OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

More documents

Recommendations

Info

$LATEX for Philosophers - University of Calgary$

248 Table A.3 continued: # Len Score Multicode % 26 3 2.1 iload 3 aaload astore 27 4 1.5 aload 1 getfield isub ireturn 28 6 1.3 aload 0 iload 3 iconst 1 iadd putfield iload 2 29 2 0.8 aload 0 invokespecial 30 4 0.8 aload 0 aload 1 aload 0 invokevirtual 31 3 0.7 iload 1 bipush if icmpne 32 9 0.7 aload 0 aload 1 putfield aload 0 iconst 0 putfield aload 0 aload 1 invokespecial 33 2 0.6 iload 0 goto 34 3 0.6 iload iload 3 if icmplt 35 4 0.6 iload 1 castore aload 0 areturn 36 8 0.4 invokestatic lstore lload lload 1 lsub lstore lload invokestatic 37 7 0.4 aload iload 3 caload aload iload caload if icmpeq 38 4 0.4 istore 1 iload 1 bipush if icmpeq 39 7 0.4 getstatic lload ladd putstatic aload 0 iconst 1 invokespecial 40 5 0.4 new dup aload 0 dup ifnonnull 41 2 0.4 getfield if icmplt 42 7 0.4 aload 0 aload 1 iload 2 iload iadd baload invokevirtual 43 4 0.4 dup istore 3 bipush if icmpeq 44 4 0.3 iload 1 iinc aload 3 invokeinterface 45 4 0.3 aload 1 iload 3 i2c invokevirtual 46 2 0.3 iconst 1 iadd 47 2 0.3 aload 1 ifnonnull 48 2 0.3 iload 2 ifge 49 5 0.3 getstatic lload 0 ladd putstatic return 50 5 0.3 getstatic iload 1 iadd putstatic return
249 # Len Score Multicode % 1 2 100.0 aload 0 getfield 2 7 21.4 aload 0 dup getfield dup x1 iconst 1 iadd putfield 3 7 13.9 aload iload 3 caload aload iload caload if icmpeq 4 5 12.1 iload 3 caload iadd istore 1 iinc 5 5 11.1 aload 0 dup getfield iconst 1 iadd 6 7 9.6 iload iadd caload istore iload iload 1 if icmpne 7 5 8.1 iconst 0 baload sipush iand ireturn 8 2 7.9 aload 0 aload 1 9 3 7.8 iload 3 iload 2 if icmplt 10 3 7.3 bipush iload 1 imul 11 4 6.5 baload bastore iconst 1 ireturn 12 3 6.4 iload 3 iload if icmplt 13 2 6.3 iconst m1 if icmpne 14 9 5.9 istore 2 iload 2 ldc iand istore 2 iload 2 iload 1 irem ireturn 15 2 5.5 aload 0 invokevirtual 16 4 5.1 aload 3 iload iload castore 17 3 4.9 iload 3 iconst 1 if icmpne 18 3 4.9 iconst 0 iconst 1 invokevirtual 19 7 4.0 aload 1 iload caload istore iload iconst 1 if icmplt 20 2 4.0 iinc iinc 21 2 4.0 aload 0 iload 1 22 2 4.0 aload 1 invokevirtual 23 2 3.9 aload 1 iload 2 24 2 3.7 aload 3 getfield 25 2 3.6 istore 1 iload 1 Table A.4: Multicodes of Length 10 or Less using Maximum Number of Bytecodes Replaced Scoring for 213 javac
Page 1 and 2:
OPTIMIZING THE JAVA VIRTUAL MACHINE
Page 3 and 4:
Abstract Since its public introduct
Page 5 and 6:
Acknowledgments While my name is th
Page 7 and 8:
2.4.4 Direct Threading . . . . . .
Page 9 and 10:
7.2 An Overview of Multicodes . . .
Page 11 and 12:
List of Figures 2.1 Core Virtual Ma
Page 13 and 14:
7.11 201 compress Performance by Mu
Page 15 and 16:
B.1 The Steps Followed to Evaluate
Page 17 and 18:
6.1 Most Frequently Occurring Const
Page 19 and 20:
1 Chapter 1 Introduction When Moore
Page 21 and 22:
3 Studies were conducted that consi
Page 23 and 24:
5 Chapter 2 An Overview of Java Thi
Page 25 and 26:
7 Addressing this need led to the d
Page 27 and 28:
9 performs garbage collection, a cl
Page 29 and 30:
11 also has the ability to contain
Page 31 and 32:
13 ... i − 1 iload 0x04 dstore 0x
Page 33 and 34:
15 ... aload 3 iconst 2 faload ...
Page 35 and 36:
17 Condition Equal Unequal Greater
Page 37 and 38:
19 transfered to the start of a new
Page 39 and 40:
21 piled into machine language inst
