https://www.sandia.gov/~sjplimp/papers/jcompphys95.pdf

Fast parallel algorithms for short-range molecular dynamics

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.

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

/pdf/scalable-molecular-dynamics-with-namd-3j7xyc70g4.pdf

Scalable Molecular Dynamics with NAMD

http://kth.diva-portal.org/smash/get/diva2:1303357/FULLTEXT01

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

The Rayleigh-Taylor instability (RTI) is investigated using the direct simulation Monte Carlo method of molecular gas dynamics. Fully resolved two-dimensional simulations are performed to quantify the growth of flat and single-mode perturbed interfaces between two atmospheric-pressure monatomic gases as a function of the Atwood number and the gravitational acceleration. Future simulations on more extreme computational platforms will enable investigation of the RTI in greater detail.

Direct simulation Monte Carlo investigation of the Rayleigh-Taylor instability

The transition from two-dimensional (2D) to three-dimensional (3D) granular packings is studied using large-scale discrete element computer simulations. We focus on vertical stress profiles and examine how they change with dimensionality from 2D to 3D. We compare results for packings in 2D, quasi-2D packings between flat plates, and 3D packings. Analysis of these packings suggests that the Janssen theory does not fully describe these packings, especially at the top of the piles, where a hydrostatic-like region of vertical stress is visible in all cases. We find that the interior of the packing is far from incipient failure, while in general, the forces at the walls are close to incipient failure.

Discrete element simulations of stress distributions in silos: crossover from two to three dimensions

The Richtmyer-Meshkov instability (RMI) is investigated using the Direct Simulation Monte Carlo (DSMC) method of molecular gas dynamics. Due to the inherent statistical noise and the significant computational requirements, DSMC is hardly ever applied to hydrodynamic flows. Here, DSMC RMI simulations are performed to quantify the shock-driven growth of a single-mode perturbation on the interface between two atmospheric-pressure monatomic gases prior to re-shocking as a function of the Atwood and Mach numbers. The DSMC results qualitatively reproduce all features of the RMI and are in reasonable quantitative agreement with existing theoretical and empirical models. Consistent with previous work in this field, the DSMC simulations indicate that RMI growth follows a universal behavior.

Direct simulation Monte Carlo investigation of the Richtmyer-Meshkov instability

Stochastic rotation dynamics (SRD) is a relatively recent technique, closely related to lattice Boltzmann, for capturing hydrodynamic fluid flow at the mesoscale. The SRD method is based on simple constituent fluid particle interactions and dynamics. Here we parametrize the SRD fluid to provide a one to one match in the shear viscosity of a Lennard-Jones fluid and present viscosity measurements for a range of such parameters. We demonstrate how to apply the Muller-Plathe reverse perturbation method for determining the shear viscosity of the SRD fluid and discuss how finite system size and momentum exchange rates effect the measured viscosity. The implementation and performance of SRD in a parallel molecular dynamics code is also described.

Mesoscale hydrodynamics via stochastic rotation dynamics: comparison with Lennard-Jones fluid.

Neural networks are an increasingly attractive algorithm for natural language processing and pattern recognition. Deep networks with >50M parameters are made possible by modern GPU clusters operating at <50 pJ per op and more recently, production accelerators capable of <5pJ per operation at the board level. However, with the slowing of CMOS scaling, new paradigms will be required to achieve the next several orders of magnitude in performance per watt gains. Using an analog resistive memory (ReRAM) crossbar to perform key matrix operations in an accelerator is an attractive option. This work presents a detailed design using a state of the art 14/16 nm PDK for of an analog crossbar circuit block designed to process three key kernels required in training and inference of neural networks. A detailed circuit and device-level analysis of energy, latency, area, and accuracy are given and compared to relevant designs using standard digital ReRAM and SRAM operations. It is shown that the analog accelerator has a 270x energy and 540x latency advantage over a similar block utilizing only digital ReRAM and takes only 11 fJ per multiply and accumulate (MAC). Compared to an SRAM based accelerator, the energy is 430X better and latency is 34X better. Although training accuracy is degraded in the analog accelerator, several options to improve this are presented. The possible gains over a similar digital-only version of this accelerator block suggest that continued optimization of analog resistive memories is valuable. This detailed circuit and device analysis of a training accelerator may serve as a foundation for further architecture-level studies.

Steven J. Plimpton

Papers

Direct simulation Monte Carlo investigation of the Rayleigh-Taylor instability

Discrete element simulations of stress distributions in silos: crossover from two to three dimensions

Direct simulation Monte Carlo investigation of the Richtmyer-Meshkov instability

Mesoscale hydrodynamics via stochastic rotation dynamics: comparison with Lennard-Jones fluid.

Multiscale Co-Design Analysis of Energy, Latency, Area, and Accuracy of a ReRAM Analog Neural Training Accelerator