OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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184 be replaced with multicodes (compared to the specialized version), which will lead to further efficiency gains because each new multicode substitution removes more transfers. However, this benefit is competing with the small performance loss associated with executing despecialized code instead of specialized code. Previous despecialization studies showed that this performance loss is approximately 2 percent of the application’s run time. Consider the commonly occurring bytecode sequence aload 0 → getfield →. Before despecialization is performed, four additional bytecode sequences perform nearly identical tasks. These sequences are aload 1 → getfield →, aload 2 → getfield →, aload 3 → getfield → and aload → getfield →. After despecialization is performed, only a single bytecode sequence remains to perform this purpose: aload → getfield →. Consequently, before despecialization is performed, five multicode substitutions are necessary if each of the sequences occurs frequently. However, once despecialization is performed a single multicode can be employed to cover all five sequences. The number of distinct sequences that can be collapsed into a single substitution increases considerably with the length of the bytecode sequence under consideration. For example, consider a multicode that contains three general purpose load and/or store bytecodes (for example, two iload and one istore). There are 125 variants of this single general purpose bytecode sequence, since both iload and istore have 4 specialized variants. This number climbs to over 15,000 when the sequence contains six load and/or store bytecodes. Because of this, performing multicode identification after despecialization has the potential to impact a much larger proportion of the bytecode stream than it does without despecialization. In order to establish whether the above observations occurred in practice, the profile data collected in the previous chapter was compared with the profile data collected after despecialization was performed. This comparison confirmed that despecialization does reduce the total number of distinct sequences executed. However, the reduction in the number of sequences observed in practice is much smaller than the largest theoretical reduction that can occur. This is not surprising, because it is highly unlikely that any application would encounter all 125 variants of the bytecode sequence discussed above. The benchmarks that showed the greatest reduction were 209 db and 213 javac. Performing despecialization reduced the number of distinct sequences to approximately 72 percent of the number distinct sequences present before despecialization
185 Figure 8.1: A Comparison of the Cumulative Score for the Top n Multicodes Identified Before and After Despecialization was performed. It was also observed that these benchmarks executed the largest number of distinct sequences before despecialization was performed. The benchmark that showed the smallest reduction in the number of unique bytecode sequences executed as a result of despecialization was 201 compress. In this case, performing despecialization reduced the number of unique sequences to 81 percent of the original number. It was also observed that before despecialization was performed, the 201 compress benchmark executed the smallest number of distinct sequences. Figure 8.1 provides another means of comparing the impact of despecialization on multicode identification. The graph contains two pairs of lines. Each pair represents a before-and-after graphing of multicode-count versus cumulative score for a particular benchmark. The upper pair of lines is for the 202 jess benchmark, while the lower pair of lines is for 213 javac. These two graphs are representative of the graphs for the other benchmarks as well. In particular, 201 compress is similar to 202 jess, and the other benchmarks are similar to 213 javac. The scale on the y-axis in Figure 8.1 shows that the score for a multicode can be
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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
Page 151 and 152: 133 multicode block is defined to b
Page 153 and 154: 135 Figure 7.1: Two Bytecodes and t
Page 155 and 156: 137 because of the presence of the
Page 157 and 158: 139 cache performance. As a result,
Page 159 and 160: 141 Block 1: Block 2: aload 0 → g
Page 161 and 162: 143 Sequence Count Score getfield a
Page 163 and 164: Figure 7.3: Iterative Multicode Ide
Page 165 and 166: 147 number of bytecodes that will b
Page 167 and 168: 149 BlockList: A list of the byteco
Page 169 and 170: 151 Both the Total Bytecodes Score
Page 171 and 172: 153 ... i − 1 ❄ ifeq 0x00 0x15
Page 173 and 174: 155 Figure 7.7: Number of Unique Ca
Page 175 and 176: Figure 7.9: Cumulative Multicode Bl
Page 177 and 178: 159 However, while considering long
Page 179 and 180: 161 Figure 7.10: 201 compress Perfo
Page 181 and 182: 163 Figure 7.14: Length 209 db Perf
Page 183 and 184: 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: 183 ated will differ from that occu
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 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.
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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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