OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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122 Bytecode Percentage iconst 1 1.649 iconst 0 0.695 iconst 2 0.360 dconst 1 0.206 dconst 0 0.177 iconst m1 0.171 bipush 08 0.140 bipush 10 0.128 ldc (int)65535 0.123 ldc2 w (long)65535 0.122 ldc2 w (long)65537 0.122 aconst null 0.120 sipush 00 ff 0.096 iconst 3 0.095 bipush 07 0.087 iconst 4 0.079 iconst 5 0.075 bipush 06 0.061 bipush 20 0.050 bipush 0a 0.047 bipush 0f 0.034 bipush 1f 0.032 ldc (int)2147483647 0.031 bipush 30 0.026 bipush 0d 0.024 ldc (int)1050011 0.023 sipush 01 00 0.022 bipush 28 0.022 bipush 09 0.019 ldc2 w (long)281474976710655 0.018 ldc2 w (long)25214903917 0.018 ldc2 w (long)11 0.018 bipush 40 0.018 ldc (int)58399 0.018 bipush 1b 0.014 bipush 18 0.013 ldc (int)65536 0.013 sipush 0c 35 0.013 ldc2 w (double)16384 0.013 ldc2 w (double)32768 0.013 Table 6.1: Most Frequently Occurring Constant Values
123 load and store bytecodes. It is the author’s belief that the larger use of specialized constant loading bytecodes is the result of the fact the bytecode language designers chose to introduce an asymmetry into the language, supplying more instructions for handling integer constants than constants of other types. However some integer constants such as 8, 16 and 65535 still occur more frequently than at least half of the specialized constant loading bytecodes. It was also observed that unlike the specialized store bytecodes, all of the specialized constant loading bytecodes made available by the Java Virtual Machine Specification are executed by the benchmarks tested in this study. Examining additional constant loading bytecodes reinforces the patterns observed initially. In particular, it was observed that the majority of the top 20 constant loading bytecodes executed were used for handling integers. Only 5 of these bytecodes were used to handle other types including 2 bytecodes for loading double constants, 2 bytecodes for loading long integer constants and aconst null, the bytecode that places the null object reference constant on the stack 1 . This leaves 15 of the top 20 bytecodes handling integers. It was also observed that 10 of the top 20 load bytecodes are specialized, a proportion that was superior to that achieved by either the load or store bytecodes. This is particularly noteworthy because there are only 15 specialized constant loading bytecodes compared to 20 each for specialized load and store bytecodes. Examining the top 40 load bytecodes reveals that 29 are used for loading integer values. The presence of 3 additional long integer constants and 2 additional double constants was also observed when the top 40 constant loading bytecodes were compared to the top 20 constant loading bytecodes. The three long integer constants are particularly noteworthy because the constant values 281474976710655, 2521490- 3917 and 11 are all used during random number generation. The JGF benchmarks SOR, FFT, HeapSort, and Spare Matrix Multiplication make use of a random number generator in order to populate data structures. Populating data structures in this manner is not typical of most “real world” applications. As a result, specializing constant values used for this purpose is not recommended. Further analysis was conducted in order to determine which constant values were 1 Strictly speaking aconst null is not a specialized bytecode since the current Java Virtual Machine Specification does not permit a null object reference to be loaded from the constant pool. However, such an extension to the semantics of ldc/ldc w would be minor making aconst null a possible candidate for despecialization if any changes are made to the Java Virtual Machine Specification. As a result, it was included as part of this analysis.
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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
Page 89 and 90: 71 Category Original Desp. Percent
Page 91 and 92: 73 the maximum value encountered af
Page 93 and 94: 75 • Efficient support of new lan
Page 95 and 96: 77 difference in the performance of
Page 97 and 98: 79 JGF RayTracer: A 150 by 150 pixe
Page 99 and 100: 81 # Average % of Specialized Despe
Page 101 and 102: 83 provided by only two vendors. IB
Page 103 and 104: Figure 5.1: Average Performance Res
Page 105 and 106: 87 Figure 5.4: Performance Results
Page 107 and 108: 89 Figure 5.8: Performance Results
Page 109 and 110: 91 ences in background processes, m
Page 111 and 112: 93 difference in performance due to
Page 113 and 114: 95 when a pure interpreter virtual
Page 115 and 116: 97 It is observed that many of the
Page 117 and 118: 99 Within this category, it was als
Page 119 and 120: 101 machine code for the idioms. As
Page 121 and 122: 103 were performed was less than 0.
Page 123 and 124: 105 these despecializations in a me
Page 125 and 126: 107 Chapter 6 Operand Profiling Cha
Page 127 and 128: 109 the applications. In addition,
Page 129 and 130: 111 The aload 0 bytecode was the mo
Page 131 and 132: 113 function being integrated. Beca
Page 133 and 134: 115 Implementing alignment rules un
Page 135 and 136: 117 these data types in the list of
Page 137 and 138: Figure 6.3: Distribution of Local V
Page 139: 121 are never executed by any of th
Page 143 and 144: 125 the time because the index bein
Page 145 and 146: 127 6.2.5 Fields Only four bytecode
Page 147 and 148: 129 Figure 6.8: Data types Accessed
Page 149 and 150: 131 lar, the new set of specialized
Page 151 and 152: 133 multicode block is defined to b
Page 153 and 154: 135 Figure 7.1: Two Bytecodes and t
Page 155 and 156: 137 because of the presence of the
Page 157 and 158: 139 cache performance. As a result,
Page 159 and 160: 141 Block 1: Block 2: aload 0 → g
Page 161 and 162: 143 Sequence Count Score getfield a
Page 163 and 164: Figure 7.3: Iterative Multicode Ide
Page 165 and 166: 147 number of bytecodes that will b
Page 167 and 168: 149 BlockList: A list of the byteco
Page 169 and 170: 151 Both the Total Bytecodes Score
Page 171 and 172: 153 ... i − 1 ❄ ifeq 0x00 0x15
Page 173 and 174: 155 Figure 7.7: Number of Unique Ca
Page 175 and 176: Figure 7.9: Cumulative Multicode Bl
Page 177 and 178: 159 However, while considering long
Page 179 and 180: 161 Figure 7.10: 201 compress Perfo
Page 181 and 182: 163 Figure 7.14: Length 209 db Perf
Page 183 and 184: 165 Figure 7.18: 222 mpegaudio Perf
Page 185 and 186: 167 7.6.2 Overall Performance Acros
Page 187 and 188: 169 a larger portion of the instruc
Page 189 and 190: 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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