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584 appendix cformat = "general"inquiry = "position gridcounts"dimension = "y"(a.1–1)/2shape = [400 25]opacity = 0.3type = "spiffy"shape = 50scale = 1.0Figure C.15 The visual program at the top plots two equipotential surfaces in yellow andgreen, as well as the electric field on a y + z plane going through the tori. The blue and redsurfaces are the charged tori. The image on the far right is a close-up of the middle image.−101COPYRIGHT 2008, PRINCET O N UNIVE R S I T Y P R E S SEVALUATION COPY ONLY. NOT FOR USE IN COURSES.ALLpup_06.04 — 2008/2/15 — Page 584
industrial-strength data visualization 585format = “general”color = “orange”direction = "off–top"Figure C.16 The visual program on the left produces the surface plot of constant probabilitydensity for the 3-D state on the right.plane is created by the module Slab. In addition, an equipotential surface surroundingeach torus is plotted, with the surfaces made semitransparent in order to viewtheir relation to the electric field. The bending of the arrows is evident in the closeupon the far right.C.5.6 Sample 6: 3-D Functions, the Hydrogen AtomThe electron probability density ρ nlm (r, θ, φ) (calculated by H_atom_wf.java) isascalar function that depends on three spatial variables [Libb 03]. Yet because thephysical interpretation of a density is different from that of a potential, differentvisualization techniques are required. One way of visualizing a density is to draw3-D surfaces of constant density. Because the density has a single fixed value, thesurfaces indicates regions of space that have equal likelihood of having an electronpresent. The visual program H_atom_prob_density.net_1 produced the visualizationin Figure C.16. This visualization does an excellent job of conveying the shapeand rotational symmetry of the state but does not provide information about thevariation of the density throughout space.A modern technique for visualizing densities is volume rendering. This method,which is DX’s default for 3-D scalar functions, represents the density as a translucent−101COPYRIGHT 2008, PRINCET O N UNIVE R S I T Y P R E S SEVALUATION COPY ONLY. NOT FOR USE IN COURSES.ALLpup_06.04 — 2008/2/15 — Page 585
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−101COPYRIGHT 2008, PRINCET O N U
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Copyright © 2008 by Princeton Univ
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viiicontents2.3 Experimental Error
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xcontents6.3 Experimentation 1356.4
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xiicontents9.7.1 Friction: Model an
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xivcontents12.8 Signals of Chaos: L
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xvicontents14.15.2Exercise 2: Cache
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xviiicontents18.4 Waves for Variabl
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xxcontentsC.3 DX Tools Summary 576C
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xxivprefacewhich includes understan
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2 chapter 1PhysicsCPFigure 1.1 A re
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4 chapter 1Scientific LibrariesPerf
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6 chapter 1C DWe have tried to make
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8 chapter 1possibly when installing
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10 chapter 11.4.1 Structured Progra
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12 chapter 17. Revise Area.java so
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14 chapter 1argv). Because main met
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16 chapter 1Package Class Tree Depr
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18 chapter 1Memory and storage size
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20 chapter 1TABLE 1.4The IEEE 754 S
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22 chapter 1gives 24-bit precision
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24 chapter 1TABLE 1.6Representation
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26 chapter 1The computer fetches th
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28 chapter 1Your problem is to use
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2Errors & Uncertainties in Computat
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32 chapter 2purposes, let us consid
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34 chapter 2can be avoided by combi
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36 chapter 2means that a program ru
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38 chapter 2To be more specific, le
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40 chapter 2Let us assume that an a
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42 chapter 2To see if these assumpt
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44 chapter 2computation. Accordingl
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46 chapter 3is regular practice to
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48 chapter 3to plot for x and y, we
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50 chapter 3Sample PtPlot Data file
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52 chapter 3TABLE 3.1Text Files Gra
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54 chapter 3Figure 3.4 Left: A plot
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56 chapter 3TABLE 3.2Grace Menu and
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58 chapter 33.4.1 Gnuplot Input Dat
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60 chapter 3gnuplot> set output "pl
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62 chapter 3gnuplot> set terminal e
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64 chapter 3By setting terminal to
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66 chapter 33.6 Texturing and 3-D I
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68 chapter 4R R 2RL L 2LC C 2CFigur
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70 chapter 44.3 Resistance Becomes
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72 chapter 4be either static or dyn
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74 chapter 4On line 8 we see a meth
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76 chapter 44.4.3 Static and Nonsta
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78 chapter 42. Compile and execute
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80 chapter 41 / Z0.51400R0400R80021
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82 chapter 4no arguments and return
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84 chapter 42. Define a daughter cl
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86 chapter 4new features without
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88 chapter 4qFigure 4.7 Left: The t
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90 chapter 4with properties differi
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92 chapter 4In a project such as th
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94 chapter 4data types called objec
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96 chapter 46. Change the mass of t
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98 chapter 4Check that all the plot
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100 chapter 43. You should see now
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102 chapter 44.9.11 Complex Object
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104 chapter 4✞/ / KomplexTest : t
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106 chapter 4motion in other direct
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108 chapter 4✝protected double y(
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110 chapter 5Mathematically, the li
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112 chapter 5The linear congruent m
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114 chapter 5TABLE 5.1A Table of a
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116 chapter 55.3 Unit II. Monte Car
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118 chapter 5300y0-10-20R2001000 20
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120 chapter 54100,000log[N(t)]10,00
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122 chapter 5✞/ / Decay . java :
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124 chapter 6f(x)a x i x i+1 x i+2
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126 chapter 6f(x)f(x)parabola 1para
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128 chapter 6evaluate the function
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130 chapter 6⇒ N =( ) 2/91 1(ɛ m
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132 chapter 63. [−∞→∞], sca
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134 chapter 6}public static double
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136 chapter 6the integral of f(x)=1
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138 chapter 6f(x)< f(x) >xFigure 6.
