TheDynamopackage for tomography and subtomogram averaging: components forMATLAB, GPU computing and EC2 Amazon Web Services

Dynamois a package for the processing of tomographic data. As a tool for subtomogram averaging, it includes different alignment and classification strategies. Furthermore, its data-management module allows experiments to be organized in groups of tomograms, while offering specialized three-dimensional tomographic browsers that facilitate visualization, location of regions of interest, modelling and particle extraction in complex geometries. Here, a technical description of the package is presented, focusing on its diverse strategies for optimizing computing performance.Dynamois built upon mbtools (middle layer toolbox), a general-purposeMATLABlibrary for object-oriented scientific programming specifically developed to underpinDynamobut usable as an independent tool. Its structure intertwines a flexibleMATLABcodebase with precompiled C++ functions that carry the burden of numerically intensive operations. The package can be delivered as a precompiled standalone ready for execution without aMATLABlicense. Multicore parallelization on a single node is directly inherited from the high-level parallelization engine provided forMATLAB, automatically imparting a balanced workload among the threads in computationally intense tasks such as alignment and classification, but also in logistic-oriented tasks such as tomogram binning and particle extraction.Dynamosupports the use of graphical processing units (GPUs), yielding considerable speedup factors both for nativeDynamoprocedures (such as the numerically intensive subtomogram alignment) and procedures defined by the user through itsMATLAB-based GPU library for three-dimensional operations. Cloud-based virtual computing environments supplied with a pre-installed version ofDynamocan be publicly accessed through the Amazon Elastic Compute Cloud (EC2), enabling users to rent GPU computing time on a pay-as-you-go basis, thus avoiding upfront investments in hardware and longterm software maintenance.

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Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Information ◽

10.3390/info11040193 ◽

2020 ◽

Vol 11 (4) ◽

pp. 193 ◽

Cited By ~ 7

Author(s):

Sebastian Raschka ◽

Joshua Patterson ◽

Corey Nolet

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Science ◽

Gpu Computing ◽

Graphics Processing Unit ◽

General Purpose ◽

Processing Unit ◽

The Core ◽

Critical Components ◽

High Level

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

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Computing Gamma Calculus on Computer Cluster

Computer Engineering ◽

10.4018/978-1-61350-456-7.ch813 ◽

2012 ◽

pp. 2016-2026

Author(s):

Hong Lin ◽

Jeremy Kemp ◽

Padraic Gilbert

Keyword(s):

Gpu Computing ◽

Programming Model ◽

Large Data ◽

General Purpose ◽

Tuple Space ◽

Data Set ◽

Cuda Architecture ◽

High Level ◽

General Purpose Gpu ◽

Grid Cluster

Gamma Calculus is an inherently parallel, high-level programming model, which allows simple programming molecules to interact, creating a complex system with minimum of coding. Gamma calculus modeled programs were written on top of IBM’s TSpaces middleware, which is Java-based and uses a “Tuple Space” based model for communication, similar to that in Gamma. A parser was written in C++ to translate the Gamma syntax. This was implemented on UHD’s grid cluster (grid.uhd.edu), and in an effort to increase performance and scalability, existing Gamma programs are being transferred to Nvidia’s CUDA architecture. General Purpose GPU computing is well suited to run Gamma programs, as GPU’s excel at running the same operation on a large data set, potentially offering a large speedup.

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CPU/GPU COMPUTING FOR AN IMPLICIT MULTI-BLOCK COMPRESSIBLE NAVIER-STOKES SOLVER ON HETEROGENEOUS PLATFORM

International Journal of Modern Physics Conference Series ◽

10.1142/s2010194516601630 ◽

2016 ◽

Vol 42 ◽

pp. 1660163

Author(s):

LIANG DENG ◽

HANLI BAI ◽

FANG WANG ◽

QINGXIN XU

Keyword(s):

