50 million atoms scale molecular dynamics modelling on a single consumer graphics card

Xiao, Gao; Ren, Mengye; Hong, Haibo

doi:10.1016/j.advengsoft.2018.08.004

Cited by 8 publications

(8 citation statements)

References 30 publications

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“…This approach enables simulation timescales that are 2 to 3 orders of magnitude larger than atomistic simulations. Thus, the advantages of CG simulations have spurred interest in simulations of larger sizes (hundreds of nanometers) and longer timescales (tens of microseconds) [177,178,179,180]. Others and we have rigorously demonstrated the ability of MD simulations to capture residue-level precision in membrane protein interactions [91,94,97,108,110,115].…”

Section: Computational Toolkitmentioning

confidence: 99%

Computational Nanoscopy of Tight Junctions at the Blood–Brain Barrier Interface

Rajagopal

Irudayanathan

Nangia

2019

IJMS

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The selectivity of the blood–brain barrier (BBB) is primarily maintained by tight junctions (TJs), which act as gatekeepers of the paracellular space by blocking blood-borne toxins, drugs, and pathogens from entering the brain. The BBB presents a significant challenge in designing neurotherapeutics, so a comprehensive understanding of the TJ architecture can aid in the design of novel therapeutics. Unraveling the intricacies of TJs with conventional experimental techniques alone is challenging, but recently developed computational tools can provide a valuable molecular-level understanding of TJ architecture. We employed the computational methods toolkit to investigate claudin-5, a highly expressed TJ protein at the BBB interface. Our approach started with the prediction of claudin-5 structure, evaluation of stable dimer conformations and nanoscale assemblies, followed by the impact of lipid environments, and posttranslational modifications on these claudin-5 assemblies. These led to the study of TJ pores and barriers and finally understanding of ion and small molecule transport through the TJs. Some of these in silico, molecular-level findings, will need to be corroborated by future experiments. The resulting understanding can be advantageous towards the eventual goal of drug delivery across the BBB. This review provides key insights gleaned from a series of state-of-the-art nanoscale simulations (or computational nanoscopy studies) performed on the TJ architecture.

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Section: Computational Toolkitmentioning

confidence: 99%

Computational Nanoscopy of Tight Junctions at the Blood–Brain Barrier Interface

Rajagopal

Irudayanathan

Nangia

2019

IJMS

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“…Xiao et al 193 (MB, G4, PA, CC, FS, SC, AA). Xiao et al present a dynamic cell list method framework for use in largescale MD runs with more than 50 million atoms on the used GPU.…”

Section: Other Published Implementationsmentioning

confidence: 99%

Classical molecular dynamics on graphics processing unit architectures

Jász¹,

Rák²,

Ladjánszki³

et al. 2019

WIREs Comput Mol Sci

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Molecular dynamics (MD) has experienced a significant growth in the recent decades. Simulating systems consisting of hundreds of thousands of atoms is a routine task of computational chemistry researchers nowadays. Thanks to the straightforwardly parallelizable structure of the algorithms, the most promising method to speed‐up MD calculations is exploiting the large‐scale processing power offered by the parallel hardware architecture of graphics processing units or GPUs. Programming GPUs is becoming easier with general‐purpose GPU computing frameworks and higher levels of abstraction. In the recent years, implementing MD simulations on graphics processors has gained a large interest, with multiple popular software packages including some form of GPU‐acceleration support. Different approaches have been developed regarding various aspects of the algorithms, with important differences in the specific solutions. Focusing on published works in the field of classical MD, we describe the chosen implementation methods and algorithmic techniques used for porting to GPU, as well as how recent advances of GPU architectures will provide even more optimization possibilities in the future. This article is characterized under: Software > Simulation Methods Computer and Information Science > Computer Algorithms and Programming Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

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“…Such advances have been possible largely due to the advent of GPUs, necessitating the reimplementation of MD algorithms to extract maximal performance on both GPUs and increasingly hierarchical multi-core CPUs. These re-implementations have resulted in many GPU-accelerated MD codes [6][7][8][9][10], as well as calls for performance portability measures [11]. With exascale computing on the horizon [12], modeling and simulations tools can be increasingly used to solve 'grand challenges' in material science, such as understanding high-temperature superconductivity using quantum mechanical (QM) simulations, simulating energetic materials under extreme conditions at an atomistic level, and enabling additive manufacturing (AM) with multi-scale modeling of the processes involved.…”

Section: Introductionmentioning

confidence: 99%