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140 chapter 61. Conduct 16 trials a
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142 chapter 6acceptrejectFigure 6.6
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144 chapter 6The crux of this techn
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7Differentiation & SearchingIn this
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148 chapter 7the forward-difference
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150 chapter 7algorithm (7.7) is O(h
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152 chapter 7algorithms in which de
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154 chapter 7we know a zero occurs.
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156 chapter 7Figure 7.3 Two example
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8Solving Systems of Equationswith M
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160 chapter 8the spheres, and the d
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162 chapter 8We now have a solvable
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164 chapter 8of (8.19) by A −1 :x
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166 chapter 8Row MajorColumn Majora
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168 chapter 8having different varia
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170 chapter 8Matrix objects, add an
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172 chapter 8✞/∗ JamaFit : JAMA
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174 chapter 8( ) α β3. Consider t
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176 chapter 8discarding some inform
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178 chapter 8Lagrange interpolation
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180 chapter 8Figure 8.3 Three fits
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182 chapter 8if you have the abilit
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184 chapter 840fitNumber20dataN(t)0
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186 chapter 8theoretical curve went
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188 chapter 8This is a measure of t
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190 chapter 88.7.4 Linear Quadratic
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192 chapter 8Your problem here is t
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9Differential Equation Applications
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196 chapter 9V(x)HarmonicAnharmonic
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198 chapter 9B(t) are solutions of
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200 chapter 9This expresses the acc
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202 chapter 9derivative:dy(t)dt≃
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204 chapter 9As an example of the u
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206 chapter 9C Dhigh-precision work
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208 chapter 9Amplitude Dependence,
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210 chapter 9Here N is the normal f
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212 chapter 99.9 Unit II. Binding A
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214 chapter 9the Schrödinger equat
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216 chapter 99.11.1 Numerov Algorit
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218 chapter 9}}xl = xl0+i∗h;ul[i]
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220 chapter 9public static double d
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222 chapter 99.13 Unit III. Scatter
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224 chapter 9The equations for the
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226 chapter 9Figure 9.9 The traject
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228 chapter 9fFigure 9.10 Left: The
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230 chapter 99.17.1.1 EXPLORATION:
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232 chapter 10y(t)10Y( )10-1t-10 20
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234 chapter 10As seen in the b n co
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236 chapter 1010.4 Fourier Transfor
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238 chapter 10Regardless of the act
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240 chapter 10for the exponential a
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242 chapter 10102 4 6-1Figure 10.2
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244 chapter 10coefficients a k . If
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246 chapter 104. As always, check t
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248 chapter 10A special case of the
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250 chapter 10Figure 10.4 Input sig
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252 chapter 10Figure 10.5 Left: An
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254 chapter 10A 1.5mp1.0lit 0.5ud 0
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256 chapter 1010.8 Unit III. Fast F
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258 chapter 10Figure 10.9 The basic
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260 chapter 10TABLE 10.1Binary-Reve
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262 chapter 10✞/∗ FFT. java : F
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11Wavelet Analysis & Data Compressi
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266 chapter 11the second derivative
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268 chapter 1111.2.1 Wave Packet As
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270 chapter 11localized in time, su
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272 chapter 11201 Y100Signal0-10-10
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274 chapter 11you have been using f
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276 chapter 11}return Math . sin (8
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278 chapter 11FrequencyTimeFigure 1
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280 chapter 11N SamplesInputN/2(1)c
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282 chapter 11Figure 11.10 The filt
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wavelet analysis & data compression
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288 chapter 113. Reproduce the scal
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290 chapter 12discrete decay law,
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292 chapter 120.80.400 10 20Ax nn n
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294 chapter 12b. Repeat the simulat
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296 chapter 121b=1.0b=4.0b=5.0X0-1-
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298 chapter 12TABLE 12.1Several Non
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300 chapter 12✞/ / LyapLog . java
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302 chapter 1212.10 Unit II. Pendul
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304 chapter 12In Chapter 9, “Diff
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306 chapter 12Figure 12.6 The data
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308 chapter 12rotating solutionsθ2
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310 chapter 12θ(t)8040.θ(0)=0.314
