Efficiency of linked cell algorithms

The linked cell list algorithm is an essential part of molecular simulation software, both molecular dynamics and Monte Carlo. Though it scales linearly with the number of particles, there has been a constant interest in increasing its efficiency, because a large part of CPU time is spent to identify the interacting particles. Several recent publications proposed improvements to the algorithm and investigated their efficiency by applying them to particular setups. In this publication we develop a general method to evaluate the efficiency of these algorithms, which is mostly independent of the parameters of the simulation, and test it for a number of linked cell list algorithms. We also propose a combination of linked cell reordering and interaction sorting that shows a good efficiency for a broad range of simulation setups.Comment: Submitted to Computer Physics Communications on 22 December 2009, still awaiting a referee repor

Numerical simulation of InGaN Schottky solar cell

The Indium Gallium Nitride (InGaN) III-Nitride ternary alloy has the potentiality to allow achieving high efficiency solar cells through the tuning of its band gap by changing the Indium composition. It also counts among its advantages a relatively low effective mass, high carriers\^a mobility, a high absorption coefficient along with good radiation tolerance.However, the main drawback of InGaN is linked to its p-type doping, which is difficult to grow in good quality and on which ohmic contacts are difficult to realize. The Schottky solar cell is a good alternative to avoid the p-type doping of InGaN. In this report, a comprehensive numerical simulation, using mathematically rigorous optimization approach based on state-of-the-art optimization algorithms, is used to find the optimum geometrical and physical parameters that yield the best efficiency of a Schottky solar cell within the achievable device fabrication range. A 18.2% efficiency is predicted for this new InGaN solar cell design

Collision Detection and Administration Methods for Many Particles with Different Sizes

This paper deals with the calculation of the motion and the adminis-tration of the contacts for systems with many colliding bodies of round shape and possibly large size-differences. Both two dimensional (2D) and three dimensional (3D) cases are investigated, while the efficiency of the employed algorithms is compared. For the integration of the equations of motion, standard methods are used, but to reduce the effort for collision detection, more sophisticated administration algorithms for the neighbor-hood search are prosented. Especially for large systems with many parti-cles and a wide, polydisperse size distribution, this is a challenge. Three methods, the Verlet-Neighbor List (VL), the Linked Cell (LC) method, and the Linked Linear List (LLL), are discussed and compared for 2D and 3D. Only LLL performs well for strongly different particle sizes

Improved neighbor list algorithm in molecular simulations using cell decomposition and data sorting method

An improved neighbor list algorithm is proposed to reduce unnecessary interatomic distance calculations in molecular simulations. It combines the advantages of Verlet table and cell linked list algorithms by using cell decomposition approach to accelerate the neighbor list construction speed, and data sorting method to lower the CPU data cache miss rate, as well as partial updating method to minimize the unnecessary reconstruction of the neighbor list. Both serial and parallel performance of molecular dynamics simulation are evaluated using the proposed algorithm and compared with those using conventional Verlet table and cell linked list algorithms. Results show that the new algorithm outperforms the conventional algorithms by a factor of 2~3 in cases of both small and large number of atoms.Comment: 14 pages, 7 figures. Submitted to Computer Physics Communication

Identifying Web Tables - Supporting a Neglected Type of Content on the Web

The abundance of the data in the Internet facilitates the improvement of extraction and processing tools. The trend in the open data publishing encourages the adoption of structured formats like CSV and RDF. However, there is still a plethora of unstructured data on the Web which we assume contain semantics. For this reason, we propose an approach to derive semantics from web tables which are still the most popular publishing tool on the Web. The paper also discusses methods and services of unstructured data extraction and processing as well as machine learning techniques to enhance such a workflow. The eventual result is a framework to process, publish and visualize linked open data. The software enables tables extraction from various open data sources in the HTML format and an automatic export to the RDF format making the data linked. The paper also gives the evaluation of machine learning techniques in conjunction with string similarity functions to be applied in a tables recognition task.Comment: 9 pages, 4 figure

A short-loop algorithm for quantum Monte Carlo simulations

We present an algorithmic framework for a variant of the quantum Monte Carlo operator-loop algorithm, where non-local cluster updates are constructed in a way that makes each individual loop smaller. The algorithm is designed to increase simulation efficiency in cases where conventional loops become very large, do not close altogether, or otherwise behave poorly. We demonstrate and characterize some aspects of the short-loop on a square lattice spin-1/2 XXZ model where, remarkably, a significant increase in simulation efficiency is observed in some parameter regimes. The simplicity of the model provides a prototype for the use of short-loops on more complicated quantum systems.Comment: 9 pages, 9 figures: new FSS discussion adde

Efficient Parallelization of Short-Range Molecular Dynamics Simulations on Many-Core Systems

This article introduces a highly parallel algorithm for molecular dynamics simulations with short-range forces on single node multi- and many-core systems. The algorithm is designed to achieve high parallel speedups for strongly inhomogeneous systems like nanodevices or nanostructured materials. In the proposed scheme the calculation of the forces and the generation of neighbor lists is divided into small tasks. The tasks are then executed by a thread pool according to a dependent task schedule. This schedule is constructed in such a way that a particle is never accessed by two threads at the same time.Benchmark simulations on a typical 12 core machine show that the described algorithm achieves excellent parallel efficiencies above 80 % for different kinds of systems and all numbers of cores. For inhomogeneous systems the speedups are strongly superior to those obtained with spatial decomposition. Further benchmarks were performed on an Intel Xeon Phi coprocessor. These simulations demonstrate that the algorithm scales well to large numbers of cores.Comment: 12 pages, 8 figure