OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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74 Because despecialization increases the size of the code attribute, it was necessary to update the start pc and end pc values so as to guarantee that any of the general purpose bytecodes generated for a specialized bytecode are within the range of each exception handler if and only if the original specialized bytecode was within the range of the exception handler. Furthermore, it was also necessary to update handler pc in order to ensure that control is transferred to the correct location in the event that an exception is caught. Performing despecialization does not inhibit verification. It has already been noted previously that, in practice, despecialization does not violate any of the size constraints imposed by the Java Virtual Machine Specification. Consequently, verification is performed on the larger class files. Since the despecialized code attributes are semantically equivalent to their original specialized forms, a despecialized code attribute will pass verification if and only if the original specialized form passed verification. Similarly, a code attribute that failed verification before despecialization will still fail verification after despecialization is performed. 4.5 Summary This chapter has described the difference between general purpose bytecodes and specialized bytecodes. A total of 76 specialized bytecodes were identified along with their corresponding general purpose forms. Of these, 67 were implemented and tested using three Java virtual machines. Performance results were presented for the SPEC JVM98 Benchmark Suite. These results showed that an average increase of 2.0 percent was observed across all three virtual machines when complete despecialization was performed on all of the benchmarks. An analysis was also performed on despecialization’s impact on class file size. It was found that despecialization had a similarly small impact on class file size, increasing average class file size across all of the library and application classes by between 5.3 and 6.3 percent depending on the set of classes under consideration. While there are minor performance and class file size penalties associated with performing despecialization, a large benefit is achieved: the number of Java bytecodes in use is reduced from 201 to 134. This opens up many opportunities for expanding the bytecode language. New instructions could include: • Supporting frequently occurring operations that require several bytecodes to perform. The task of incrementing a field by a small integer is a particularly good example, requiring six bytecodes to perform.
75 • Efficient support of new language features such as complex numbers, quad precision floats, generics and multi methods. Alternately, it may not be desirable to make use of all of the instructions that despecialization makes available. Reducing the Java Virtual Machine instruction set reduces its complexity, speeding implementation and debugging. A smaller instruction set also benefits hardware implementations of Java, allowing for smaller implementations that make use of fewer transistors and consume less power. Given these benefits in comparison to the performance losses noted previously, it is recommended that any future revisions to the Java Virtual Machine Specification seriously consider omitting most or all of the specialized bytecodes tested during this study. Furthermore, researchers generating new intermediate languages or virtual machines are strongly advised to carefully consider both the costs and benefits associated with specialized bytecodes before including them as part of their specification.
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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
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 87 and 88: 69 4.2.6 Performance Summary Compar
Page 89 and 90: 71 Category Original Desp. Percent
Page 91: 73 the maximum value encountered af
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 and 140: 121 are never executed by any of th
Page 141 and 142: 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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