OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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264 B.3 Virtual Machine Invocation Script The Kaffe virtual machine is normally invoked using a shell script which executes the virtual machine binary after performing some setup operations. One of these operations is to check the value of the CLASSPATH environment variable and modify it to include Kaffe’s implementation of the Java standard library. While the intentions of the script are good, allowing the virtual machine to execute when the CLASSPATH environment variable is not set correctly, performing this update cause problems in a research environment where one is testing different versions of the library. A new version of the script was created which did not modify the CLASSPATH environment variable. Using the modified script ensured that no profiling or timings were inadvertently conducted using the standard library implementation instead of the modified version. Similar scripts were also created for the Sun and IBM virtual machines. These scripts ensured that the classes loaded by the virtual machine were restricted to those modified as part of the experiment being performed. Without these scripts, unmodified classes could be used inadvertently when the CLASSPATH environment variable was not set correctly or some command line arguments were not specified correctly. B.4 Virtual Machine Generation A separate tool was developed to create a new, multicode enabled version of the Kaffe virtual machine. It was not used during the despecialization studies presented in Chapters 4 and 5. This tool was developed using Perl because of the amount of text processing involved in generating the source code for the new virtual machine. Like the class file mutator, this tool reads a rule file which describes the despecializations and / or multicode substitutions to be performed. A small number of modifications were made to the Kaffe virtual machine by hand. These changes involved modifying the source files that required modifications in order to support multicodes so that those portions of the file that would change were abstracted into separate files. The separate files were inserted at the correct locations using the #include macro. Making this change has no impact on the performance of the virtual machine because the macro processor performs a simple textual substitution as one of the first steps in the compilation process. However, the change eased the task of developing the virtual machine generation tool, requiring it
265 to emit four files that were subsequently included into the virtual machine during the compilation process instead of requiring it to patch the existing files. The virtual machine generation tool outputs four distinct files that enable the Kaffe virtual machine to support multicodes. The first contains the declaration of an array that specifies the size of each bytecode including its operands. Two additional files describe the tasks that must be performed as part of verifying the correctness of the class files at the basic block and method level. It is important to note that if Kaffe is extended to fully implement the verification procedure required by the Java Virtual Machine Specification then it will likely be necessary to generate several more files to update a full verifier. The final file generated contains the codelets for all of the bytecodes supported by the interpreter, including the new multicodes. B.5 Benchmark Performance Testing Scripts In order to generate the performance results presented in this thesis, it was necessary to execute many benchmarks using several different versions of the class files that represent them. It was also necessary to use the corresponding versions of the Java library. The following sections describes the scripts used for the despecialization and multicode studies. B.5.1 Despecialization Scripts The first despecialization study conducted used a different script for each virtual machine tested. This script measured the performance of each benchmark using all of the rule files located in a prescribed subdirectory. It was responsible for applying the rule file to every class in the benchmark being tested, every class in the Java library and invoking the virtual machine in such a manner so as to ensure that only those classes were utilized. These scripts were subsequently generalized so that a single script could be used to test an arbitrarily large number of virtual machines. The timing script executed each benchmark several times. By default, 6 iterations were used. However, this value could be changed by updating the value of a single constant within the script. The output from the timing script included an indication of each rule file tested and the actual output generated when each benchmark was executed. A separate script was used to parse the performance results. It reported the average performance of each rule file tested for each benchmark after the highest and lowest run times were discarded in order to minimize the impact of outliers.
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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
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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
Page 231 and 232: 213 exceptions if the value being c
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Page 235 and 236: 217 approximately 30 percent of the
Page 237 and 238: 219 a strategy other than direct th
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: 263 information for each method so
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Page 287 and 288: 269 B.7 Summary This Chapter has de
Page 289 and 290: 271 Selected Honors and Awards Cont
Page 291: 273 Departmental and University Pre
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