Stokes Equations ◽

Gpu Computing ◽

Three Dimensional ◽

Navier Stokes ◽

Double Precision ◽

Single Node ◽

Navier Stokes Equations ◽

Heterogeneous Platform ◽

Redundant Data ◽

Fine Grain

CPU/GPU computing allows scientists to tremendously accelerate their numerical codes. In this paper, we port and optimize a double precision alternating direction implicit (ADI) solver for three-dimensional compressible Navier-Stokes equations from our in-house Computational Fluid Dynamics (CFD) software on heterogeneous platform. First, we implement a full GPU version of the ADI solver to remove a lot of redundant data transfers between CPU and GPU, and then design two fine-grain schemes, namely “one-thread-one-point” and “one-thread-one-line”, to maximize the performance. Second, we present a dual-level parallelization scheme using the CPU/GPU collaborative model to exploit the computational resources of both multi-core CPUs and many-core GPUs within the heterogeneous platform. Finally, considering the fact that memory on a single node becomes inadequate when the simulation size grows, we present a tri-level hybrid programming pattern MPI-OpenMP-CUDA that merges fine-grain parallelism using OpenMP and CUDA threads with coarse-grain parallelism using MPI for inter-node communication. We also propose a strategy to overlap the computation with communication using the advanced features of CUDA and MPI programming. We obtain speedups of 6.0 for the ADI solver on one Tesla M2050 GPU in contrast to two Xeon X5670 CPUs. Scalability tests show that our implementation can offer significant performance improvement on heterogeneous platform.

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Les apports respectifs des langages symboliques et de la cao en programmation des robots

Robotica ◽

10.1017/s0263574700004355 ◽

1988 ◽

Vol 6 (3) ◽

pp. 243-253 ◽

Cited By ~ 2

Author(s):

C. Laugier

Keyword(s):

Programming Language ◽

Robot Control ◽

Three Dimensional ◽

Physical World ◽

General Purpose ◽

Robot Programming ◽

Programming System ◽

Control Programs ◽

Functional Capabilities ◽

High Level

SUMMARYA high-level robot programming language constitutes a general purpose interface for accessing the basic functional capabilities of a robot. On the other hand, CAD facilities give the possibility of using a subset of these capabilities in an easier fashion. In this paper, we show how a robot programming language and CAD facilities can be combined to obtain a robot programming system satisfying the need for generality, and allowing an easy connection with the basic robot programming functions. Such a connection is based on a “complete” simulator providing facilities for executing robot control programs on a graphic display, for describing manipulation tasks using interactive graphic tools, for simulating the physical world and its perception through sensors, and for displaying three-dimensional scenes as shaded pictures.

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Computing Gamma Calculus on Computer Cluster

Knowledge and Technology Adoption, Diffusion, and Transfer - Advances in Knowledge Acquisition, Transfer, and Management ◽

10.4018/978-1-4666-1752-0.ch020 ◽

2012 ◽

pp. 275-286

Author(s):

Hong Lin ◽

Jeremy Kemp ◽

Padraic Gilbert

Keyword(s):

Gpu Computing ◽

Programming Model ◽

Large Data ◽

General Purpose ◽

Tuple Space ◽

Data Set ◽

Cuda Architecture ◽

High Level ◽

General Purpose Gpu ◽

Grid Cluster

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Towards a New Approach to Vision: Applications to Robots and Humans

Proceedings of the Human Factors Society Annual Meeting ◽

10.1177/154193128703101123 ◽

1987 ◽

Vol 31 (11) ◽

pp. 1281-1285

Author(s):

John G. Kreifeldt ◽

Ming C. Chuang

Keyword(s):

