme de domaines, incluant notamment le traitement des images et des vidéos, la chimie et la biologie par modélisation numérique, la mécanique des fluides numérique, la reconstruction tomodensitométrique, l’analyse sismique, le ray tracing et bien plus encore. Traitement parallèle avec CUDA Le calcul informatique a évolué en passant du traitement central exclusif des CPU vers les capacités de co-traitement offertes par l’association du CPU et du GPU. Du côté de la recherche scientifique, CUDA a été reçu avec enthousiasme.