OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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126 Figure 6.5: Percentage of Methods Invoked by Number of Parameters 6.2.4 Methods The descriptors for the methods invoked by the invoke* bytecodes were recorded. This made it possible to determine the number of parameters passed to each method as well as the static type of each method parameter and return value. When invokevirtual, invokeinterface and invokespecial are executed the receiver object is passed as the first parameter to the method. The receiver object is not included as part of the method’s descriptor. As a result, it had to be added during the analysis for those cases when it was present. Figure 6.5 shows the distribution of the number of method arguments to those methods invoked with any of the invoke* bytecodes. It includes two series of data. The first series, represented in gray, shows the number of formal method parameters declared by the programmer without including the receiver object. The second series, shown in black, shows the number of parameters actually passed to the method, including the receiver object when necessary. These distributions show that most of the methods invoked require one or two actual parameters with methods requiring three, four or five parameters also making contributions that are visible on the graph. The results of this analysis have already been employed in Section 6.2.1 when the utility of specialized load bytecodes was discussed. It is not yet know if this data may have any utility other than impacting the selection of specialized bytecodes.
127 6.2.5 Fields Only four bytecodes are included in the Java Virtual Machine Specification for field access. The getfield and putfield bytecodes provide the ability to read and write instance fields respectively. Similarly, the getstatic and putstatic bytecodes provide read and write access for static fields. Each of these bytecodes is able to access data of any type including array types. Figures 6.6 and 6.7 show the distribution of the types of the instance fields read and written. While the distributions are not identical, they show many similarities, with fields containing integers, object references and doubles being accessed with greatest frequency. Initially it may appear that integer writes are performed more frequently than integer reads because Figure 6.6 shows almost 32 percent of the fields accessed having integer type while Figure 6.7 shows more than 56 percent of the fields written having integer type. This is not the case. The getfield opcode accounts for more than 9.5 percent of the opcodes executed by the benchmarks considered during this study. In comparison, putfield accounts for only 1.3 percent of the bytecodes executed. Consequently, integer field reads account for about 3 percent of all bytecodes executed while integer field writes account for about 0.73 percent of all opcodes executed. Figures 6.8 and 6.9 show the distribution of the types of the static fields read and written. While the relative type distributions for getfield and putfield were similar, getstatic and putstatic show considerably different distributions. In particular, while arrays of object references and scalar object references were the static field types read with greatest frequency, the most frequently written static field types were double precision floating point numbers and integers. Static fields are accessed with much lower frequency than instance fields. Uses of getstatic account for 0.36 percent of the bytecodes executed while uses of putstatic account for less than 0.02 percent of the bytecodes executed. Based on these execution frequencies it was possible to determine how frequently each specific data type was accessed by both getstatic and putstatic. This analysis revealed that while static fields of type object reference array and object reference were read with much greater frequency than they were written, double, integer and long static fields were read and written with almost identical frequency. This pattern of behaviour is considerably different than that observed for getfield and putfield. In that case, every type was read more frequently than it was written, and in general, the reads far out numbered the writes. The utility of the information presented on fields for improving Java application
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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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Page 97 and 98: 79 JGF RayTracer: A 150 by 150 pixe
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Page 103 and 104: 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: 125 the time because the index bein
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 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
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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
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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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