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312 chapter 12Figure 12.11 Top row:
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314 chapter 12|θ(t)|200 1 2Figure
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Angular Velocity of Lower Pendulum3
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318 chapter 12some fraction of a ch
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320 chapter 1212.19 Lotka-Volterra
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322 chapter 12numbers that accounts
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324 chapter 12400200pPopulationpP00
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13Fractals & Statistical GrowthIt i
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328 chapter 1330010,000 points20010
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330 chapter 13Figure 13.2 Left: A f
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332 chapter 13copy of a frond, and
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334 chapter 13The results of this t
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336 chapter 13log N(r) = log C −
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338 chapter 13to determine the slop
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340 chapter 13✞☎import java . i
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342 chapter 13Figure 13.8 Number 8
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344 chapter 13Conway in the 1970s,
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346 chapter 13(x 0, y 1)(x 0, y 0)(
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348 chapter 13yxFigure 13.13 After
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350 chapter 13gradient // Vertical
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14High-Performance Computing Hardwa
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354 chapter 14A(1)A(2)A(3)CPUA(N)M(
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356 chapter 14ADBCFigure 14.4 Multi
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358 chapter 14as Fortran or C, tran
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360 chapter 14TABLE 14.2Computation
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362 chapter 14The processors in a p
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364 chapter 14Figure 14.7 Two views
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366 chapter 14Speedup8Amdahl's Lawp
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368 chapter 1414.11 Parallelization
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370 chapter 14The problem affects p
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372 chapter 14• A race condition
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374 chapter 14yet in order to obtai
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376 chapter 14slightly faster or sm
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378 chapter 14You see (Listing 14.1
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380 chapter 14/optimize:4/optimize:
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382 chapter 14• As indicated in
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384 chapter 1410,000Execution Time
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386 chapter 14as high-performance c
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388 chapter 14✞Dimension Vec( idi
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16Simulating Matter withMolecular D
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426 chapter 16+ +Figure 16.1 The mo
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428 chapter 16the molecules stay cl
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430 chapter 162 3 2 3 2 31 4 1 4 1
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432 chapter 16Velocity-Verlet Algor
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434 chapter 16Energy vs Timefor 36
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436 chapter 16Figure 16.7 A simulat
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thermodynamic simulations & feynman
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simulating matter with molecular dy
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17PDEs for Electrostatics & Heat Fl
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pdes for electrostatics & heat flow
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θpde waves: string, quantum packet
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pde waves: string, quantum packet,
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100pde waves: string, quantum packe
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solitons & computational fluid dyna
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✞solitons & computational fluid d
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Page 595 and 596: Appendix A: Glossaryabsolute value
Page 597 and 598: glossary 557control character — A
Page 599 and 600: glossary 559log in (on) — To sign
Page 601 and 602: glossary 561supercomputer — The c
Page 603 and 604: installing ptplot & java developer
Page 609 and 610: industrial-strength data visualizat
Page 623: industrial-strength data visualizat
Page 633 and 634: Appendix D: An MPI TutorialIn this
Page 635 and 636: an mpi tutorial 595• Open a shell
Page 637 and 638: an mpi tutorial 597of all the compu
Page 639 and 640: an mpi tutorial 599TABLE D.1Some Co
Page 641 and 642: an mpi tutorial 601jobs to MPI. 2 A
Page 643 and 644: an mpi tutorial 603✞> cd $HOME Do
Page 645 and 646: an mpi tutorial 605✞/ / MPIhello
Page 647 and 648: an mpi tutorial 607Argument Namemsg
Page 649 and 650: an mpi tutorial 609D.3.4 Broadcast
Page 651 and 652: an mpi tutorial 611D.4 Parallel Tun
Page 653 and 654: an mpi tutorial 613✝}MPI_Recv ( &
Page 655 and 656: an mpi tutorial 615✞/ / Code list
Page 657 and 658: an mpi tutorial 617-1) does not sen
Page 659 and 660: an mpi tutorial 619D.6.1 Nonblockin
Page 661 and 662: an mpi tutorial 621D.9 List of MPI
Page 663 and 664: Appendix E: Calling LAPACK from CCa
Page 665 and 666: calling LAPACK from C 625E.2 Compil
Page 667 and 668: software on the CD 627JavaCodes Con
Page 669 and 670: software on the CD 629JavaCodes Con
Page 671 and 672: software on the CD 631Applets Direc
Page 673 and 674: software on the CD 633Fortran77code
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Appendix G: Compression via DWTwith
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compression via DWT with thresholdi
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compression via DWT with thresholdi
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BIBLIOGRAPHY[Abar 93] Abarbanel, H.
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ibliography 643[Erco] Ercolessi, F.
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ibliography 645[L&F 93] Landau, R.
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ibliography 647[P&W 91] Pinson, L.
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ibliography 649[VdeV 94] van de Vel
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IndexAbstract data structures,70Abs
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index 653drand, 114Driving force, 2
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index 655Libraries, see Subroutines
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index 657structured, 10for virtual
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