An Acceleration Toolkit of MATLAB Based on Hybrid CPU/GPU Clusters

2013 ◽  
Author(s):  
Tyng-Yeu Liang ◽  
Jyun-Kai Wu ◽  
Yu-Chih Chen
Keyword(s):  
Gpu Clusters

scholarly journals Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

2021 ◽  
Vol 22 (10) ◽  
pp. 5212
Author(s):  
Andrzej Bak
Keyword(s):  
Molecular Conformation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Diverse Range ◽  
Current State ◽  
Gpu Clusters ◽  
Pharmacophore Hypothesis ◽  
Rising Power ◽  
Graphics Processing ◽  
Ligand Conformation

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

CUDA-DTM: Distributed Transactional Memory for GPU Clusters

2019 ◽  
pp. 183-199 ◽  
Author(s):  
Samuel Irving ◽  
Sui Chen ◽  
Lu Peng ◽  
Costas Busch ◽  
Maurice Herlihy ◽  
...  
Keyword(s):  
Transactional Memory ◽  
Gpu Clusters

scholarly journals OpSeF: Open source Python framework for collaborative instance segmentation of bioimages

2020 ◽  
Author(s):  
Tobias M. Rasse ◽  
Réka Hollandi ◽  
Péter Horváth
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Complex Analysis ◽  
Ease Of Use ◽  
Problem Definition ◽  
Training Data ◽  
Post Processing ◽  
Gpu Clusters ◽  
User Tasks ◽  
Instance Segmentation

AbstractVarious pre-trained deep learning models for the segmentation of bioimages have been made available as ‘developer-to-end-user’ solutions. They usually require neither knowledge of machine learning nor coding skills, are optimized for ease of use, and deployability on laptops. However, testing these tools individually is tedious and success is uncertain.Here, we present the ‘Op’en ‘Se’gmentation ‘F’ramework (OpSeF), a Python framework for deep learning-based instance segmentation. OpSeF aims at facilitating the collaboration of biomedical users with experienced image analysts. It builds on the analysts’ knowledge in Python, machine learning, and workflow design to solve complex analysis tasks at any scale in a reproducible, well-documented way. OpSeF defines standard inputs and outputs, thereby facilitating modular workflow design and interoperability with other software. Users play an important role in problem definition, quality control, and manual refinement of results. All analyst tasks are optimized for deployment on Linux workstations or GPU clusters, all user tasks may be performed on any laptop in ImageJ.OpSeF semi-automates preprocessing, convolutional neural network (CNN)-based segmentation in 2D or 3D, and post-processing. It facilitates benchmarking of multiple models in parallel. OpSeF streamlines the optimization of parameters for pre- and post-processing such, that an available model may frequently be used without retraining. Even if sufficiently good results are not achievable with this approach, intermediate results can inform the analysts in the selection of the most promising CNN-architecture in which the biomedical user might invest the effort of manually labeling training data.We provide Jupyter notebooks that document sample workflows based on various image collections. Analysts may find these notebooks useful to illustrate common segmentation challenges, as they prepare the advanced user for gradually taking over some of their tasks and completing their projects independently. The notebooks may also be used to explore the analysis options available within OpSeF in an interactive way and to document and share final workflows.Currently, three mechanistically distinct CNN-based segmentation methods, the U-Net implementation used in Cellprofiler 3.0, StarDist, and Cellpose have been integrated within OpSeF. The addition of new networks requires little, the addition of new models requires no coding skills. Thus, OpSeF might soon become both an interactive model repository, in which pre-trained models might be shared, evaluated, and reused with ease.

scholarly journals A MONTE CARLO ANALYSIS METHOD OF CRUSTAL DEFORMATION COMPUTATION ON GPU CLUSTERS

2017 ◽  
Vol 73 (4) ◽  
pp. I_310-I_320
Author(s):  
Takuma YAMAGUCHI ◽  
Ryoichiro AGATA ◽  
Tsuyoshi ICHIMURA ◽  
Muneo HORI ◽  
Lalith WIJERATHNE
Keyword(s):  
Monte Carlo ◽  
Crustal Deformation ◽  
Monte Carlo Analysis ◽  
Analysis Method ◽  
Gpu Clusters
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