Page 41 and 42:
23 Figure 2.6: Interpreter Engine C
Page 43 and 44:
25 Figure 2.8: Interpreter Engine C
Page 45 and 46:
27 The following section examines t
Page 47 and 48:
29 execute (JMethod * method, Slot
Page 49 and 50:
31 codelets shown in this example a
Page 51 and 52:
33 3.1 Instruction Set Design The a
Page 53 and 54:
35 is replaced with a new opcode if
Page 55 and 56:
37 method. However, the Java Virtua
Page 57 and 58:
39 3.2.5 Bytecode Optimization Tool
Page 59 and 60:
41 each bytecode executed as was ac
Page 61 and 62:
43 executing the application. The a
Page 63 and 64:
45 Chapter 4 Despecialization The J
Page 65 and 66:
47 ... i − 1 iload 3 iload 0x04 i
Page 67 and 68:
49 purpose bytecodes. The notation,
Page 69 and 70:
51 is present in the constant pool
Page 71 and 72:
53 ... iload 3 iflt ... i − 1 i i
Page 73 and 74:
55 the stack challenging in some ca
Page 75 and 76:
Figure 4.7: Despecialization of Bra
Page 77 and 78:
59 Specialized Bytecode load store
Page 79 and 80:
61 tual machine (RVM). Version 2.3.
Page 81 and 82:
63 Test Total Weighted Condition Pe
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
69 4.2.6 Performance Summary Compar
Page 89 and 90:
71 Category Original Desp. Percent
Page 91 and 92:
73 the maximum value encountered af
Page 93 and 94:
75 • Efficient support of new lan
Page 95 and 96:
77 difference in the performance of
Page 97 and 98:
79 JGF RayTracer: A 150 by 150 pixe
Page 99 and 100:
81 # Average % of Specialized Despe
Page 101 and 102:
83 provided by only two vendors. IB
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 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 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
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
Page 227 and 228: 209 Algorithm CountUniqueOccurrence
Page 229 and 230: 211 this algorithm did not consider
Page 231 and 232: 213 exceptions if the value being c
Page 233 and 234: 215 Figure 10.1: Multicode Blocks u
Page 235 and 236: 217 approximately 30 percent of the
Page 237 and 238: 219 a strategy other than direct th
Page 239 and 240: 221 10.2.6 Combining Multicodes and
Page 241 and 242: 223 10.2.9 Expanding the Class File
Page 243 and 244: 225 revealed that none of the const
Page 245 and 246: 227 based on these profile results
Page 247 and 248: 229 stitution were performed. It co
Page 249 and 250: 231 [13] B. Alpern, C. R. Attanasio
Page 251 and 252: 233 [33] K. Casey, D. Gregg, M. A.
Page 253 and 254: 235 [55] D. Gregg and J. Waldron. P
Page 255 and 256: 237 [78] E. M. McCreight. A space-e
Page 257 and 258: 239 [100] L. A. Smith, J. M. Bull,
Page 259 and 260: 241 [120] T. A. Welch. A technique
Page 261 and 262: 243 # Len Score Multicode % 1 30 10
Page 265: 247 # Len Score Multicode % 1 19 10
Page 271 and 272: 253 Table A.5 continued: # Len Scor
Page 277 and 278: 259 the level of entropy within the
Page 279 and 280: 261 there are no pointers to these
Page 281 and 282: 263 information for each method so
Page 283 and 284: 265 to emit four files that were su
Page 285 and 286: Figure B.1: The Steps Followed to E
Page 287 and 288: 269 B.7 Summary This Chapter has de
Page 289 and 290: 271 Selected Honors and Awards Cont
Page 291: 273 Departmental and University Pre
show all

OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

Create successful ePaper yourself

Delete template?

Save as template?