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 present a parameter-free method for estimating the BRDF of a subject's skin from a single image. We show how the technique can be used for photometric correction as a preprocessing step for face analysis tasks, and show its application to graphics by re-rendering faces with the different skin reflectance models.

Skin reflectance modelling for face recognition

In this paper, we perform gender classification based on 2.5D facial surface normals (facial needle-maps), and present two novel principal geodesic analysis (PGA) methods, weighted PGA and supervised PGA, to parameterize the facial needle-maps, and compare their performances with PGA for gender classification. Experimental results demonstrate the feasibility of gender classification based on facial needle-maps, and show that incorporating weights or pairwise relationships of labeled data into PGA improves the gender discriminating powers in the leading eigenvectors and the gender classification accuracy.

Gender classification based on facial surface normals

We explore the use of convolutional neural networks (CNNs) for filling voids in digital elevation models (DEM). We propose a baseline approach using a fully convolutional network to predict complete from incomplete DEMs, which is trained in a supervised fashion. We then extend this to a shadow-constrained CNN (SCCNN) by introducing additional loss functions that encourage the restored DEM to adhere to geometric constraints implied by cast shadows. At the training time, we use automatically extracted cast shadow maps and known sun directions to compute the shadow-based supervisory signal in addition to the direct DEM supervision. At the test time, our network directly predicts restored DEMs from an incomplete DEM. One key advantage of our SCCNN model is that it is characterized by both CNN data inference and geometric shadow cues. It thus avoids data restoration that may violate shadowing conditions. Both our baseline CNN and SCCNN outperform the inverse distance weighting (IDW)-based interpolation method, with the shadow supervision enabling SCCNN to obtain the best performance.

/pdf/filling-voids-in-elevation-models-using-a-shadow-constrained-ed87r88868.pdf

Filling Voids in Elevation Models Using a Shadow-Constrained Convolutional Neural Network

Molecular dynamics simulations have been performed with the objective of understanding the phenomenon of nanoparticle aggregation. We have attempted to calculate the free energy associated with the interaction between two 38-atom gold nanocores, with attached passivating thiol chains, in a supercritical ethane solvent and in the vacuum, and without passivating chains in ethane at the critical density and twice the critical density. Our model differs from those used by others in that each gold nanocore is bound by a realistic metal potential that is not formally rigid. In the case of the passivated nanoparticles, we observe profound structural changes in the nanocores, which radically affect the nature of the interaction between them—to the extent that fusion of the two gold nanocores cannot be prevented under the conditions examined. Bare nanocores, on the other hand, do not exhibit much structural change until close contact occurs. The fused nanocores in the passivated and bare nanocore systems have significantly different morphologies. There is evidence that at higher solvent density, the interaction between bare nanocores is slightly repulsive.

A computer simulation study of the interaction between passivated and bare gold nanoclusters

In this paper, we show how to extract gender discriminating features from 2.5D facial needle-maps. The standard eigenspace analysis method for non-Euclidean data is principal geodesic analysis (PGA). Based on PGA, we propose a novel supervised weighted PGA method which incorporates local weights into standard PGA to improve gender discriminating capability of the extracted features. The weight map is iteratively optimized from the labeled data, which is different from other gender relevant weights used in the literature [1]. Experimental results illustrate the effectiveness of this method and its successful application to gender classification.

William A. P. Smith

Papers

Skin reflectance modelling for face recognition

Gender classification based on facial surface normals

Filling Voids in Elevation Models Using a Shadow-Constrained Convolutional Neural Network

A computer simulation study of the interaction between passivated and bare gold nanoclusters

Extracting gender discriminating features from facial needle-maps