OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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8.1 A Comparison of the Cumulative Score for the Top n Multicodes Identified Before and After Despecialization . . . . . . . . . . . . . . . . . 185 8.2 201 compress Performance by Scoring Technique and Maximum Multicode Length with 67 Despecializations . . . . . . . . . . . . . . . . . 189 8.3 202 jess Performance by Scoring Technique and Maximum Multicode Length with 67 Despecializations . . . . . . . . . . . . . . . . . 190 8.4 209 db Performance by Scoring Technique and Maximum Multicode Length with 67 Despecializations . . . . . . . . . . . . . . . . . . . . 190 8.5 213 javac Performance by Scoring Technique and Maximum Multicode Length with 67 Despecializations . . . . . . . . . . . . . . . . . 191 8.6 222 mpegaudio Performance by Scoring Technique and Maximum Multicode Length with 67 Despecializations . . . . . . . . . . . . . . 191 8.7 228 jack Performance by Scoring Technique and Maximum Multicode Length with 67 Despecializations . . . . . . . . . . . . . . . . . 192 8.8 201 compress Performance by Scoring Technique and Maximum Multicode Length with 50 Despecializations . . . . . . . . . . . . . . . . . 194 8.9 202 jess Performance by Scoring Technique and Maximum Multicode Length with 50 Despecializations . . . . . . . . . . . . . . . . . 194 8.10 209 db Performance by Scoring Technique and Maximum Multicode Length with 50 Despecializations . . . . . . . . . . . . . . . . . . . . 195 8.11 213 javac Performance by Scoring Technique and Maximum Multicode Length with 50 Despecializations . . . . . . . . . . . . . . . . . 195 8.12 222 mpegaudio Performance by Scoring Technique and Maximum Multicode Length with 50 Despecializations . . . . . . . . . . . . . . 196 8.13 228 jack Performance by Scoring Technique and Maximum Multicode Length with 50 Despecializations . . . . . . . . . . . . . . . . . 196 9.1 Suffix Tree for the string ababab$abab# . . . . . . . . . . . . . . . . . 202 9.2 Algorithm SplitLeaves . . . . . . . . . . . . . . . . . . . . . . . . . . 204 9.3 The Impact of Algorithm SplitLeaves . . . . . . . . . . . . . . . . . . 205 9.4 Suffix Tree for the string ababab$abab# Including Scores . . . . . . . 206 9.5 Algorithm NonOverlappingScore . . . . . . . . . . . . . . . . . . . . . 207 9.6 Algorithm CountUniqueOccurrences . . . . . . . . . . . . . . . . . . . 209 9.7 Suffix tree for the string ababab$abab# Including Score Computed Using Only Non-Overlapping Occurrences . . . . . . . . . . . . . . . . . 210 10.1 Multicode Blocks under the Original and Proposed Future Definition 215 xiv
B.1 The Steps Followed to Evaluate Despecialization Performance . . . . 267 B.2 The Steps Followed to Evaluate Multicode Performance . . . . . . . . 268 xv
Page 1 and 2: OPTIMIZING THE JAVA VIRTUAL MACHINE
Page 3 and 4: Abstract Since its public introduct
Page 5 and 6: Acknowledgments While my name is th
Page 7 and 8: 2.4.4 Direct Threading . . . . . .
Page 9 and 10: 7.2 An Overview of Multicodes . . .
Page 11 and 12: List of Figures 2.1 Core Virtual Ma
Page 13: 7.11 201 compress Performance by Mu
Page 17 and 18: 6.1 Most Frequently Occurring Const
Page 19 and 20: 1 Chapter 1 Introduction When Moore
Page 21 and 22: 3 Studies were conducted that consi
Page 23 and 24: 5 Chapter 2 An Overview of Java Thi
Page 25 and 26: 7 Addressing this need led to the d
Page 27 and 28: 9 performs garbage collection, a cl
Page 29 and 30: 11 also has the ability to contain
Page 31 and 32: 13 ... i − 1 iload 0x04 dstore 0x
Page 33 and 34: 15 ... aload 3 iconst 2 faload ...
Page 35 and 36: 17 Condition Equal Unequal Greater
Page 37 and 38: 19 transfered to the start of a new
Page 39 and 40: 21 piled into machine language inst
Page 41 and 42: 23 Figure 2.6: Interpreter Engine C
Page 43 and 44: 25 Figure 2.8: Interpreter Engine C
Page 45 and 46: 27 The following section examines t
Page 47 and 48: 29 execute (JMethod * method, Slot
Page 49 and 50: 31 codelets shown in this example a
Page 51 and 52: 33 3.1 Instruction Set Design The a
Page 53 and 54: 35 is replaced with a new opcode if
Page 55 and 56: 37 method. However, the Java Virtua
Page 57 and 58: 39 3.2.5 Bytecode Optimization Tool
Page 59 and 60: 41 each bytecode executed as was ac
Page 61 and 62: 43 executing the application. The a
Page 63 and 64: 45 Chapter 4 Despecialization The J
Page 65 and 66:
47 ... i − 1 iload 3 iload 0x04 i
Page 67 and 68:
49 purpose bytecodes. The notation,
Page 69 and 70:
51 is present in the constant pool
Page 71 and 72:
53 ... iload 3 iflt ... i − 1 i i
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55 the stack challenging in some ca
Page 75 and 76:
Figure 4.7: Despecialization of Bra
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59 Specialized Bytecode load store
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61 tual machine (RVM). Version 2.3.
