OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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244 Table A.1 continued: # Len Score Multicode % 26 2 5.3 iload 2 invokevirtual 27 2 4.9 iload ifgt 28 7 4.6 astore aload getfield iload 2 iload 1 iastore goto 29 4 4.6 iload iinc baload invokevirtual 30 2 3.4 iload bastore 31 3 3.4 aload 0 dup getfield 32 3 3.2 iload 3 iload 2 if icmplt 33 5 3.2 istore 3 iconst 0 istore iload 1 iflt 34 3 3.0 iinc iload 3 ifne 35 10 2.8 iload baload getstatic iload 3 baload iand iload 2 ishl ior istore 1 36 2 2.6 istore goto 37 2 2.6 iconst 0 ifeq 38 4 2.6 iload 1 iconst m1 iastore iinc 39 4 2.4 iadd putfield iload 1 ireturn 40 4 2.4 dup istore 2 iconst m1 if icmpgt 41 3 1.6 iconst 3 ishl if icmpne 42 3 1.6 aload 0 iload invokespecial 43 3 1.6 iload 2 sipush if icmpne 44 3 1.6 iload 2 istore iload 2 45 2 1.6 iload iflt 46 3 1.5 iload 2 iload 3 invokevirtual 47 2 1.5 aload 1 iload 3 48 2 1.5 baload bastore 49 4 0.9 iload iload 1 i2b bastore 50 2 0.9 iadd putfield
245 # Len Score Multicode % 1 9 100.0 aload 0 getfield iload 3 aaload aload 2 getfield iload 3 aaload invokevirtual 2 4 63.1 iload 3 aload 0 getfield if icmplt 3 8 59.5 aload 0 getfield iload 3 aaload aload 2 iload 3 aaload invokevirtual 4 3 42.9 aload 0 aload 1 if acmpne 5 8 32.4 aload 1 checkcast astore 2 aload 0 getfield aload 2 getfield if icmpeq 6 7 26.9 aload 0 getfield istore 2 aload 1 getfield iload 2 if icmpeq 7 5 24.0 aload 0 getfield iload 1 aaload areturn 8 6 20.8 aload 1 getfield astore 2 iconst 0 istore 3 goto 9 6 20.8 astore 3 aload 2 getfield aload 3 getfield if icmpne 10 5 17.1 aload 0 getfield aload 1 getfield if icmpeq 11 4 13.5 iload 2 ldc iand ifeq 12 2 13.4 iconst 0 ireturn 13 2 12.9 iconst 1 ireturn 14 3 9.0 iload iload if icmplt 15 3 8.5 aload 1 iload invokevirtual 16 3 8.5 aload 2 aload 3 invokevirtual 17 3 8.4 iconst 0 istore 3 goto 18 2 8.4 aload 0 getfield 19 8 5.4 aload getfield istore aload getfield istore aload getfield 20 12 2.8 iload aaload checkcast astore aload 1 getfield aload getfield aaload astore aload ifnull 21 20 2.1 aload 0 aload 1 getfield iconst 3 iadd anewarray putfield aload 0 aload 1 getfield putfield aload 0 iconst 0 putfield aload 0 aload 1 getfield putfield aload 1 getfield 22 9 1.9 istore 2 iload 2 ldc iand istore 2 iload 2 iload 1 irem ireturn 23 7 1.8 aload 0 dup getfield dup x1 iconst 1 iadd putfield 24 3 1.6 istore aload getfield 25 2 1.2 iload invokevirtual Table A.2: Multicodes of Length 20 or Less using Transfer Reduction Scoring for 202 jess
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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
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
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: 243 # Len Score Multicode % 1 30 10
Page 265 and 266: 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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