OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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$LATEX for Philosophers - University of Calgary$

List of Tables 2.1 Load and Store Bytecodes . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Primitive Data Types and Their Abbreviations . . . . . . . . . . . . 12 2.3 Constant Loading Bytecodes . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Array Access Bytecodes . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.5 Stack Manipulation Bytecodes . . . . . . . . . . . . . . . . . . . . . . 15 2.6 Arithmetic Operation Bytecodes . . . . . . . . . . . . . . . . . . . . . 16 2.7 Numeric Casting Bytecodes . . . . . . . . . . . . . . . . . . . . . . . 16 2.8 Branch Condition Abbreviations . . . . . . . . . . . . . . . . . . . . . 17 2.9 Branching Bytecodes . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.10 Comparison Bytecodes . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.11 Miscellaneous Bytecodes . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1 Bytecode Idiom Examples . . . . . . . . . . . . . . . . . . . . . . . . 33 4.1 Specialized Load Bytecodes . . . . . . . . . . . . . . . . . . . . . . . 46 4.2 Specialized Store Bytecodes and their General Purpose Forms . . . . 48 4.3 Specialized Branch Bytecodes and their General Purpose Forms . . . 54 4.4 Other Specialized Bytecodes and their General Purpose Forms . . . . 58 4.5 All Specialized Bytecodes and their General Forms . . . . . . . . . . 59 4.6 Despecialization Conditions Tested . . . . . . . . . . . . . . . . . . . 60 4.7 Kaffe Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.8 RVM Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.9 Sun Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.10 Despecialization’s Impact on Class File Size . . . . . . . . . . . . . . 71 5.1 Average Execution Frequency for 62 Specialized Bytecodes Across All Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.2 Configurations Tested . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.3 Category Summary by Virtual Machine . . . . . . . . . . . . . . . . . 92 xvi
6.1 Most Frequently Occurring Constant Values . . . . . . . . . . . . . . 122 7.1 Bytecode Sequences Example . . . . . . . . . . . . . . . . . . . . . . 141 7.2 Bytecode Sequences Example after One Multicode Substitution . . . 143 7.3 Maximum Multicode Block Length for each Benchmark . . . . . . . . 154 7.4 Multicode Performance Results Summary . . . . . . . . . . . . . . . . 160 7.5 Bytecode Patterns Utilized for Optimization . . . . . . . . . . . . . . 176 8.1 Bytecodes Selected for Despecialization . . . . . . . . . . . . . . . . . 192 A.1 Multicodes of Length 35 or Less using Transfer Reduction Scoring for 201 compress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 A.2 Multicodes of Length 20 or Less using Transfer Reduction Scoring for 202 jess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 A.3 Multicodes of Length 45 or Less using Transfer Reduction Scoring for 209 db . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 A.4 Multicodes of Length 10 or Less using Maximum Number of Bytecodes Replaced Scoring for 213 javac . . . . . . . . . . . . . . . . . . . . . 249 A.5 Multicodes of Length 25 or Less using Transfer Reduction Scoring for 222 mpegaudio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 A.6 Multicodes of Length 5 or Less using Transfer Reduction Scoring for 228 jack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 A.7 Multicodes of Length 25 or Less using Transfer Reduction Scoring for All Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 xvii
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 and 14: 7.11 201 compress Performance by Mu
Page 15: B.1 The Steps Followed to Evaluate
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
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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
Page 201 and 202:
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
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197 performing multicode substituti
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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
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Figure 9.3: The Impact of Algorithm
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207 Algorithm NonOverlappingScore(n
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209 Algorithm CountUniqueOccurrence
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211 this algorithm did not consider
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213 exceptions if the value being c
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215 Figure 10.1: Multicode Blocks u
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217 approximately 30 percent of the
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219 a strategy other than direct th
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221 10.2.6 Combining Multicodes and
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223 10.2.9 Expanding the Class File
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225 revealed that none of the const
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227 based on these profile results
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229 stitution were performed. It co
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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
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239 [100] L. A. Smith, J. M. Bull,
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241 [120] T. A. Welch. A technique
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243 # Len Score Multicode % 1 30 10
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Page 265 and 266:
Page 267 and 268:
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253 Table A.5 continued: # Len Scor
Page 273 and 274:
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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
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265 to emit four files that were su
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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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