OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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218 specific class file will be dictated by a new attribute that will reside within each class file. At class load time, the value in this attribute will be used to associate each class with its execution engine through a function pointer. As the application executes, method invocations will be performed by identifying the class of the method being invoked and dereferencing the function pointer rather than making use of a standard function call. Implementing method invocations in this manner imposes only a small amount of runtime overhead while allowing multiple sets of multicodes to be employed. Under this system, it will be possible to execute any class file that does not contain multicodes using any execution engine since all of the original bytecodes are still available. The impact that this strategy will have on instruction cache performance is yet to be determined. Interpreters suffer from a general problem where the bytecodes that implement the user’s application reside in the data cache. Only the machine instructions that implement the virtual machine reside in the instruction cache. As a result, the demands placed on the instruction cache are relatively small, suggesting that two or more execution engines may be able to reside within the instruction cache simultaneously when large instruction caches are available. If the total size of the execution engines employed by an application is larger than the instruction cache it is expected that the loss in cache performance will likely exceed the performance gains that can be achieved with multicodes. Dynamic Multicode Sets While the strategy discussed in the previous section provides a technique to use multiple sets of multicodes, it does not provide any capability for defining these sets without recompiling the virtual machine. Ideally, a class file should be able to contain a description of the multicodes that it requires for optimal performance. Upon reading the list, the virtual machine should provide functionality for these multicodes. These goals can be accomplished. When the application is compiled, the multicodes that should be supported for optimal performance will be described in a new attribute. This attribute will specify the functionality of each multicode in terms of existing bytecodes. By specifying the functionality in this manner, verifiability of the class file can be retained. When the class file is loaded, the virtual machine must update its execution engine in order to support the multicodes specified by the class file. This kind of dynamic code modification can be accomplished through the use of a package such as Dyninst [27]. It is unclear what the best way would be to construct this execution engine. If
219 a strategy other than direct threading is used, then the number of multicodes that can be implemented is quite restricted, requiring the virtual machine to either ignore some multicode requests or build multiple execution engines in order to support the multicodes requested by each class file. Ideally, when the class files are modified in order to make use of multicodes, effort should be expended in order to only define multicodes that actually result in a meaningful change in performance. However, in general, one cannot assume that all of the class files were generated with the same compiler or modified to make use of multicodes at the same time, requiring the virtual machine to handle competing requests for multicodes in a reasonable manner. 10.2.4 Multicodes and Operands When profiling was conducted as part of the multicode identification process, every opcode executed was recorded. No information about the operands supplied to those opcodes was retained. Expanding the profiling to include the operands utilized may lead to further insights into the behaviour of the application and multicodes that offer even greater optimization opportunities. Consider the following bytecode sequence: iload → iconst 3 → imul → istore. When this sequence is converted to a multicode it will require two operand bytes. The first will specify the local variable accessed by the iload bytecode while the second will specify the local variable accessed by the istore bytecode. Profiling the operands in addition to the opcodes may reveal that there are many cases where each of these operands are the same, indicating that the same memory location is both read and written. This will reduce the number of operand bytes that must be provided to the multicode and may also offer additional optimization opportunities in the codelet for the multicode that could not be exploited when it was not known that each bytecode referred to the same local variable. 10.2.5 Expanding the Number of Multicodes Presently, the number of multicode substitutions that can be performed is restricted by the number of bytecodes that are left undefined by the Java Virtual Machine Specification, at 52. However, when considering many applications that will be executed by a single virtual machine, it may be desirable to support more multicodes without moving to multiple execution engines. This goal can be accomplished in one of several manners.
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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
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71 Category Original Desp. Percent
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73 the maximum value encountered af
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75 • Efficient support of new lan
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77 difference in the performance of
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79 JGF RayTracer: A 150 by 150 pixe
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81 # Average % of Specialized Despe
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83 provided by only two vendors. IB
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Figure 5.1: Average Performance Res
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87 Figure 5.4: Performance Results
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89 Figure 5.8: Performance Results
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91 ences in background processes, m
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93 difference in performance due to
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95 when a pure interpreter virtual
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97 It is observed that many of the
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99 Within this category, it was als
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101 machine code for the idioms. As
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103 were performed was less than 0.
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105 these despecializations in a me
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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
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
Page 191 and 192: 173 // iload_1: // iconst_1: // iad
Page 193 and 194: 175 Such bytecodes have a variable
Page 195 and 196: 177 ∆ x1 /x 2 /x 3 /.../x n→ an
Page 197 and 198: 179 Popping any values left on the
Page 199 and 200: 181 7.9 Conclusion This chapter has
Page 201 and 202: 183 ated will differ from that occu
Page 203 and 204: 185 Figure 8.1: A Comparison of the
Page 205 and 206: 187 performed impacts the sequences
Page 207 and 208: 189 Figure 8.2: 201 compress Perfor
Page 209 and 210: 191 Figure 8.5: 213 javac Performan
Page 211 and 212: 193 Compared to the complete despec
Page 213 and 214: 195 Figure 8.10: 209 db Performance
Page 215 and 216: 197 performing multicode substituti
Page 217 and 218: 199 Chapter 9 An Efficient Multicod
Page 219 and 220: 201 alphabet employed by each of th
Page 221 and 222: 9.4 An Algorithm for the Most Contr
Page 223 and 224: Figure 9.3: The Impact of Algorithm
Page 225 and 226: 207 Algorithm NonOverlappingScore(n
Page 227 and 228: 209 Algorithm CountUniqueOccurrence
Page 229 and 230: 211 this algorithm did not consider
Page 231 and 232: 213 exceptions if the value being c
Page 233 and 234: 215 Figure 10.1: Multicode Blocks u
Page 235: 217 approximately 30 percent of the
Page 239 and 240: 221 10.2.6 Combining Multicodes and
Page 241 and 242: 223 10.2.9 Expanding the Class File
Page 243 and 244: 225 revealed that none of the const
Page 245 and 246: 227 based on these profile results
Page 247 and 248: 229 stitution were performed. It co
Page 249 and 250: 231 [13] B. Alpern, C. R. Attanasio
Page 251 and 252: 233 [33] K. Casey, D. Gregg, M. A.
Page 253 and 254: 235 [55] D. Gregg and J. Waldron. P
Page 255 and 256: 237 [78] E. M. McCreight. A space-e
Page 257 and 258: 239 [100] L. A. Smith, J. M. Bull,
Page 259 and 260: 241 [120] T. A. Welch. A technique
Page 261 and 262: 243 # Len Score Multicode % 1 30 10
Page 271 and 272: 253 Table A.5 continued: # Len Scor
Page 277 and 278: 259 the level of entropy within the
Page 279 and 280: 261 there are no pointers to these
Page 281 and 282: 263 information for each method so
Page 283 and 284: 265 to emit four files that were su
Page 285 and 286: 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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