Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Molecular simulation is an extremely useful, but computationally very expensive tool for studies of chemical and biomolecular systems Here, we present a new implementation of our molecular simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines The code encompasses a minimal-communication domain decomposition algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addition used a Multiple-Program, Multiple-Data approach, with separate node domains responsible for direct and reciprocal space interactions Not only does this combination of a

/pdf/gromacs-4-algorithms-for-highly-efficient-load-balanced-and-16xnd83poh.pdf

GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

IEEE Transactions on Pattern Analysis and Machine Intelligence

We generalize the classic self-organizing map (SOM) in flat Euclidean space (linear manifold) onto a Riemannian manifold. Both sequential and batch learning algorithms for the generalized SOM are presented. Compared with the classical SOM, the most novel feature of the generalized SOM is that it can learn the intrinsic topological neighborhood structure of the underlying Riemannian manifold that fits to the input data. We here compared the performance of the generalized SOM and the classical SOM using real 3-Dimensional facial surface normals data. Experimental results show that the generalized SOM outperforms the classical SOM when the data lie on a curved Riemannian manifold.

Learning a Self-organizing Map Model on a Riemannian Manifold

A bathing pad comprises a compliant body, a support structure, a plurality of anti-slip members and a temperature sensing device. The compliant body includes end faces, a top face, a bottom face and side faces. The support structure includes a horizontally extending portion engaged with the bottom face of the compliant body and a vertically extending portion engaged with one of the end faces of the compliant body. The support structure exhibits substantial rigidity with respect to compliance of the compliant body. The plurality of anti-slip members are attached to the horizontally extending portion of the support structure. The temperature sensing device is attached to the compliant body and is visibly exposed at one of the faces of the compliant body. The temperature sensing device provides for a visual indication of temperature of water in contact therewith.

Bathtub floor pad

In this paper we present a statistical model, learnt from empirical data, which captures variations in the spectra of daylight. We demonstrate two novel techniques which use the model to constrain vision and graphics problems. The first uses the model generatively to render scenes using spectra which are constrained to be plausible. The second uses the model to solve the ill-posed problem of estimating high dimensional illumination spectra from one or more tristimulus images, such as might be observed over the course of a day.

A Statistical Model for Daylight Spectra

In this paper, we present a reflectance-aware optical flow technique for nonrigidly aligning images captured in different lighting environments. We consider alignment between two specific lighting conditions of particular relevance to object capture using a light stage. The technique uses optical flow combined with three image transformation functions, namely a) the Illumination-Independent image transformation, b) the image colour transformation and c) the specular-invariant projection. We explore two types of photometric image alignment problem, i) aligning images captured with different spherical gradient patterns but the same types of light source and ii) aligning the spherical gradient sequence to an image captured with a different light source. Our results show that a previously proposed method can accurately solve the first problem and we propose a model-based approach to solving the second.

Reflectance-aware optical flow

In this paper we make two contributions to the problem of recovering surface shape from single images of faces. The first of these is to develop a representation of the distribution of surface normals based on the exponential map, and to show how to model shape-deformations using principal geodesic analysis on the exponential map. The second contribution is to show how ideas from robust statistics can be used to fit the model to facial images in which there is significant self-shadowing. The method is evaluated on both synthetic and real-world images. It is demonstrated to effectively fill-in the facial surface when more than 30% of the area is subject to self-shadowing.

William A. P. Smith

Papers

Learning a Self-organizing Map Model on a Riemannian Manifold

Bathtub floor pad

A Statistical Model for Daylight Spectra

Reflectance-aware optical flow

Facial shape-from-shading using principal geodesic analysis and robust statistics