Hardware accelerator for molecular dynamics: MDGRAPE-2

Susukita, Ryutaro; Ebisuzaki, Toshikazu; Elmegreen, Bruce G.; Furusawa, Hideaki; Kato, Kazumichi; Kawai, Maki; Kobayashi, Yoshinao; Kondo, Takahiro; Mcniven, Geoffrey; Narumi, Takatsune; Yasuoka, Kenji

doi:10.1016/s0010-4655(03)00349-7

Cited by 65 publications

(48 citation statements)

References 24 publications

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“…The parallel GRAPE-5 cluster consists of eight host computers (Pentium 4/1.9 GHz, i845), each of which has one GRAPE-5 (Kawai et al 2000) board. The parallel MDGRAPE-2 cluster consists of eight host computers (Pentium 4/2.2 GHz, i850), each of which has one MDGRAPE-2 (Susukita et al 2003) board. For cosmological simulations we used one board GRAPE-5.…”

Section: Simulation Methodsmentioning

confidence: 99%

Structure of Dark Matter Halos from Hierarchical Clustering. III. Shallowing of the Inner Cusp

Fukushige

Kawai

Makino

2004

ApJ

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100

106

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We investigate the structure of the dark matter halo formed in the cold dark matter scenarios by N-body simulations with a parallel tree code on GRAPE cluster systems. We simulated eight halos with the mass of 4:4 ; 10 14 to 1:6 ; 10 15 M in SCDM and LCDM models using up to 30 million particles. With the resolution of our simulations, the density profile is reliable down to 0.2% of the virial radius. Our results show that the slope of inner cusp within 1% virial radius is shallower than À1.5, and the radius where the shallowing starts exhibits run-to-run variation, which means that the innermost profile is not universal.

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Section: Simulation Methodsmentioning

confidence: 99%

Structure of Dark Matter Halos from Hierarchical Clustering. III. Shallowing of the Inner Cusp

Fukushige

Kawai

Makino

2004

ApJ

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100

106

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“…For other applications, such as simulation of protein enzymatic mechanisms [50] or analysis of biomolecular interactions [51,52], explicit solvent models will become increasingly preferred. At the same time, increases in computing power and MD/GRAvity-PipE [53] hardware-accelerated systems will enable investigators to calculate average explicit-solvent characteristics for particular conformations of molecular systems of all scales. In order to bring the advances in explicit solvent simulations to docking and protein structure prediction problems, implicit solvent models must be adapted to mimic explicit solvent effects [54,55].…”

Section: Introductionmentioning

confidence: 99%

Solvent reaction field potential inside an uncharged globular protein: A bridge between implicit and explicit solvent models?

Cerutti

Baker

McCammon

2007

The Journal of Chemical Physics

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The solvent reaction field potential of an uncharged protein immersed in Simple Point Charge/ Extended (SPC/E) explicit solvent was computed over a series of molecular dynamics trajectories, intotal 1560 ns of simulation time. A finite, positive potential of 13 to 24 k b Te c −1 (where T = 300K), dependent on the geometry of the solvent-accessible surface, was observed inside the biomolecule. The primary contribution to this potential arose from a layer of positive charge density 1.0 Å from the solute surface, on average 0.008 e c /Å 3 , which we found to be the product of a highly ordered first solvation shell. Significant second solvation shell effects, including additional layers of charge density and a slight decrease in the short-range solvent-solvent interaction strength, were also observed. The impact of these findings on implicit solvent models was assessed by running similar explicit-solvent simulations on the fully charged protein system. When the energy due to the solvent reaction field in the uncharged system is accounted for, correlation between per-atom electrostatic energies for the explicit solvent model and a simple implicit (Poisson) calculation is 0.97, and correlation between per-atom energies for the explicit solvent model and a previously published, optimized Poisson model is 0.99.

