OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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142 length n (the entire multicode block itself). By counting the number of times each candidate sequence occurs across all multicode blocks, one can obtain the complete set of candidate sequences executed, as well as the number of times each sequence executed. Performing multicode identification on the blocks shown in Figure 7.2 began by determining all subsequences of length 2. This revealed that a total of 7 sequences with length 2 are present. Three of these sequences are contributed by Block 1: aload 0 → getfield, getfield → aload 0, and aload 0 → getfield. Four additional sequences with length 2 are contributed by Block 2. However, while a total of 7 sequences with length 2 are present, only four of the sequences are distinct. For example, the sequence aload 0 → getfield occurs three times. The identification process went on to consider all sequences of length 3, 4 and 5 in the same manner. Table 7.1 presents some of the information needed to perform multicode identification. In addition to enumerating the set of unique candidate sequences, the table maintains the number of times each sequence appears. This count, in conjunction with an analysis of a specific candidate sequence, can be used to establish a score representing how much optimization potential the candidate sequence has. In the table, a very simple scoring procedure was used; the number of times the candidate sequence appears multiplied by the length of the candidate sequence. Other scoring algorithms will be presented later. Using maximum score as the basis for selection, the table indicates that aload 0 getfield should be the first multicode selected. However, attempting to use the table to determine the second multicode poses problems. To see why, assume that the first substitution is performed, using mc to indicate the multicode aload 0 → getfield. The two multicode blocks will change to the following: Block 1: Block 2: mc → mc → getfield → aload 0 → getstatic → mc → From the blocks shown above, it is obvious that Table 7.1 cannot be used to establish the second multicode. Almost all of the candidate sequences in the table no longer exist, and most of the others will have their occurrence count (and thus their score) changed because of the first multicode substitution. This issue suggests an iterative algorithm for multicode identification. The profiling work provides an initial stream of bytecodes, which is broken into a collection of multicode blocks, and then used to obtain a set of candidate sequences and associated scores. The candidate sequence with the best score is chosen as the first
143 Sequence Count Score getfield aload 0 getstatic mc 1 4 getfield aload 0 getstatic 1 3 aload 0 getstatic mc 1 3 mc mc 1 2 getfield aload 0 1 2 aload 0 getstatic 1 2 getstatic mc 1 2 Table 7.2: Bytecode Sequences Example after One Multicode Substitution multicode, after which a new collection of multicode blocks is obtained by replacing all occurrences of the candidate sequence with a newly assigned bytecode. The process of obtaining candidate sequences and scores is then repeated, and the candidate sequence with the best score is chosen as the second multicode. This process repeats until the desired number of multicodes is obtained. Table 7.2 shows the candidate sequences, occurrence counts, and scores obtained after the first multicode has been identified and substituted in the current example. Note that the number of candidate sequences is smaller than what exists in Table 7.1, and that the number of occurrences for some of the candidate sequences that are common to the two tables has changed. This second table indicates that the candidate sequence with the greatest optimization potential (contingent on the accuracy of the scoring algorithm) is getfield → aload 0 → getstatic → mc. Note that this sequence contains mc, representing the multicode identified previously. From a multicode identification point of view, this causes no real problems. However, special care must be taken during the implementation of such nested multicodes to ensure that the codelet representing the new multicode is generated correctly. As an alternative, the multicode identification algorithm could ignore any candidate sequences containing previously identified multicodes. The iterative multicode identification algorithm was implemented as a set of three distinct applications: • The Kaffe virtual machine was modified and used to generated an execution trace for the application being executed. Note that any virtual machine could have been used since the sequence of bytecodes executed is independent of the virtual machine (assuming that the application and library class files utilized are identical).
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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
Page 109 and 110: 91 ences in background processes, m
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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
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: 141 Block 1: Block 2: aload 0 → g
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 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
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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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