OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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64 Time (s) 167 166 165 164 163 162 161 160 159 Kaffe Intrp Compress Testing Conditions Time (s) 15.5 15.4 15.3 15.2 15.1 15 14.9 14.8 Kaffe Intrp DB Testing Conditions Time (s) 38.6 38.5 38.4 38.3 38.2 38.1 38 37.9 37.8 37.7 37.6 Kaffe Intrp Jack Testing Conditions Time (s) 23.9 23.8 23.7 23.6 23.5 23.4 23.3 23.2 23.1 Kaffe Intrp Javac Testing Conditions Time (s) 31.5 31 30.5 30 29.5 29 28.5 Kaffe Intrp Jess Testing Conditions Time (s) 15.8 15.7 15.6 15.5 15.4 15.3 15.2 15.1 15 14.9 Kaffe Intrp Mpeg Testing Conditions Time (s) 22.7 22.6 22.5 22.4 22.3 22.2 22.1 22 21.9 21.8 21.7 21.6 21.5 21.4 21.3 Kaffe Intrp MTRT Testing Conditions Legend Base Load Store Load Store Branch Constant Constant Widening LSC LSCB Complete Figure 4.8: Despecialization Performance for the Kaffe Virtual Machine
65 Test Total Weighted Condition Performance Average Change (s) (Percent) Base 0.00 0.00 Load -0.26 -0.15 Store 1.36 0.78 Load Store 0.74 0.42 Branch -2.38 -1.36 Constant 0.43 0.25 Constant Widening -0.98 -0.56 LSC 0.17 0.10 LSCB -2.04 -1.17 Complete -3.89 -2.23 Table 4.8: RVM Performance tual machine would be smaller than those observed for the Kaffe interpreter. This assessment is based on the fact that many of the despecializations performed will not impact the final machine code generated by the JIT compiler. Consider the following example. When a specialized load bytecode is converted to machine code the JIT compiler generates the machine language instructions necessary to load the local variable specified by the opcode. When a general purpose load bytecode is compiled, the same instructions are generated to copy the value from the local variable storage to the stack. In this case, the position that is copied is dictated by an operand in the code stream. However, this operand is a constant value that cannot change during the execution of the application. Consequently, when JIT compilation is performed, the operand specifying the local variable to load can be used directly, generating machine code that is just as efficient as that generated for the specialized bytecode. The only overhead introduced as a consequence of this despecialization is the small amount of extra work that must be performed during JIT compilation to decode the operand specifying the local variable and the extra time related to loading the despecialized class file which is slightly larger than the original class file. Each of the operations that have extra costs associated with them occur once when the application is executed. In an interpreter, the overhead associated with decoding extra operands are imposed every time the despecialized bytecodes are executed, causing the overhead to impact the performance of the application many times throughout its execution. Table 4.8 shows a summary of the performance results observed when the IBM’s RVM was tested. It is observed that performing Complete despecialization slows 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
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 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: 63 Test Total Weighted Condition Pe
Page 85 and 86: 67 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
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
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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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