What is a LAMMPS Container in the NVIDIA GPU Cloud?

March 15, 2022

LAMMPS is a molecular dynamics simulation software tool. It is a parallel computing system that you can create and use on a laptop or computer. It can run in serial and parallel mode on any machine that supports the MPI message-passing library. Shared-memory boxes, distributed-memory clusters, and supercomputers all fall within this category. LAMMPS supports OpenMP multithreading, vectorization, and GPU acceleration. It is built to be flexible enough to add additional features like force fields, atom kinds, boundary conditions, and diagnostics.

LAMMPS integrates Newton's equations of motion for the collection of interacting particles. An atom, molecule, electron, a coarse-grained cluster of atoms, a mesoscopic particle, or microscopic clump of material can all be considered single particles. The interaction models contained in LAMMPS are generally short-range in nature, with a few long-range models thrown in for good measure. LAMMPS uses message-passing techniques and spatial decomposition of the simulation domain to run on single processors or in parallel. LAMMPS also allows for rapid coupling of spin and molecular dynamics. LAMMPS is also linked to a variety of analysis tools and engines.

LAMMPS Container Features

  • LAMMPS is a GPLv2 licensed open-source distribution that runs on a single processor or in parallel distributed memory message-passing parallelism (MPI).
  • Shared memory multithreading parallelism (OpenMP) 
  • Spatial decomposition of simulation domain for MPI parallelism 
  • Particle decomposition inside of spatial decomposition for OpenMP and GPU parallelism 
  • C++-11 modular code with the most functionality in optional packages.
  • Rely on the MPI library for basic parallel capability and the MPI stub for serial compilation. All other libraries are optional and only necessary for specific packages. 
  • Many code features are supported by GPU, Intel Xeon Phi, and OpenMP, making it easy to add new features and capabilities. 
  • Runs based on an input script
  • Variables and formulae: syntax for declaring and using variables and formulas 
  • Looping overruns and breaking out of loops syntax 
  • Invoke LAMMPS through the library interface or provided Python wrapper or SWIG-based wrappers. 
  • Coupled with other codes: Other code calls LAMMPS, and LAMMPS calls other code. Both LAMMPS and umbrella codes are used.

LAMMPS Ensembles, Constraints, and Boundary Conditions

  • 2d or 3d systems 
  • Orthogonal or non-orthogonal simulation domains 
  • Options for atoms' groups and geometric regions to be thermostated 
  • Pressure control in 1 to 3 dimensions through Nose or Berendsen barostatting 
  • Deformation of a simulation box (tensile and shear) 
  • Rigid body constraints are imposed via the harmonic (umbrella) constraint. 
  • Manifold surface motion restrictions 
  • Bond breaking, formation, swapping, template-based reaction modeling
  • Several types of atom/molecule insertion and deletion barriers
  • Static and moving non-equilibrium molecular dynamics (NEMD), and 
  • several other boundary conditions and limitations

Particle and Model Types

  • Atoms 
  • Particles with a coarse granularity (for example, bead-spring polymers) 
  • Organic compounds or polymers with a single atom 
  • Polymers with all atoms, organic compounds, proteins, and DNA metals 
  • Granular materials 
  • Coarse-grained mesoscale models 
  • Stiff collections of n particles 
  • Hybrid combinations of these finite-size spherical and ellipsoidal particles 
  • Finite-size line segment (2d) and triangle (3d) particles 
  • Finite-size rounded polygons (2d) and polyhedra (3d) particles

LAMMPS Open-Source License

GPL Version

LAMMPS is a free open-source program distributed under the terms of the GNU Public License Version 2 (GPLv2). It means you can use or adjust the program however you want for your purposes. But you must follow some rules when redistributing it - especially in binary form - or distributing software derived from it or containing parts of it. LAMMPS containers are sold without any warranty.

LGPL Version

This version removes all the Non-LGPL compatible files and is only available upon request. Users can link non-GPL compatible software to the (otherwise unmodified) LAMMPS library or dynamically load it at runtime as a result of this. Any modifications to the LAMMPS code must be provided under the same open-source terms as LAMMPS itself, even if the LGPL license version is used. 

Final Words

LAMMPS is a molecular dynamics (MD) simulation engine. It only has a limited range of features for creating simulations and analyzing their results. LAMMPS keeps track of adjacent particles using neighbor lists. The lists are designed for systems with particles that repel each other across short distances, ensuring that the particle density in the local area never exceeds a certain threshold. 

On parallel machines, LAMMPS partitions the simulation domain into small sub-domains of similar computing cost, one for each processor, using spatial-decomposition techniques and MPI parallelization. In addition, multi-threading parallelization and GPU acceleration with particle-decomposition are possible.

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A Complete Guide To Customer Acquisition For Startups

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You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

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Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

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Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

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What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

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Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
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GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

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What does GAUDI do?

GAUDI can perform multiple functions –

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How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

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  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/ 

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html

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