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

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

BACKGROUND Materials by design is an appealing idea that is very hard to realize in practice. Combining the best of different ingredients in one ultimate material is a task for which we currently have no general solution. However, we do have some successful examples to draw upon: Composite materials and III-V heterostructures have revolutionized many aspects of our lives. Still, we need a general strategy to solve the problem of mixing and matching crystals with different properties, creating combinations with predetermined attributes and functionalities. ADVANCES Two-dimensional (2D) materials offer a platform that allows creation of heterostructures with a variety of properties. One-atom-thick crystals now comprise a large family of these materials, collectively covering a very broad range of properties. The first material to be included was graphene, a zero-overlap semimetal. The family of 2D crystals has grown to includes metals (e.g., NbSe 2 ), semiconductors (e.g., MoS 2 ), and insulators [e.g., hexagonal boron nitride (hBN)]. Many of these materials are stable at ambient conditions, and we have come up with strategies for handling those that are not. Surprisingly, the properties of such 2D materials are often very different from those of their 3D counterparts. Furthermore, even the study of familiar phenomena (like superconductivity or ferromagnetism) in the 2D case, where there is no long-range order, raises many thought-provoking questions. A plethora of opportunities appear when we start to combine several 2D crystals in one vertical stack. Held together by van der Waals forces (the same forces that hold layered materials together), such heterostructures allow a far greater number of combinations than any traditional growth method. As the family of 2D crystals is expanding day by day, so too is the complexity of the heterostructures that could be created with atomic precision. When stacking different crystals together, the synergetic effects become very important. In the first-order approximation, charge redistribution might occur between the neighboring (and even more distant) crystals in the stack. Neighboring crystals can also induce structural changes in each other. Furthermore, such changes can be controlled by adjusting the relative orientation between the individual elements. Such heterostructures have already led to the observation of numerous exciting physical phenomena. Thus, spectrum reconstruction in graphene interacting with hBN allowed several groups to study the Hofstadter butterfly effect and topological currents in such a system. The possibility of positioning crystals in very close (but controlled) proximity to one another allows for the study of tunneling and drag effects. The use of semiconducting monolayers leads to the creation of optically active heterostructures. The extended range of functionalities of such heterostructures yields a range of possible applications. Now the highest-mobility graphene transistors are achieved by encapsulating graphene with hBN. Photovoltaic and light-emitting devices have been demonstrated by combining optically active semiconducting layers and graphene as transparent electrodes. OUTLOOK Currently, most 2D heterostructures are composed by direct stacking of individual monolayer flakes of different materials. Although this method allows ultimate flexibility, it is slow and cumbersome. Thus, techniques involving transfer of large-area crystals grown by chemical vapor deposition (CVD), direct growth of heterostructures by CVD or physical epitaxy, or one-step growth in solution are being developed. Currently, we are at the same level as we were with graphene 10 years ago: plenty of interesting science and unclear prospects for mass production. Given the fast progress of graphene technology over the past few years, we can expect similar advances in the production of the heterostructures, making the science and applications more achievable.

2D materials and van der Waals heterostructures

Computer simulation of random sphere packing is important for the study of densely packed particulate systems. In previous work, quasi dynamics method (QDM), a heuristic collective random sphere packing algorithm was developed to effectively handle large numbers of densely packed spheres in complex geometries. In this work, a theoretical analysis of the convergence of QDM is performed and the impact of algorithm step size on the convergence is discussed. System potential functions that measure the overall system overlaps are introduced and defined. By using different system potentials, the convergence/stability of QDM for a sphere packing domain with and without active boundary conditions is investigated. QDM is proved to be strictly convergent with small step size when no active boundary constraint exists. When active boundary constraint is imposed, the upper limit of step size for convergence and the criteria for step size selection are theoretically analysed and obtained. Our analyses focus on systems packed with mono-dispersed spheres. The mathematical approaches for the analysis, however, can be easily modified for poly-dispersed sphere systems and extended to analyse other collective packing algorithms.

