OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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140 identify the branch targets and exception handler boundaries. A further analysis would have determined how many times each sequence of bytecodes was present. Unfortunately, such an analysis was unlikely to identify good candidates for multicodes because it entirely fails to consider which bytecodes are executed once, which bytecodes are executed many times and which bytecodes are never executed as the application runs. As a result, it was necessary to consider another profiling technique that would overcome these limitations. 7.3.3 An Initial Approach to Dynamic Profiling Dynamic profiling records information about an application as it executes. It can be used to overcome the limitations associated with static profiling. A copy of the Kaffe virtual machine was modified in order to collect the profile information needed to determine which candidate sequences were executed with greatest frequency. The information collected included: • Every bytecode executed, and the program counter value for the bytecode. • The name of each method executed. • A list of branch and exception targets for each method executed. Each opcode was recorded as it was executed. The program counter value associated with the opcode was needed in order to determine the multicode block boundaries within the profile data. The first time a method was invoked, its fully qualified name and signature was recorded. In order to reduce the memory requirements of profiling, a unique integer was assigned to each method. It was used to identify subsequent invocations of the method so it was not necessary to store its fully qualified name multiple times. The branch structure for each method was also computed and stored the first time the method was invoked. This information was required in order to break the profile data into multicode blocks once it was collected. Once the profile data was collected, it was converted from a list of bytecodes and method invocations into a list of multicode blocks. This involved reading every bytecode executed and its program counter value from the profile data, and writing the bytecode to the list of multicode blocks. Because the program counter value for each bytecode, and the branch structure for each method was recorded, sufficient information was available to determine which bytecodes represented the start of a
141 Block 1: Block 2: aload 0 → getfield → aload 0 → getfield → getfield → aload 0 → getstatic → aload 0 → getfield → Figure 7.2: Example Candidate Sequences new multicode block. When such a bytecode was encountered, a newline character was added to the block list in order to denote the start of a new multicode block. During the initial phases of this study, multicodes of lengths between two and seven bytecodes were considered. This analysis consisted of finding all subsequences of each multicode block and recording how many times each sequence was encountered in the profile data for the application. Once the counts were determined a scoring operation was performed. It assigned a score to each multicode, computed as the product of the number of times it occurred and the length of the sequence. a result, the score was the total number of bytecodes that would be impacted by performing the substitution. those sequences which had the highest score. The best candidates for multicode substitution were Figure 7.2 shows a small example consisting of two multicode blocks. It will be used to illustrate how candidate sequences are identified and how to determine which candidate sequences will give the most performance benefit. In a multicode block of length n, each contiguous subsequence of bytecodes is one occurrence of a particular candidate sequence. There are n − 1 length 2 candidate sequences, n−2 length 3 candidate sequences, etc., up to one candidate sequence with Sequence Count Score aload 0 getfield 3 6 getfield aload 0 getstatic aload 0 getfield 1 5 getfield aload 0 2 4 aload 0 getfield aload 0 getfield 1 4 getfield aload 0 getstatic aload 0 1 4 aload 0 getstatic aload 0 getfield 1 4 aload 0 getfield aload 0 1 3 getfield aload 0 getfield 1 3 getfield aload 0 getstatic 1 3 aload 0 getstatic aload 0 1 3 getstatic aload 0 getfield 1 3 aload 0 getstatic 1 2 getstatic aload 0 1 2 As Table 7.1: Bytecode Sequences Example
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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
Page 107 and 108: 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: 139 cache performance. As a result,
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
Page 207 and 208: 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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