Page 81 and 82:
63 Test Total Weighted Condition Pe
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
69 4.2.6 Performance Summary Compar
Page 89 and 90:
71 Category Original Desp. Percent
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73 the maximum value encountered af
Page 93 and 94:
75 • Efficient support of new lan
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77 difference in the performance of
Page 97 and 98:
79 JGF RayTracer: A 150 by 150 pixe
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81 # Average % of Specialized Despe
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83 provided by only two vendors. IB
Page 103 and 104:
Figure 5.1: Average Performance Res
Page 105 and 106:
87 Figure 5.4: Performance Results
Page 107 and 108:
89 Figure 5.8: Performance Results
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91 ences in background processes, m
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93 difference in performance due to
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95 when a pure interpreter virtual
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97 It is observed that many of the
Page 117 and 118:
99 Within this category, it was als
Page 119 and 120:
101 machine code for the idioms. As
Page 121 and 122:
103 were performed was less than 0.
Page 123 and 124:
105 these despecializations in a me
Page 125 and 126:
107 Chapter 6 Operand Profiling Cha
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109 the applications. In addition,
Page 129 and 130:
111 The aload 0 bytecode was the mo
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113 function being integrated. Beca
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115 Implementing alignment rules un
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117 these data types in the list of
Page 137 and 138:
Figure 6.3: Distribution of Local V
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121 are never executed by any of th
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123 load and store bytecodes. It is
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125 the time because the index bein
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127 6.2.5 Fields Only four bytecode
Page 147 and 148:
129 Figure 6.8: Data types Accessed
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131 lar, the new set of specialized
Page 151 and 152:
133 multicode block is defined to b
Page 153 and 154:
135 Figure 7.1: Two Bytecodes and t
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137 because of the presence of the
Page 157 and 158:
139 cache performance. As a result,
Page 159 and 160:
141 Block 1: Block 2: aload 0 → g
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143 Sequence Count Score getfield a
Page 163 and 164:
Figure 7.3: Iterative Multicode Ide
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147 number of bytecodes that will b
Page 167 and 168:
149 BlockList: A list of the byteco
Page 169 and 170:
151 Both the Total Bytecodes Score
Page 171 and 172:
153 ... i − 1 ❄ ifeq 0x00 0x15
Page 173 and 174:
155 Figure 7.7: Number of Unique Ca
Page 175 and 176:
Figure 7.9: Cumulative Multicode Bl
Page 177 and 178:
159 However, while considering long
Page 179 and 180:
161 Figure 7.10: 201 compress Perfo
Page 181 and 182:
163 Figure 7.14: Length 209 db Perf
Page 183 and 184:
165 Figure 7.18: 222 mpegaudio Perf
Page 185 and 186:
167 7.6.2 Overall Performance Acros
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169 a larger portion of the instruc
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171 // iload_1: stack[sp+1] = local
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173 // iload_1: // iconst_1: // iad
Page 193 and 194:
175 Such bytecodes have a variable
Page 195 and 196:
177 ∆ x1 /x 2 /x 3 /.../x n→ an
Page 197 and 198:
179 Popping any values left on the
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181 7.9 Conclusion This chapter has
Page 201 and 202:
183 ated will differ from that occu
Page 203 and 204:
185 Figure 8.1: A Comparison of the
Page 205 and 206:
187 performed impacts the sequences
Page 207 and 208:
189 Figure 8.2: 201 compress Perfor
Page 209 and 210:
191 Figure 8.5: 213 javac Performan
Page 211 and 212:
193 Compared to the complete despec
Page 213 and 214:
195 Figure 8.10: 209 db Performance
Page 215 and 216:
197 performing multicode substituti
Page 217 and 218:
199 Chapter 9 An Efficient Multicod
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201 alphabet employed by each of th
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9.4 An Algorithm for the Most Contr
Page 223 and 224:
Figure 9.3: The Impact of Algorithm
Page 225 and 226:
207 Algorithm NonOverlappingScore(n
Page 227 and 228:
209 Algorithm CountUniqueOccurrence
Page 229 and 230:
211 this algorithm did not consider
Page 231 and 232:
213 exceptions if the value being c
Page 233 and 234:
215 Figure 10.1: Multicode Blocks u
Page 235 and 236:
217 approximately 30 percent of the
Page 237 and 238:
219 a strategy other than direct th
Page 239 and 240:
221 10.2.6 Combining Multicodes and
Page 241 and 242:
223 10.2.9 Expanding the Class File
Page 243 and 244:
225 revealed that none of the const
Page 245 and 246:
227 based on these profile results
Page 247 and 248:
229 stitution were performed. It co
Page 249 and 250:
231 [13] B. Alpern, C. R. Attanasio
Page 251 and 252:
233 [33] K. Casey, D. Gregg, M. A.
Page 253 and 254:
235 [55] D. Gregg and J. Waldron. P
Page 255 and 256:
237 [78] E. M. McCreight. A space-e
Page 257 and 258:
239 [100] L. A. Smith, J. M. Bull,
Page 259 and 260:
241 [120] T. A. Welch. A technique
Page 261 and 262:
243 # Len Score Multicode % 1 30 10
Page 263 and 264:
Page 265 and 266:
Page 267 and 268:
Page 269 and 270:
Page 271 and 272:
253 Table A.5 continued: # Len Scor
Page 273 and 274:
Page 275 and 276:
Page 277 and 278:
259 the level of entropy within the
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261 there are no pointers to these
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263 information for each method so
Page 283 and 284:
265 to emit four files that were su
Page 285 and 286:
Figure B.1: The Steps Followed to E
Page 287 and 288:
269 B.7 Summary This Chapter has de
Page 289 and 290:
271 Selected Honors and Awards Cont
Page 291:
273 Departmental and University Pre
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