BlueGene/L applications: Parallelism On a Massive Scale

Supinski, Bronis R. de; Schulz, Martin; Bulatov, Vasily V.; Cabot, W.; Chan, Betty; Cook, Andrew W.; Draeger, Erik W.; Glosli, James N.; Greenough, Jeffrey A.; Henderson, Keith; Kubota, Alison; Louis, S.; Miller, Brian J.; Patel, Mehul V.; Spelce, Thomas E.; Streitz, Frederick H.; Williams, Peter; Yates, Robert K.; Yoo, Andy; Almási, George; Bhanot, Gyan; Gara, Alan; Gunnels, John A.; Gupta, Manish; Moreira, José E.; Sexton, James; Walkup, Bob; Archer, Charles J; Gygi, François; Germann, Timothy C.; Kadau, Kai; Lomdahl, Peter S.; Rendleman, Charles A.; Welcome, Michael; McLendon, William Clarence; Hendrickson, Bruce; Franchetti, Franz; Král, Štefan; Lorenz, J.; Überhuber, Christoph; Chow, Edmond; Çatalyürek, Ümit V.

doi:10.1177/1094342007085025

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Direct comparison between simulations and experiments requires both an accurate description of the system and the possibility to sample extensively the configuration space. In order to observe folding with molecular dynamics, it is necessary to use very large computers [4] , [5] , worldwide distributed computing [6] , or an enhanced sampling technique [7] – [16] .…”

Section: Introductionmentioning

confidence: 99%

A Kinetic Model of Trp-Cage Folding from Multiple Biased Molecular Dynamics Simulations

et al. 2009

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Trp-cage is a designed 20-residue polypeptide that, in spite of its size, shares several features with larger globular proteins. Although the system has been intensively investigated experimentally and theoretically, its folding mechanism is not yet fully understood. Indeed, some experiments suggest a two-state behavior, while others point to the presence of intermediates. In this work we show that the results of a bias-exchange metadynamics simulation can be used for constructing a detailed thermodynamic and kinetic model of the system. The model, although constructed from a biased simulation, has a quality similar to those extracted from the analysis of long unbiased molecular dynamics trajectories. This is demonstrated by a careful benchmark of the approach on a smaller system, the solvated Ace-Ala3-Nme peptide. For the Trp-cage folding, the model predicts that the relaxation time of 3100 ns observed experimentally is due to the presence of a compact molten globule-like conformation. This state has an occupancy of only 3% at 300 K, but acts as a kinetic trap. Instead, non-compact structures relax to the folded state on the sub-microsecond timescale. The model also predicts the presence of a state at of 4.4 Å from the NMR structure in which the Trp strongly interacts with Pro12. This state can explain the abnormal temperature dependence of the and chemical shifts. The structures of the two most stable misfolded intermediates are in agreement with NMR experiments on the unfolded protein. Our work shows that, using biased molecular dynamics trajectories, it is possible to construct a model describing in detail the Trp-cage folding kinetics and thermodynamics in agreement with experimental data.

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Section: Introductionmentioning

confidence: 99%

A Kinetic Model of Trp-Cage Folding from Multiple Biased Molecular Dynamics Simulations

et al. 2009

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“…ddcMD [35,48] is a highly optimized molecular dynamics application that has twice won the Gordon Bell prize for high performance computing [48,90]. It is written in C and uses the MPI library for interprocess communication.…”

Section: Ddcmdmentioning

confidence: 99%

Load Balancing Scientific Applications

Pearce

2014

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The largest supercomputers have millions of independent processors, and concurrency levels are rapidly increasing. For ideal efficiency, developers of the simulations that run on these machines must ensure that computational work is evenly balanced among processors. Assigning work evenly is challenging because many large modern parallel codes simulate behavior of physical systems that evolve over time, and their workloads change over time. Furthermore, the cost of imbalanced load increases with scale because most large-scale scientific simulations today use a Single Program Multiple Data (SPMD) parallel programming model, and an increasing number of processors will wait for the slowest one at the synchronization points. To address load imbalance, many large-scale parallel applications use dynamic load balance algorithms to redistribute work evenly. The research objective of this dissertation is to develop methods to decide when and how to load balance the application, and to balance it effectively and affordably. We measure and evaluate the computational load of the application, and develop strategies to decide when and how to correct the imbalance. Depending on the simulation, a fast, local load balance algorithm may be suitable, or a more sophisticated and expensive algorithm may be required. We developed a model for comparison of load balance algorithms for a specific state of the simulation that enables the selection of a balancing algorithm that will minimize overall runtime. Dynamic load balancing of parallel applications becomes more critical at scale, while also being expensive. To make the load balance correction affordable at scale, we propose a lazy load balancing strategy that evaluates the imbalance and computes the new assignment of work to processes asynchronously to the main application computation. We decouple the load balance algorithm from the application and run it on potentially fewer, separate I would like to thank the students and faculty of Western Oregon University.

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Application Modernization at LLNL and the Sierra Center of Excellence

Neely

Supinski

2017

Comput. Sci. Eng.

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BlueGene/L applications: Parallelism On a Massive Scale

Cited by 4 publications

References 23 publications

A Kinetic Model of Trp-Cage Folding from Multiple Biased Molecular Dynamics Simulations

A Kinetic Model of Trp-Cage Folding from Multiple Biased Molecular Dynamics Simulations

Load Balancing Scientific Applications

Application Modernization at LLNL and the Sierra Center of Excellence

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