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“…An alternative approach is to employ special purpose hardware specifically tailored for molecular dynamics simulation, such as MD-GRAPE [8] or Anton [9]. These can yield an improvement in performance of several orders of magnitude over commodity processors, however special hardware is expensive to buy, develop and -without continued development -Moore's Law ensures that gains in performance relative to general purpose computing hardware are rapidly eroded.…”

Section: Accelerator Processorsmentioning

confidence: 99%

“…In the case of molecular dynamics (MD) modeling, parallel codes such as NAMD [7] may be used to distribute simulations across multiple processors. Low latency, high bandwidth interconnections between processors are needed for good scalability and performance is ultimately limited by the size of the simulated system which must increase with processor count in order to maintain parallel efficiency.An alternative approach is to employ special purpose hardware specifically tailored for molecular dynamics simulation, such as MD-GRAPE [8] or Anton [9]. These can yield an improvement in performance of several orders of magnitude over commodity processors, however special hardware is expensive to buy, develop and -without continued development -Moore's Law ensures that gains in performance relative to general purpose computing hardware are rapidly eroded.…”

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confidence: 99%

The impact of accelerator processors for high-throughput molecular modeling and simulation

Giupponi

Harvey

Fabritiis

2008

Drug Discovery Today

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The recent introduction of cost-effective accelerator processors (APs), such as the IBM Cell processor and Nvidia's graphics processing units (GPUs), represents an important technological innovation which promises to unleash the full potential of atomistic molecular modeling and simulation for the biotechnology industry. Present accelerator processors can deliver over an order of magnitude more floatingpoint operations per second (flops) than standard processors, broadly equivalent to a decade of Moore's law growth, and significantly reduce the cost of current atombased molecular simulations. In conjunction with distributed and grid computing solutions, accelerated molecular simulations may finally be used to extend current in silico protocols by use of accurate thermodynamic calculations instead of approximate methods and simulate hundreds of protein-ligand complexes with full molecular specificity, a crucial requirement of in silico drug discovery workflows.Teaser phrase: New accelerated computing devices will unleash the predictive power of molecular modeling and simulation for biotechnology.Key words: Cell processor, graphics processing units (GPUs), accelerated computing, molecular modeling and simulation, drug discovery, distributed and grid computing Preprint submitted to Elsevier Science 5 August 2008Bringing a new drug to market is a long and expensive process[1] encompassing theoretical modeling, chemical synthesis and experimental and clinical trials. Despite the considerable growth of biotechnologies in the last ten years, the practical consequences of these techniques on the number of approved drugs has failed to meet expectation [2]. Nonetheless, the discovery process greatly benefits from the use of computational modeling [3], at least in the initial stages of compound discovery, screening and optimization [4].Among the techniques available, force-based molecular modeling methods (briefly, molecular modeling) such as molecular dynamics are particularly useful in studying molecular processes at the atomistic level, providing accurate information on macromolecular dynamics and thermodynamic properties. Bridging from the molecular-atomistic (femtosecond) to biological (micromillisecond) timescales is still an unaccomplished feat in computational biology: the complexity of the modeling impedes sufficient sampling of the evolution of the system, even on expensive high performance computing (HPC) resources. For instance, cost and sampling constraints have so far limited a routine molecular dynamics run over a PC cluster to a single protein system for tens of nanoseconds, barely sufficient to compute the binding free energy using an exact thermodynamic method [5]. This information is of great importance in the discovery process for virtual screening, lead optimization and in silico drug discovery [4], but needs to be computed for hundreds of protein-ligand systems efficiently and economically in order to open the way for molecular simulations to become a routine tool in the discovery workflows us...

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Hardware accelerator for molecular dynamics: MDGRAPE-2

Cited by 65 publications

References 24 publications

Structure of Dark Matter Halos from Hierarchical Clustering. III. Shallowing of the Inner Cusp

Structure of Dark Matter Halos from Hierarchical Clustering. III. Shallowing of the Inner Cusp

Solvent reaction field potential inside an uncharged globular protein: A bridge between implicit and explicit solvent models?

The impact of accelerator processors for high-throughput molecular modeling and simulation

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