OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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138 eligible for multicode substitution because they span two or more multicode blocks. As a result, it is necessary to establish a separate notation to denote the number of times that the multicode will be executed if it is substituted. This notation is shown below in Equation 7.10 and Equation 7.11. Equation 7.10 represents the number of times that an unoptimized multicode substitution can be performed, while Equation 7.11 represents the number of optimized multicode substitutions that can be performed. These values are identical in all cases because multicode block boundaries, not optimization potential, dictate whether a substitution is possible. N x1 /x 2 /x 3 /.../x n→ (7.10) N x1 x 2 x 3 ...x n→ (7.11) The overall performance gain that can be expected from a multicode substitution is the product of the number of times that the multicode substitution can be performed and the amount of performance gain achieved for each substitution. The notation used to denote the unoptimized and optimized performance gain are shown in Equations 7.12 and 7.13 respectively. ε x1 /x 2 /x 3 /.../x n→ = ∆ x1 /x 2 /x 3 /.../x n→ × N x1 /x 2 /x 3 /.../x n→ (7.12) ε x1 x 2 x 3 ...x n→ = ∆ x1 x 2 x 3 ...x n→ × N x1 x 2 x 3 ...x n→ (7.13) The notation introduced in this section will be used to describe how to identify the best candidate sequences for multicode replacement for a particular application or set of applications. 7.3 Multicode Identification The process of determining what sequences of bytecodes should be replaced with multicodes will be referred to as multicode identification. The goal is to select those sequences that provide the maximal value of ε x1 x 2 x 3 ...x n→. However, determining these sequences is difficult because it is necessary to quantify the cumulative impact of a variety of factors, including the cost of the transfers removed, the amount of cost savings from optimization, and the impact the selection of the multicode will have on
139 cache performance. As a result, several different multicode identification techniques are considered. Each technique is responsible for assigning a score to each candidate sequence. The score is an integer value that specifies the candidate sequences’ relative value as a multicode. It may be derived through the use of a formula or may be assigned through less definite means such as intuition. Higher scores are assigned to sequences that are expected to be good selections for replacement with a multicode while lower scores are assigned to sequences that are expected to be poor selections. Using scores in this manner provides a mechanism for estimating the value of ε x1 x 2 x 3 ...x n→ during the multicode identification process. Several scoring systems are discussed in the following sections. 7.3.1 Intuition Intuition can be used to identify some short sequences that are expected to execute with great frequency. An example of such a sequence is aload 0 getfield. It is expected to execute with great frequency because it represents the process of reading the value of a field within a receiver object. Consequently, a high score will normally be assigned to this sequence while another sequence, intuitively expected to be less useful, will be assigned a low score. Unfortunately this technique may not provide good results because programs do not always behave the way one might intuitively expect and because it is difficult to predict sequences of more than a few bytecodes. This technique has been used by some of the related optimization techniques discussed in Section 3.1, such as bytecode idioms. 7.3.2 Static Profiling Techniques were developed that attempted to overcome the problems associated with intuitive approaches. A static examination of the classes that make up the benchmarks and the Java library was considered initially. The goal of this examination was to identify which sequences of bytecodes occurred within those class files with greatest frequency. Then the frequency information could be used in conjunction with other information, such as the length of the sequence, in order to determine a score for each candidate sequence. A static analysis would have begun by extracting the code attribute for each method in each class. Each code attribute would have been analyzed in order to
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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
Page 105 and 106: 87 Figure 5.4: Performance Results
Page 107 and 108: 89 Figure 5.8: Performance Results
Page 109 and 110: 91 ences in background processes, m
Page 111 and 112: 93 difference in performance due to
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: 137 because of the presence of the
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 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
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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
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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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