OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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$LATEX for Philosophers - University of Calgary$

Contents Certificate of Examination Abstract Acknowledgments ii iii v 1 Introduction 1 2 An Overview of Java 5 2.1 A Brief History of Java . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Overview of a Java Interpreter . . . . . . . . . . . . . . . . . . . . . . 8 2.3 The Java Instruction Set . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1 Loads and Stores . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.2 Constant Loading . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.3 Array Access . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.4 Stack Manipulation . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3.5 Arithmetic Operations . . . . . . . . . . . . . . . . . . . . . . 15 2.3.6 Casting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.7 Branching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.8 Float, Double and Long Comparison . . . . . . . . . . . . . . 18 2.3.9 Method Invocation . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.10 Field Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.11 Object Allocation . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.12 Method Completion . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.13 Miscellaneous . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.4 Executing a Java Application . . . . . . . . . . . . . . . . . . . . . . 20 2.4.1 Implementing an Interpreter with a Switch Statement . . . . . 21 2.4.2 A Better Switch Based Interpreter . . . . . . . . . . . . . . . 22 2.4.3 Indirect Threading . . . . . . . . . . . . . . . . . . . . . . . . 23 vi
2.4.4 Direct Threading . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.4.5 Just-In-Time (JIT) Compilation . . . . . . . . . . . . . . . . . 25 2.5 Java Virtual Machine Implementations . . . . . . . . . . . . . . . . . 27 2.5.1 Sun’s Java Virtual Machine . . . . . . . . . . . . . . . . . . . 27 2.5.2 The IBM Jikes Research Virtual Machine . . . . . . . . . . . . 27 2.5.3 The Kaffe Virtual Machine . . . . . . . . . . . . . . . . . . . . 28 2.6 Implementing a Simple Java Interpreter . . . . . . . . . . . . . . . . . 29 2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3 Related Work 32 3.1 Instruction Set Design . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.1.1 Bytecode Idioms . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.1.2 Super Operators / Semantically Enriched Code . . . . . . . . 33 3.1.3 Selective Inlining . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2 Other Optimization Strategies . . . . . . . . . . . . . . . . . . . . . . 36 3.2.1 Array Bounds Check Removal . . . . . . . . . . . . . . . . . . 36 3.2.2 Providing Extra Data to the VM . . . . . . . . . . . . . . . . 36 3.2.3 Devirtualization of Method Calls . . . . . . . . . . . . . . . . 37 3.2.4 Minimizing Synchronization Operations . . . . . . . . . . . . . 38 3.2.5 Bytecode Optimization . . . . . . . . . . . . . . . . . . . . . . 39 3.2.6 Native Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.3 Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3.1 Gathering the Profile Data . . . . . . . . . . . . . . . . . . . . 40 3.3.2 Other Profiling Studies . . . . . . . . . . . . . . . . . . . . . . 42 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4 Despecialization 45 4.1 Despecialization Categories . . . . . . . . . . . . . . . . . . . . . . . 45 4.1.1 Load Despecializations . . . . . . . . . . . . . . . . . . . . . . 46 4.1.2 Store Despecializations . . . . . . . . . . . . . . . . . . . . . . 48 4.1.3 Integer Constant Despecializations . . . . . . . . . . . . . . . 49 4.1.4 Non-Integer Constant Despecialization . . . . . . . . . . . . . 51 4.1.5 Branch Despecialization . . . . . . . . . . . . . . . . . . . . . 52 4.1.6 Widening Despecializations . . . . . . . . . . . . . . . . . . . 55 4.1.7 Other Despecializations . . . . . . . . . . . . . . . . . . . . . 56 4.2 Despecialization Performance . . . . . . . . . . . . . . . . . . . . . . 59 4.2.1 Test Environment . . . . . . . . . . . . . . . . . . . . . . . . . 59 vii
Page 1 and 2: OPTIMIZING THE JAVA VIRTUAL MACHINE
Page 3 and 4: Abstract Since its public introduct
Page 5: Acknowledgments While my name is th
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 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
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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
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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
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271 Selected Honors and Awards Cont
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273 Departmental and University Pre
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