Lecture Notes in Computational Science and Engineering - Bioserver

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X Contents Numerical Solution of Optimal Control Problems for Parabolic Systems Peter Benner, Sabine Görner, Jens Saak ............................151 Part III Applications Parallel Simulations of Phase Transitions in Disordered Many-Particle Systems Thomas Vojta ...................................................173 Localization of Electronic States in Amorphous Materials: Recursive Green’s Function Method and the Metal-Insulator Transition at E �=0 Alexander Croy, Rudolf A. Römer, Michael Schreiber .................203 Optimizing Simulated Annealing Schedules for Amorphous Carbons Peter Blaudeck, Karl Heinz Hoffmann ..............................227 Amorphisation at Heterophase Interfaces Sibylle Gemming, Andrey Enyashin, Michael Schreiber ................235 Energy-Level and Wave-Function Statistics in the Anderson Model of Localization Bernhard Mehlig, Michael Schreiber ................................255 Fine Structure of the Integrated Density of States for Bernoulli–Anderson Models Peter Karmann, Rudolf A. Römer, Michael Schreiber, Peter Stollmann .................................................267 Modelling Aging Experiments in Spin Glasses Karl Heinz Hoffmann, Andreas Fischer, Sven Schubert, Thomas Streibert.................................................281 Random Walks on Fractals Astrid Franz, Christian Schulzky, Do Hoang Ngoc Anh, Steffen Seeger, Janett Balg, Karl Heinz Hoffmann .................................303 Lyapunov Instabilities of Extended Systems Hong-liu Yang, Günter Radons ....................................315 The Cumulant Method for Gas Dynamics Steffen Seeger, Karl Heinz Hoffmann, Arnd Meyer ...................335 Index ..........................................................361
Parallel Programming Models for Irregular Algorithms Gudula Rünger Technische Universität Chemnitz, Fakultät für Informatik 09107 Chemnitz, Germany ruenger@informatik.tu-chemnitz.de Applications from science and engineering disciplines make extensive use of computer simulations and the steady increase in size and detail leads to growing computational costs. Computational resources can be provided by modern parallel hardware platforms which nowadays are usually cluster systems. Effective exploitation of cluster systems requires load balancing and locality of reference in order to avoid extensive communication. But new sophisticated modeling techniques lead to application algorithms with varying computational effort in space and time, which may be input dependent or may evolve with the computation itself. Such applications are called irregular. Because of the characteristics of irregular algorithms, efficient parallel implementations are difficult to achieve since the distribution of work and data cannot be determined a priori. However, suitable parallel programming models and libraries for structuring, scheduling, load balancing, coordination, and communication can support the design of efficient and scalable parallel implementations. 1 Challenges for parallel irregular algorithms Important issues for gaining efficient and scalable parallel programs are load balancing and communication. On parallel platforms with distributed memory and clusters, load balancing means spreading the calculations evenly across processors while minimizing communication. For algorithms with regular computational load known at compile time, load balancing can be achieved by suitable data distributions or mappings of task to processors. For irregular algorithms, static load balancing becomes more difficult because of dynamically changing computation load and data load. The appropriate load balancing technique for regular and irregular algorithms depends on the specific algorithmic properties concerning the behavior of data and task:
Page 1 and 2: Lecture Notes in Computational Scie
Page 3 and 4: Editors Karl Heinz Hoffmann Institu
Page 5 and 6: Preface High performance computing
Page 7: Contents Part I Implementions Paral
Page 11 and 12: Parallel Programming Models for Irr
Page 13 and 14: surface element j Parallel Programm
Page 15 and 16: quadtree of polygon A Parallel Prog
Page 27 and 28: References Parallel Programming Mod
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Page 32 and 33: 26 Arnd Meyer 2 Finite element comp
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Page 43 and 44: A Performance Analysis of ABINIT on
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54 Matthias Pester and obvious fiel
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56 Matthias Pester • The program
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58 Matthias Pester SFB 393 - TU Che
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60 Matthias Pester SFB 393 - TU Che
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62 Matthias Pester or time-dependen
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64 Matthias Pester 5. G. Haase, T.
