OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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68 Time (s) 25.7 25.6 25.5 25.4 25.3 25.2 25.1 25 24.9 24.8 24.7 24.6 Sun Compress Testing Conditions Time (s) 36.4 36.3 36.2 36.1 36 35.9 35.8 35.7 35.6 35.5 35.4 35.3 35.2 Sun DB Testing Conditions 13.9 13.8 13.7 13.6 13.5 13.4 13.3 13.2 13.1 13 12.9 12.8 12.7 12.6 12.5 Time (s) Sun Jack Testing Conditions Time (s) 27.2 27.1 27 26.9 26.8 26.7 26.6 26.5 26.4 26.3 26.2 Sun Javac Testing Conditions Time (s) 11.7 11.6 11.5 11.4 11.3 11.2 11.1 11 10.9 10.8 10.7 10.6 Sun Jess Testing Conditions Time (s) 19.9 19.8 19.7 19.6 19.5 19.4 19.3 19.2 19.1 19 18.9 18.8 Sun Mpeg Testing Conditions Sun MTRT Legend Time (s) 7.7 7.6 7.5 7.4 7.3 Base Load Store Load Store Branch Constant Constant Widening 7.2 7.1 Testing Conditions LSC LSCB Complete Figure 4.10: Despecialization Performance for Sun’s Virtual Machine
69 4.2.6 Performance Summary Comparing the results of the three benchmarks tested in this study reveals that the Sun virtual machine was impacted the least by despecialization. The Kaffe virtual machine showed the largest performance loss as a result of despecialization, leaving IBM’s research virtual machine in the middle position. When the average performance across all of the benchmarks was considered, each of the virtual machines showed showed a performance loss of less than 2.4 percent. Computing the average performance across all of the virtual machines and benchmarks revealed an overall increase in runtime of 2.0 percent. These changes in performance are minor. Using 67 bytecodes (one third of those defined by the Java Virtual Machine Specification) to achieve this difference is wasteful when those bytecodes can be utilized for other purposes. The largest single change in benchmark performance occurred when 227 mtrt was executed using IBM’s RVM under the complete despecialization condition. This performance loss of almost 12.7 percent was considerably larger than the largest losses observed for the other two virtual machines. Note that this change in performance is the direct result of performing branch despecialization. The introduction of a new optimization into the IBM virtual machine to handle the branching patterns that occur in the despecialized bytecode stream would likely result in a considerably smaller maximum performance loss. Consequently, examining the maximum performance losses also leads to the previous conclusion. 4.3 Despecialization and Class File Size The use of specialized bytecodes impacts more than application runtime. Another result that must be considered is the impact despecialization has on class file size. Each of the despecializations discussed previously replaces a specialized bytecode with one or more general purpose bytecodes. In all cases except for the despecialization of bipush and sipush into ldc or ldc w, the general purpose bytecode or sequence is at least one byte larger than the original specialized bytecode. This may be the result of the fact that an operand value is now required in order to specify the position to be accessed, the fact that the same operand value has been widened to occupy additional bytes or the fact that additional opcodes reside in the code stream. It is possible for some occurrences of bipush to be despecialized without increasing class file size. This can occur when the value being generated already resides in the
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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 ...
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
Page 67 and 68: 49 purpose bytecodes. The notation,
Page 69 and 70: 51 is present in the constant pool
Page 71 and 72: 53 ... iload 3 iflt ... i − 1 i i
Page 73 and 74: 55 the stack challenging in some ca
Page 75 and 76: Figure 4.7: Despecialization of Bra
Page 77 and 78: 59 Specialized Bytecode load store
Page 79 and 80: 61 tual machine (RVM). Version 2.3.
Page 81 and 82: 63 Test Total Weighted Condition Pe
Page 83 and 84: 65 Test Total Weighted Condition Pe
Page 85: 67 Test Total Weighted Condition Pe
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
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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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