Convergence analysis of a heuristic collective sphere packing algorithm

We are developing and testing a new generation of parallel MC code called ARCHER-CT [1] to accurately and rapidly calculate radiation dose from X-ray Computed Tomography (CT) scans on the modern multi-core CPU and on two types of hardware accelerators: Nvidia Graphics Processing Unit (GPU) and Intel coprocessor. While benchmark tests against MCNPX have already indicated high accuracy and speed in ARCHERCT [1], a head-to-head comparison between experiment and simulation is performed herewith.

http://neams.rpi.edu/jiw2/papers/ANS%20110%20Xu.pdf

Testing of ARCHER-CT, A Fast Monte Carlo Code for CT Dose Calculation: Experiment versus Simulation

The utility model relates to the technical field of industrial production gas supply, and provides a low-temperature gas supply system which comprises a low-temperature storage tank, a vaporizer, a buffer tank and a bypass pipeline, an outlet of the low-temperature storage tank is communicated with an inlet of the vaporizer, and an outlet of the vaporizer is communicated with the lower portion ofthe buffer tank. One end of the bypass pipeline is communicated with the first pipeline, and the other end of the bypass pipeline is communicated with the upper part of the buffer tank; a first flow adjusting component is arranged on the bypass pipeline, and a second flow adjusting component is arranged on the second pipeline. The utility model provides a low-temperature gas supply system. Low-temperature liquid is vaporized into low-temperature gas by utilizing a self vaporizer; meanwhile, low-temperature liquid is led out through the bypass pipeline to cool the low-temperature gas; by combining the flow control of the first flow regulating part and the second flow regulating part, the temperature and the pressure of the low-temperature gas can be accurately controlled, the flow of the low-temperature gas can be accurately output, the low-temperature gas can be intermittently supplied, and the system is simple to operate, low in cost and low in energy consumption.

Low-temperature gas supply system

Volatile organic compounds (VOCs) are important precursors of ozone and PM2.5, and the reduction of VOCs emission is a key to control ozone and PM2.5 pollutions. For high-value VOCs such as oil gas, the cryogenic condensation method based on liquid nitrogen cooling is an effective way to recover VOCs. This method can achieve a refrigeration temperature of -170°C or even lower so that the treated exhaust gas concentration can satisfy the emission standard. In this paper, a thermodynamic model is established to calculate and analyze a designed three-stage condensation recovery VOCs system process based on liquid nitrogen cooling. A set of experimental equipment was further developed and tested. And the system process, calculation results, and experimental results are introduced.

https://doi.org/10.1088/1757-899x/1240/1/012098

Development of a volatile organic compounds cryogenic condensation recovery system cooled by liquid nitrogen

In this work we integrate MCNP and PROTEUS, two high-fidelity reactor physics codes, into the Nuclear Energy Advanced Modeling and Simulation (NEAMS)Workbench. Specifically, we enhance the user interface of each code by developing full-featured editor services. Next, we re-define MCNP textual outputs in HDF5 formats, at great convenience to the user. Further, these HDF5 outputs are (optionally) coerced to the OpenMC format, enabling powerful and common post-processing workflows. For compatible use of MCNP and PROTEUS, we develop a model unification workflow, converting PROTEUS to MCNP-compatible meshes and generating a corresponding template MCNP deck. Finally, we investigate cooperative use of MCNP and PROTEUS for hybrid Monte Carlo and deterministic variance reduction through Consistent Adjoint-Driven Importance Sampling (CADIS), demonstrated with a fixed-source shielding problem. Ultimately, we find our integration effort dramatically streamlines pre- and post-processing with MCNP and PROTEUS and enables transformative hybrid Monte Carlo and deterministic workflows.

/pdf/scientific-workflows-for-mcnp6-and-proteus-within-the-neams-16od3qgt4b.pdf

Wei Ji

Papers

Convergence analysis of a heuristic collective sphere packing algorithm

Testing of ARCHER-CT, A Fast Monte Carlo Code for CT Dose Calculation: Experiment versus Simulation

Low-temperature gas supply system

Development of a volatile organic compounds cryogenic condensation recovery system cooled by liquid nitrogen

Scientific Workflows for MCNP6 and Proteus Within the Neams Workbench