OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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228 was developed which allowed such bytecode sequences to be identified automatically. Because the identification process was automated, some long sequences of bytecodes were replaced by multicodes. Such sequences could not be determined by human intuition alone. Performance results showed that designing a set of multicodes for a single application could decrease application runtime to approximately 70 percent of the original runtime in the best case. The performance gains achieved by performing multicode substitution come from at least two sources. Some of the performance gain is achieved as a result of reducing the total number of bytecode dispatches necessary in order to execute the application. Additional performance gains were achieved by performing optimizations on the codelet that implements the multicode. While some of these optimization can be performed automatically by the compiler, other optimizations rely on knowledge of the behaviour associated with internal virtual machine data structures. In particular, it is possible to reduce the number of memory writes considerably by recognizing that values which reside above the top of the stack once the multicode finishes executing will not be utilized subsequently. One of the disadvantages associated with performing multicode substitution is the fact that those bytecodes that are not currently defined by the Java Virtual Machine Specification are utilized. This means that performing multicode substitution prevents other optimizations that rely on their availability from being performed and limits the options for future expansion of the Java bytecode language. In order to overcome this disadvantage, a study was performed that examined the impact of performing both despecialization and multicode substitution at the same time. It was necessary to profile the applications again in order to perform this study due to four specialized bytecodes whose general purpose form depends on the context in which the despecialization is performed. Performing 67 despecializations before 50 multicodes were identified showed an average performance loss of approximately four percent compared to performing multicode substitution alone. This difference in performance occurred for two reasons. Part of the difference can be attributed to the fact that despecialization has already been shown to have a small detrimental impact on application runtime. The remainder of the performance loss occurred because performing 67 despecializations caused poorer overall multicode selections to be made because the best initial candidate identified in the presence of despecialization broke up many other sequences. Consequently, these sequences were not available for consideration subsequently. A second study was conducted where both despecialization and multicode sub-
229 stitution were performed. It considered performing 50 despecializations before 50 multicodes were identified. Seventeen bytecodes despecialized in the previous study were not despecialized in the second study. The bytecodes left in their specialized form were those that previous work on despecialization had shown to have the largest negative impact on application performance. The performance achieved in this second study was nearly identical to that achieved when 50 multicode substitutions were performed without any despecializations being performed. This result shows that one of the disadvantages of multicode substitution – the fact that it increases the number of distinct bytecodes in the virtual machine instruction set – can be overcome while maintaining its effectiveness as an optimization strategy. In conclusion, the studies presented in this thesis considered many interesting issues related to the design of the Java Virtual Machine instruction set. Taken independently, each study found interesting results. • Despecialization: Performing despecialization revealed that specialized bytecodes have surprisingly little impact on application performance. Further analysis revealed that using an asymmetric instruction set would allow specialized bytecodes to be used more effectively. • Multicodes: An automatic means of determining which bytecode sequences should be replaced with multicodes was presented. Using these multicodes was found to reduce application runtime by as much as 30 percent. Combining these ideas provided a mechanism for automatically developing a customized instruction set for an application. The customized instruction set retained those specialized bytecodes that were most used by the application while replacing under utilized specialized bytecodes with multicodes, improving application performance.
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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
Page 195 and 196: 177 ∆ x1 /x 2 /x 3 /.../x n→ an
Page 197 and 198: 179 Popping any values left on the
Page 199 and 200: 181 7.9 Conclusion This chapter has
Page 201 and 202: 183 ated will differ from that occu
Page 203 and 204: 185 Figure 8.1: A Comparison of the
Page 205 and 206: 187 performed impacts the sequences
Page 207 and 208: 189 Figure 8.2: 201 compress Perfor
Page 209 and 210: 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: 227 based on these profile results
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 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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