Spatial Perception ◽

Vision System ◽

Three Dimensional ◽

Robot Vision ◽

High Volume ◽

General Purpose ◽

Human Vision ◽

Computer Algorithms ◽

New Research ◽

High Level

A description of a novel and very speculative approach to new research directions for human vision with application to robotic vision is described. The goal of the approach is to propose a plausible, implementable, spatial perception model for human vision and apply this model to a stereo robot vision system. The model is based on computer algorithms variously called “Multidimensional Scaling”, well known in psychology and sociology but relatively unknown in engineering. These algorithms can reconstruct a spatially accurate model to a high level of metric precision of a “configuration of points” from low quality, error prone non-metric data about the configuration. ALSCAL – a general purpose computer package adaptable for this purpose is being presently evaluated. This is a departure from typical engineering approaches which are directed toward gathering a low volume of highly precise referenced data about the positions of selected points in the visual scene and substitutes instead an approach of gathering a high volume of very low precision relative data about the interpoint spacings. It would seem that the latter approach is the one actually used by the human vision system. The results are highly encouraging in that the agreement between test configurations of two and three dimensional configurations of points are very faithfully reconstructed from as low as 10 points in a configuration using only rank ordered (i.e. nonmetric) information about interpoint spacings. The reconstructions are remarkably robust even under human-like “fuzzy” imprecision in visual measurements.

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Computing Gamma Calculus on Computer Cluster

International Journal of Technology Diffusion ◽

10.4018/jtd.2010100104 ◽

2010 ◽

Vol 1 (4) ◽

pp. 42-52 ◽

Cited By ~ 7

Author(s):

Hong Lin ◽

Jeremy Kemp ◽

Padraic Gilbert

Keyword(s):

Gpu Computing ◽

Programming Model ◽

Large Data ◽

General Purpose ◽

Tuple Space ◽

Data Set ◽

Computer Cluster ◽

Cuda Architecture ◽

High Level ◽

General Purpose Gpu

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Guided surgery with 3D printed device: a case report

Journal of Oral Implantology ◽

10.1563/aaid-joi-d-19-00278 ◽

2020 ◽

Author(s):

Michelle Carvalho de Sales ◽

Rafael Maluza Flores ◽

Julianny da Silva Guimaraes ◽

Gustavo Vargas da Silva Salomao ◽

Tamara Kerber Tedesco ◽

...

Keyword(s):

Operating Time ◽

Three Dimensional ◽

Conventional Technique ◽

Cervical Region ◽

Mean Deviation ◽

Apical Region ◽

Virtual Planning ◽

Surgical Guide ◽

Surgical Guides ◽

High Level

Dental surgeons need in-depth knowledge of the bone tissue status and gingival morphology of atrophic maxillae. The aim of this study is to describe preoperative virtual planning of placement of five implants and to compare the plan with the actual surgical results. Three-dimensional planning of rehabilitation using software programs enables surgical guides to be specially designed for the implant site and manufactured using 3D printing. A patient with five teeth missing was selected for this study. The patient’s maxillary region was scanned with CBCT and a cast model was produced. After virtual planning using ImplantViewer, five implants were placed using a printed surgical guide. Two weeks after the surgical procedure, the patient underwent another CBCT scan of the maxilla. Statistically significant differences were detected between the virtually planned positions and the actual positions of the implants, with a mean deviation of 0.36 mm in the cervical region and 0.7 mm in the apical region. The surgical technique used enables more accurate procedures when compared to the conventional technique. Implants can be better positioned, with a high level of predictability, reducing both operating time and patient discomfort.

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Urban network of China from the perspective of population mobility: Three-dimensional co-occurrence of nodes and links

Environment and Planning A Economy and Space ◽

10.1177/0308518x21997818 ◽

2021 ◽

pp. 0308518X2199781

Author(s):

Xinyue Luo ◽

Mingxing Chen

Keyword(s):

Three Dimensional ◽

Geographical Distance ◽

Two Dimensional ◽

Population Mobility ◽

Urban Network ◽

Urban Networks ◽

Low Level ◽

Medium Level ◽

High Level ◽

External Connections

The nodes and links in urban networks are usually presented in a two-dimensional(2D) view. The co-occurrence of nodes and links can also be realized from a three-dimensional(3D) perspective to make the characteristics of urban network more intuitively revealed. Our result shows that the external connections of high-level cities are mainly affected by the level of cities(nodes) and less affected by geographical distance, while medium-level cities are affected by the interaction of the level of cities(nodes) and geographical distance. The external connections of low-level cities are greatly restricted by geographical distance.

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