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158 bytecodes in length respectively. The 222 mpegaudio benchmark also shows a large jump for lengths 50 and beyond. This represents the frequent execution of several long multicode blocks consisting of approximately 200 bytecodes. 7.6 Multicode Performance Results This section presents performance results which show the impact of replacing frequently executed bytecode sequences with multicodes. Two distinct scoring systems have been presented in the previous sections. These include determining the score of a sequence based on the total number of bytecodes that will be replaced and determining the score based on the total number of transfers that will be removed. Performance testing was used in order to determine which technique should be employed. Differing values for the maximum length of a multicode were also considered. When the multicode identification process was discussed, candidate sequences of up to 50 bytecodes were evaluated in order to determine their value as a multicode. However, different values could be used. While testing longer sequences presents technical challenges due to the number of sequences that must be considered and the memory required to store those sequences, examining only shorter sequences was an option. The performance results presented in this section considered identifying multicodes when the maximum sequence length considered varied from 5 to 50 bytecodes. Testing multicodes up to 50 bytecodes in length covers nearly all of the bytecode sequences executed for all of the benchmarks under consideration except 222 mpegaudio. Testing longer sequences for 222 mpegaudio presented technical challenges because of the amount of memory required to store such a large number of distinct sequences. Table 7.3 shows that the average multicode block length for 222 mpegaudio is less than 15 bytecodes. As a result, while considering sequences up to 50 bytecodes in length does not cover all of the blocks executed, it exceeds the average block length executed by far. Figure 7.9 also shows that 95 percent of the blocks executed by 222 mpegaudio are less than 50 bytecodes in length. Furthermore, as Figure 7.18 shows, 222 mpegaudio showed better performance when the longest bytecode sequence considered was length 25 rather than length 50. As a result, evaluating longer sequences was not pursued. Differing values for the longest bytecode sequence were tested in order to determine what impact, if any, choosing longer sequences might have on application performance. Considering longer sequences increases the number of candidate sequences available, potentially allowing a substitution to be performed that has a higher score.
159 However, while considering longer sequences may allow a larger number of bytecodes to be replaced, or more transfers to be removed, there is also a potential cost associated with selecting many long multicodes. Since a new codelet must be added to the interpreter’s execution loop for each multicode selected, it is possible that choosing many long multicodes may increase the size of the interpreter’s main loop beyond the size of the instruction cache. Should this occur, it is likely that the performance loss due to decreased cache utilization will outweigh the performance gain achieved by multicode substitution. 7.6.1 Individual Benchmark Performance Six benchmarks from the SPEC JVM98 benchmark suite were tested. These were described previously in Section 4.2.2 as part of the discussion presented on despecialization. The performance results achieved by performing multicode identification and substitution for each benchmark are shown in Figure 7.10 through Figure 7.21. Each of the following pages contains two graphs. The upper graph shows the performance achieved for each combination of scoring system and maximum multicode length considered. Fifty multicode substitutions were performed for each point shown on the graph. For example, Figure 7.10 shows that, for the 201 compress benchmark, application run time was reduced by approximately 18 percent when 50 multicodes were identified using Total Bytecodes Replaced Scoring to identify multicodes of up to 5 bytecodes in length. Similarly, Figure 7.10 shows that the best technique for identifying multicodes for the 201 compress benchmark was to consider sequences up to 35 bytecodes in length using Transfer Reduction scoring. Using this combination of scoring system and multicode length reduced application run time by over 24 percent. The lower graph on each page shows the performance achieved using the best technique identified in the upper graph. This graph shows how performing additional multicode substitutions results in further performance gains by examining the application’s performance when 10, 20, 30, 40 and 50 multicode substitutions are performed. The specific list of bytecode sequences replaced with multicodes can be found in Table A.1 through Table A.6, located in Appendix 11. Table 7.4 presents a summary of the performance results achieved for each of the benchmarks. It lists the multicode selection technique and maximum multicode length that offered the best performance. Each performance value is expressed as a percentage of the original, unmodified application run time. The table also shows the average change in performance achieved across the six benchmarks tested. 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
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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
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
Page 133 and 134: 115 Implementing alignment rules un
Page 135 and 136: 117 these data types in the list of
Page 137 and 138: Figure 6.3: Distribution of Local V
Page 139 and 140: 121 are never executed by any of th
Page 141 and 142: 123 load and store bytecodes. It is
Page 143 and 144: 125 the time because the index bein
Page 145 and 146: 127 6.2.5 Fields Only four bytecode
Page 147 and 148: 129 Figure 6.8: Data types Accessed
Page 149 and 150: 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: Figure 7.9: Cumulative Multicode Bl
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 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
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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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