OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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58 Specialized Bytecode if icmpeq if acmpeq ifnull i2c i2b i2s iinc tableswitch anewarray General Purpose Bytecode if icmpne goto if acmpne goto ifnonnull goto ldc w iand ishl ishr ishl ishr iload ldc w iadd istore lookupswitch multianewarray Table 4.4: Other Specialized Bytecodes and their General Purpose Forms The lookupswitch and tableswitch bytecodes are used for the efficient implementation of switch/case statements, providing a single bytecode that is responsible for determining what case is to be executed and transferring control to the appropriate code segment. The semantics of the tableswitch bytecode can be encompassed by lookupswitch. While lookupswitch explicitly lists each of the possible values being tested, tableswitch provides a more compact representation for handling consecutive values, requiring approximately half as many bytes in the code stream. Consequently, performing this despecialization can increase the size of the code stream considerably because tableswitch requires 4 bytes for each value considered by the case statement while lookupswitch requires 8. As a result, it is possible that performing this despecialization could increase the size of the code attribute beyond 65535 bytes, the maximum size permitted by the Java Virtual Machine Specification. The final specialized bytecode considered in this category is anewarray. This bytecode is used to allocate a new array of object references. A separate bytecode, multianewarray, is included as part of the Java Virtual Machine Specification. It is capable of allocating an n-dimensional array of object references. The Java Virtual Machine Specification does not preclude multianewarray from allocating an array of 1 dimension. Consequently occurrences of anewarray can be despecialized by replacing them with a multianewarray bytecode that allocates an array with only one dimension. Table 4.4 lists all of the despecializations discussed in this section. The corresponding general purpose bytecodes are included in the second column. A complete list of all of the specialized bytecodes identified by this study and their equivalent despecialized forms is located in Table 4.5.
59 Specialized Bytecode load store dconst fconst lconst iconst if if icmplt if icmple bipush sipush ldc goto jsr if icmpeq if acmpeq ifnull i2c i2b i2s tableswitch iinc anewarray General Purpose Bytecode load store ldc2 w ldc / ldc w ldc2 w ldc / ldc w iconst 0 if icmp swap if icmpgt swap if icmpge sipush ldc / ldc w ldc w goto w jsr w if icmpne goto if acmpne goto ifnonnull goto ldc w iand ishl ishr ishl ishr lookupswitch iload ldc w iadd istore multianewarray Table 4.5: All Specialized Bytecodes and their General Forms 4.2 Despecialization Performance The initial phase of despecialization performance testing examined the execution of seven benchmarks on three virtual machines. The configuration of the test environment and details of the benchmarks tested are located in Sections 4.2.1 and 4.2.2 respectively. Sections 4.2.3, 4.2.4 and 4.2.5 report the observed change in benchmark performance for the Kaffe, IBM and Sun virtual machines respectively. 4.2.1 Test Environment Performance testing was conducted using three different virtual machines. Each virtual machine was used to test a total of 10 conditions. The first condition used each benchmark in its original, unmodified form, allowing a baseline execution time to be established. Nine additional conditions were tested that perform different com-
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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
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
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: Figure 4.7: Despecialization of Bra
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 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
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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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