OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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4.2.2 Benchmarks Tested . . . . . . . . . . . . . . . . . . . . . . . . 61 4.2.3 Kaffe Performance . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2.4 RVM Performance . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2.5 Sun Performance . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.2.6 Performance Summary . . . . . . . . . . . . . . . . . . . . . . 69 4.3 Despecialization and Class File Size . . . . . . . . . . . . . . . . . . . 69 4.4 Class File Correctness . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 5 Profiling and Despecialization 76 5.1 Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.1.1 Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5.1.2 Profile Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2 Execution Environments . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.2.1 Performance Results . . . . . . . . . . . . . . . . . . . . . . . 83 5.2.2 Category 1: Benchmarks that Show Little Difference In Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.2.3 Category 2: Benchmarks that Show Decreased Performance as a Result of Despecialization . . . . . . . . . . . . . . . . . . . 96 5.2.4 Category 3: Benchmarks with Irregular Performance . . . . . 99 5.2.5 Performance Summary . . . . . . . . . . . . . . . . . . . . . . 102 5.3 Despecialization and Class File Size . . . . . . . . . . . . . . . . . . . 103 5.4 Generality of Despecialization . . . . . . . . . . . . . . . . . . . . . . 105 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6 Operand Profiling 107 6.1 Profiling Environment and Benchmarks . . . . . . . . . . . . . . . . . 107 6.2 Profile Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.2.1 Local Variable Loads . . . . . . . . . . . . . . . . . . . . . . . 109 6.2.2 Local Variable Stores . . . . . . . . . . . . . . . . . . . . . . . 117 6.2.3 Constant Loading . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.2.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 6.2.5 Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 6.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 7 Multicodes 132 7.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 viii
7.2 An Overview of Multicodes . . . . . . . . . . . . . . . . . . . . . . . 133 7.2.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 7.3 Multicode Identification . . . . . . . . . . . . . . . . . . . . . . . . . 138 7.3.1 Intuition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 7.3.2 Static Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . 139 7.3.3 An Initial Approach to Dynamic Profiling . . . . . . . . . . . 140 7.3.4 Reducing the Cost of Dynamic Profiling . . . . . . . . . . . . 144 7.3.5 A Simple Alternative Scoring Technique . . . . . . . . . . . . 150 7.4 Multicode Substitution . . . . . . . . . . . . . . . . . . . . . . . . . . 151 7.5 Multicode Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 7.6 Multicode Performance Results . . . . . . . . . . . . . . . . . . . . . 158 7.6.1 Individual Benchmark Performance . . . . . . . . . . . . . . . 159 7.6.2 Overall Performance Across All Benchmarks . . . . . . . . . . 167 7.7 Multicode Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 169 7.8 Scoring that Considers the Complete Impact of a Multicode . . . . . 175 7.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 8 Multicodes and Despecialization 182 8.1 Multicode Identification in the Presence of Despecialization . . . . . . 182 8.2 Despecialization’s Impact on Multicode Substitution . . . . . . . . . . 183 8.3 Performance Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 8.4 Balancing Despecialization, Multicode Substitution and Performance 189 8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 9 An Efficient Multicode Identification Algorithm 199 9.1 The Most Contributory Substring Problem . . . . . . . . . . . . . . . 199 9.2 Related String Problems . . . . . . . . . . . . . . . . . . . . . . . . . 200 9.3 Suffix Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 9.4 An Algorithm for the Most Contributory Substring Problem . . . . . 203 9.5 Handling Overlapping Occurrences . . . . . . . . . . . . . . . . . . . 204 9.6 Other Uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 9.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 10 Future Work 212 10.1 Despecialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 10.1.1 Evaluating the Impact of 76 Despecializations . . . . . . . . . 212 10.1.2 Further Load / Store Despecializations . . . . . . . . . . . . . 213 ix
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: 2.4.4 Direct Threading . . . . . .
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 and 16: B.1 The Steps Followed to Evaluate
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
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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
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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
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233 [33] K. Casey, D. Gregg, M. A.
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235 [55] D. Gregg and J. Waldron. P
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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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253 Table A.5 continued: # Len Scor
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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
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269 B.7 Summary This Chapter has de
Page 289 and 290:
271 Selected Honors and Awards Cont
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273 Departmental and University Pre
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