Load Balancing for Molecular Dynamics Simulations on Heterogeneous Architectures

Seckler, Steffen; Tchipev, Nikola; Bungartz, Hans‐Joachim; Neumann, Philipp

doi:10.1109/hipc.2016.021

Cited by 12 publications

(7 citation statements)

References 16 publications

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“…A multidimensional, OpenMP-based coloring approach that operates on the linked cell data structure was discussed by Tchipev et al (2015), showing good scalability on Intel Xeon and Intel Xeon Phi architectures. k - d tree-based load balancing within has recently been used by Seckler et al (2016) to evenly distribute the computational load of the compute-intensive particle simulations on heterogeneous hardware systems.…”

Section: Related Work: Short-range MDmentioning

confidence: 99%

TweTriS: Twenty trillion-atom simulation

Tchipev

Seckler

Heinen

et al. 2019

The International Journal of High Performance Computing Applica

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Significant improvements are presented for the molecular dynamics code ls1 mardyn — a linked cell-based code for simulating a large number of small, rigid molecules with application areas in chemical engineering. The changes consist of a redesign of the SIMD vectorization via wrappers, MPI improvements and a software redesign to allow memory-efficient execution with the production trunk to increase portability and extensibility. Two novel, memory-efficient OpenMP schemes for the linked cell-based force calculation are presented, which are able to retain Newton’s third law optimization. Comparisons to well-optimized Verlet list-based codes, such as LAMMPS and GROMACS, demonstrate the viability of the linked cell-based approach. The present version of ls1 mardyn is used to run simulations on entire supercomputers, maximizing the number of sampled atoms. Compared to the preceding version of ls1 mardyn on the entire set of 9216 nodes of SuperMUC, Phase 1, 27% more atoms are simulated. Weak scaling performance is increased by up to 40% and strong scaling performance by up to more than 220%. On Hazel Hen, strong scaling efficiency of up to 81% and 189 billion molecule updates per second is attained, when scaling from 8 to 7168 nodes. Moreover, a total of 20 trillion atoms is simulated at up to 88% weak scaling efficiency running at up to 1.33 PFLOPS. This represents a fivefold increase in terms of the number of atoms simulated to date.

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Section: Related Work: Short-range MDmentioning

confidence: 99%

TweTriS: Twenty trillion-atom simulation

Tchipev

Seckler

Heinen

et al. 2019

The International Journal of High Performance Computing Applica

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“…Hierdurch lässt sich eine deutliche rechentechnische Effizienzsteigerung erzielen. Weitere Optimierungen von ls1 mardyn umfassten innovative Algorithmen zur Shared‐Memory‐Parallelisierung und Lastverteilung , um aktuellste heterogene Großrechnerarchitekturen zu unterstützen.…”

Section: Weiterentwicklung Der Simulationscodesls1 Mardynundms2unclassified

SkaSim – Scalable HPC Software for Molecular Simulation in the Chemical Industry

Vrabec

Bernreuther

Bungartz

et al. 2018

Chemie Ingenieur Technik

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Der vorliegende Übersichtsartikel berichtet über Fortschritte in der molekularen Modellierung und Simulation mittels massiv‐paralleler Hoch‐ und Höchstleistungsrechner (HPC). Im SkaSim‐Projekt arbeiteten dazu Partner aus der HPC‐Community mit Anwendern aus Wissenschaft und Industrie zusammen. Ziel dabei war es mittels HPC‐Methoden die Vorhersage von thermodynamischen Stoffdaten in Bezug auf Effizienz, Qualität und Zuverlässigkeit weiter zu optimieren. In diesem Zusammenhang wurden verschiedene Themen bearbeitet: Atomistische Simulation der homogenen Gasblasenbildung, Oberflächenspannung klassischer Fluide und ionischer Flüssigkeiten, multikriterielle Optimierung molekularer Modelle, Weiterentwicklung der Simulationscodes ls1 mardyn und ms2, atomistische Simulation von Gastrennprozessen, molekulare Membran‐Strukturgeneratoren, Transportwiderstände und gemischtypenspezifische Bewertung prädiktiver Stoffdatenmodelle.

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“…There have been extensive interest in MD parallelization of academic and industry software packages regarding redesigned algorithms [13][14][15][16] for multicore CPUs [17][18][19][20] and computer clusters. [21][22][23][24] Publications approach the topic in various ways, most of them implementing parallel force calculations, but solvent effect computation and integration were also investigated.…”

Section: Introductionmentioning

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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Load Balancing for Molecular Dynamics Simulations on Heterogeneous Architectures

Cited by 12 publications

References 16 publications

TweTriS: Twenty trillion-atom simulation

TweTriS: Twenty trillion-atom simulation

SkaSim – Scalable HPC Software for Molecular Simulation in the Chemical Industry

Classical molecular dynamics on graphics processing unit architectures

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