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68 Arnd Meyer TL = {T ⊂ Ω},T ar
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70 Arnd Meyer 3 Basic facts on hier
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72 Arnd Meyer Φl = ΦLQl ∀l =0,.
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74 Arnd Meyer Example 5: VL = V (3,
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76 Arnd Meyer (c) piecewise quadrat
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78 Arnd Meyer 6 Crack growth Table
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80 Arnd Meyer can access from its e
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82 Arnd Meyer where and This leads
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84 Arnd Meyer 10 A numerical exampl
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Nitsche Finite Element Method for E
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approximate solution u h pointwise
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Hierarchical Adaptive FEM at Finite
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130 Helmut Harbrecht et al. scheme,
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132 Helmut Harbrecht et al. We emph
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134 Helmut Harbrecht et al. piecewi
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136 Helmut Harbrecht et al. Herein,
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138 Helmut Harbrecht et al. Fig. 3.
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140 Helmut Harbrecht et al. Γi,j,k
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142 Helmut Harbrecht et al. This pr
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144 Helmut Harbrecht et al. unknown
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146 Helmut Harbrecht et al. 200 400
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148 Helmut Harbrecht et al. 20. W.
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Numerical Solution of Optimal Contr
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max. deviation of temperature in [
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174 Thomas Vojta disorder. Examples
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176 Thomas Vojta thermal ones. Exam
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178 Thomas Vojta 3 Classical Ising
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180 Thomas Vojta Fig. 1. Average ma
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182 Thomas Vojta Fig. 3. Local magn
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184 Thomas Vojta J ⊥ / J || 2.5 2
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186 Thomas Vojta Fig. 5. Upperpanel
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188 Thomas Vojta criterion. These r
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190 Thomas Vojta ways to actually i
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192 Thomas Vojta ρ 10 0 10 -2 10 -
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194 Thomas Vojta ln(ρ st ) -4 -6 -
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196 Thomas Vojta Fortunately, this
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198 Thomas Vojta Acknowledgements T
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200 Thomas Vojta 39. Motrunich, O.,
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Localization of Electronic States i
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〈ln g 4 〉 0 -0.5 -1 -1.5 -2 -2.
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ρ(E) 0.062 0.06 0.058 0.056 0.054
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ρ(E) 0.14 0.12 0.1 0.08 0.06 0.04
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ρ(E) 0.06 0.05 0.04 0.03 0.02 0.01
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| ξ | 100 10 1 Localization of Ele
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Optimizing Simulated Annealing Sche
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Amorphisation at Heterophase Interf
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[010] Amorphisation at Heterophase
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4.2 Stability at low temperature Am
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RDF 28 26 24 22 20 18 16 14 12 10 8
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Energy-Level and Wave-Function Stat
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3 Summary of theoretical expectatio
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˙ ¸ 2 |ψ(r)| V t 10 1 10 1 Energ
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Fine Structure of the Integrated De
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Fine Structure of the Integrated DO
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DOS Fine Structure of the Integrate
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ρ min , ρ max Fine Structure of t
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Modelling Aging Experiments in Spin
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2.1 ZFC-experiments Modelling Aging
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density 10 3 10 2 10 1 10 0 -2 -1 0
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Magnetization M(t) Modelling Aging
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TRM (a.u.) Modelling Aging Experime
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Random Walks on Fractals Astrid Fra
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Random Walks on Fractals 305 The di
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log��r 18 2�� 16 14 12 10 8
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(a) 10 3 10 1 10 2 〈r 2 〉 10 0
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Random Walks on Fractals 311 Fig. 7
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Random Walks on Fractals 313 11. A.
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316 Hong-liu Yang and Günter Radon
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The Cumulant Method for Gas Dynamic
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0.2 0.1 0.0 0.2 0.1 0.0 0 -5 The Cu
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n = eC0 � x C v = Cy � σ = 1 2
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(a) gas wall wall The Cumulant Meth
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Index ab-initio calculation 37 abin
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Editorial Policy 1. Volumes in the
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Lecture Notes in Computational Scie
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Vol. 39 S. Attinger, P. Koumoutsako
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