Accelerating Molecular Dynamics Simulation Using Graphics Processing Unit

Myung, Hun Joo; Sakamaki, Ryuji; Oh, Kwang Jin; Narumi, Takatsune; Yasuoka, Kenji; Lee, Sik

doi:10.5012/bkcs.2010.31.12.3639

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Other works involving GPU-based MD codes, going back to 2007, can be found in Refs. [25][26][27][28][29][30][31][32][33][34][35][36]. We omit a detailed exposition of GPU programming basics here; for a good overview of massive multi-threading using CUDA see the relevant section in the article by Anderson et al [11] For further information the reader can consult the book by Kirk and Hwu [37] as well as the CUDA programming guide [38].…”

Section: Introductionmentioning

confidence: 99%

RUMD: A general purpose molecular dynamics package optimized to utilize GPU hardware down to a few thousand particles

et al. 2017

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RUMD is a general purpose, high-performance molecular dynamics (MD) simulation package running on graphical processing units (GPU's). RUMD addresses the challenge of utilizing the many-core nature of modern GPU hardware when simulating small to medium system sizes (roughly from a few thousand up to hundred thousand particles). It has a performance that is comparable to other GPU-MD codes at large system sizes and substantially better at smaller sizes. RUMD is open-source and consists of a library written in C++ and the CUDA extension to C, an easy-to-use Python interface, and a set of tools for set-up and post-simulation data analysis. The paper describes RUMD's main features, optimizations and performance benchmarks.

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Section: Introductionmentioning

confidence: 99%

RUMD: A general purpose molecular dynamics package optimized to utilize GPU hardware down to a few thousand particles

et al. 2017

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“…Myung et al 135 (G2, PF, CN, ES, FS/FD, SC, AA). Implementing non-bonded forces was the focus of Myung et al The N-squared algorithm is accelerated with CUDA based on force decomposition, assigning the force calculation of each atom to a separate thread.…”

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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“…Computer hardware enhancements have included more powerful supercomputers (an updated list of the top-500 supercomputers can be found at: http://www.top500. org), the exploitation of graphics processor unit technology by MD codes (9), and custom-built hardware designed specifically for MD simulations, such as the ANTON supercomputer built by D. E. Shaw Research (New York, NY) (10). An ANTON machine consists of application-specific integrated circuits, interconnected by a specialized highspeed, three-dimensional torus network.…”

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confidence: 99%

Molecular Simulations of Gram-Negative Bacterial Membranes: A Vignette of Some Recent Successes

Parkin

Chavent

Khalid

2015

Biophysical Journal

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In the following review we use recent examples from the literature to discuss progress in the area of atomistic and coarse-grained molecular dynamics simulations of selected bacterial membranes and proteins, with a particular focus on Gram-negative bacteria. As structural biology continues to provide increasingly high-resolution data on the proteins that reside within these membranes, simulations have an important role to play in linking these data with the dynamical behavior and function of these proteins. In particular, in the last few years there has been significant progress in addressing the issue of biochemical complexity of bacterial membranes such that the heterogeneity of the lipid and protein components of these membranes are now being incorporated into molecular-level models. Thus, in future we can look forward to complementary data from structural biology and molecular simulations combining to provide key details of structure-dynamics-function relationships in bacterial membranes.

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Accelerating Molecular Dynamics Simulation Using Graphics Processing Unit

Cited by 5 publications

References 24 publications

RUMD: A general purpose molecular dynamics package optimized to utilize GPU hardware down to a few thousand particles

RUMD: A general purpose molecular dynamics package optimized to utilize GPU hardware down to a few thousand particles

Classical molecular dynamics on graphics processing unit architectures

Molecular Simulations of Gram-Negative Bacterial Membranes: A Vignette of Some Recent Successes

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