OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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248 Table A.3 continued: # Len Score Multicode % 26 3 2.1 iload 3 aaload astore 27 4 1.5 aload 1 getfield isub ireturn 28 6 1.3 aload 0 iload 3 iconst 1 iadd putfield iload 2 29 2 0.8 aload 0 invokespecial 30 4 0.8 aload 0 aload 1 aload 0 invokevirtual 31 3 0.7 iload 1 bipush if icmpne 32 9 0.7 aload 0 aload 1 putfield aload 0 iconst 0 putfield aload 0 aload 1 invokespecial 33 2 0.6 iload 0 goto 34 3 0.6 iload iload 3 if icmplt 35 4 0.6 iload 1 castore aload 0 areturn 36 8 0.4 invokestatic lstore lload lload 1 lsub lstore lload invokestatic 37 7 0.4 aload iload 3 caload aload iload caload if icmpeq 38 4 0.4 istore 1 iload 1 bipush if icmpeq 39 7 0.4 getstatic lload ladd putstatic aload 0 iconst 1 invokespecial 40 5 0.4 new dup aload 0 dup ifnonnull 41 2 0.4 getfield if icmplt 42 7 0.4 aload 0 aload 1 iload 2 iload iadd baload invokevirtual 43 4 0.4 dup istore 3 bipush if icmpeq 44 4 0.3 iload 1 iinc aload 3 invokeinterface 45 4 0.3 aload 1 iload 3 i2c invokevirtual 46 2 0.3 iconst 1 iadd 47 2 0.3 aload 1 ifnonnull 48 2 0.3 iload 2 ifge 49 5 0.3 getstatic lload 0 ladd putstatic return 50 5 0.3 getstatic iload 1 iadd putstatic return
249 # Len Score Multicode % 1 2 100.0 aload 0 getfield 2 7 21.4 aload 0 dup getfield dup x1 iconst 1 iadd putfield 3 7 13.9 aload iload 3 caload aload iload caload if icmpeq 4 5 12.1 iload 3 caload iadd istore 1 iinc 5 5 11.1 aload 0 dup getfield iconst 1 iadd 6 7 9.6 iload iadd caload istore iload iload 1 if icmpne 7 5 8.1 iconst 0 baload sipush iand ireturn 8 2 7.9 aload 0 aload 1 9 3 7.8 iload 3 iload 2 if icmplt 10 3 7.3 bipush iload 1 imul 11 4 6.5 baload bastore iconst 1 ireturn 12 3 6.4 iload 3 iload if icmplt 13 2 6.3 iconst m1 if icmpne 14 9 5.9 istore 2 iload 2 ldc iand istore 2 iload 2 iload 1 irem ireturn 15 2 5.5 aload 0 invokevirtual 16 4 5.1 aload 3 iload iload castore 17 3 4.9 iload 3 iconst 1 if icmpne 18 3 4.9 iconst 0 iconst 1 invokevirtual 19 7 4.0 aload 1 iload caload istore iload iconst 1 if icmplt 20 2 4.0 iinc iinc 21 2 4.0 aload 0 iload 1 22 2 4.0 aload 1 invokevirtual 23 2 3.9 aload 1 iload 2 24 2 3.7 aload 3 getfield 25 2 3.6 istore 1 iload 1 Table A.4: Multicodes of Length 10 or Less using Maximum Number of Bytecodes Replaced Scoring for 213 javac
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OPTIMIZING THE JAVA VIRTUAL MACHINE
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Abstract Since its public introduct
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Acknowledgments While my name is th
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2.4.4 Direct Threading . . . . . .
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7.2 An Overview of Multicodes . . .
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List of Figures 2.1 Core Virtual Ma
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7.11 201 compress Performance by Mu
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B.1 The Steps Followed to Evaluate
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6.1 Most Frequently Occurring Const
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1 Chapter 1 Introduction When Moore
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3 Studies were conducted that consi
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5 Chapter 2 An Overview of Java Thi
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7 Addressing this need led to the d
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9 performs garbage collection, a cl
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11 also has the ability to contain
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13 ... i − 1 iload 0x04 dstore 0x
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15 ... aload 3 iconst 2 faload ...
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17 Condition Equal Unequal Greater
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19 transfered to the start of a new
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21 piled into machine language inst
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23 Figure 2.6: Interpreter Engine C
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25 Figure 2.8: Interpreter Engine C
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27 The following section examines t
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29 execute (JMethod * method, Slot
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31 codelets shown in this example a
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33 3.1 Instruction Set Design The a
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35 is replaced with a new opcode if
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37 method. However, the Java Virtua
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39 3.2.5 Bytecode Optimization Tool
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41 each bytecode executed as was ac
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43 executing the application. The a
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45 Chapter 4 Despecialization The J
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47 ... i − 1 iload 3 iload 0x04 i
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49 purpose bytecodes. The notation,
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51 is present in the constant pool
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53 ... iload 3 iflt ... i − 1 i i
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55 the stack challenging in some ca
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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.
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63 Test Total Weighted Condition Pe
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69 4.2.6 Performance Summary Compar
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71 Category Original Desp. Percent
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73 the maximum value encountered af
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75 • Efficient support of new lan
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77 difference in the performance of
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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
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Figure 5.1: Average Performance Res
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87 Figure 5.4: Performance Results
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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
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99 Within this category, it was als
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101 machine code for the idioms. As
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103 were performed was less than 0.
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105 these despecializations in a me
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107 Chapter 6 Operand Profiling Cha
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109 the applications. In addition,
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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
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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
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129 Figure 6.8: Data types Accessed
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131 lar, the new set of specialized
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133 multicode block is defined to b
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135 Figure 7.1: Two Bytecodes and t
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137 because of the presence of the
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139 cache performance. As a result,
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141 Block 1: Block 2: aload 0 → g
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143 Sequence Count Score getfield a
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Figure 7.3: Iterative Multicode Ide
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147 number of bytecodes that will b
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149 BlockList: A list of the byteco
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151 Both the Total Bytecodes Score
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153 ... i − 1 ❄ ifeq 0x00 0x15
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155 Figure 7.7: Number of Unique Ca
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Figure 7.9: Cumulative Multicode Bl
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159 However, while considering long
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161 Figure 7.10: 201 compress Perfo
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163 Figure 7.14: Length 209 db Perf
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165 Figure 7.18: 222 mpegaudio Perf
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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
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175 Such bytecodes have a variable
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177 ∆ x1 /x 2 /x 3 /.../x n→ an
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179 Popping any values left on the
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181 7.9 Conclusion This chapter has
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183 ated will differ from that occu
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185 Figure 8.1: A Comparison of the
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187 performed impacts the sequences
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189 Figure 8.2: 201 compress Perfor
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191 Figure 8.5: 213 javac Performan
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193 Compared to the complete despec
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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
Page 219 and 220: 201 alphabet employed by each of th
Page 221 and 222: 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 265: 247 # Len Score Multicode % 1 19 10
Page 271 and 272: 253 Table A.5 continued: # Len Scor
Page 277 and 278: 259 the level of entropy within the
Page 279 and 280: 261 there are no pointers to these
Page 281 and 282: 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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