OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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Condition Baseline Load Store Load Store Branch Constant Constant Widening Load Store Constant (LSC) Load Store Constant Branching (LSCB) Description The original SPEC JVM98 and virtual machine library class files are used. All of the load bytecodes are despecialized as described in Section 4.1.1. All of the store bytecodes are despecialized as described in Section 4.1.2. Both the load and store bytecodes are despecialized, reducing the size of the set of bytecodes by 40. The bytecodes identified in Section 4.1.5 are despecialized. All bytecodes of the form const bcn are replaced with their general purpose counterparts as described in Sections 4.1.3 and 4.1.4. All constants are despecialized so that they reside in the constant pool. Loads are performed using ldc w or ldc2 w as appropriate. Widening is also performed on jsr and goto. All load and store bytecodes are despecialized. Non integer constants are loaded from the constant pool with ldc, ldc w or ldc2 w as appropriate. Integer constants are generated with bipush. All loads and stores are despecialized. Branches are despecialized before constants in order to ensure that any iconst 0 bytecodes introduced during this process are subsequently despecialized. Complete All 67 despecializations described in Sections 4.1.1 through 4.1.6 are performed. Despecializations are performed in an order than guarantees that later despecializations do not introduce bytecodes removed by earlier despecializations. 60 Table 4.6: Despecialization Conditions Tested binations of the despecializations described in Sections 4.1.1 through 4.1.6. These conditions are described in Table 4.6. The virtual machines tested represent three distinct models for executing Java applications. The first virtual machine considered was Kaffe version 1.0.7 [89]. This VM is a pure interpreter, fetching and executing each bytecode independently by making use of a loop and a switch/case statement. The virtual machine does not perform any aggressive optimizations. The second virtual machine considered by this study was the IBM research vir-
61 tual machine (RVM). Version 2.3.1 was tested in conjunction with Classpath version 0.07. This virtual machine does not include an interpreter. Instead, this virtual machine takes a compile everything approach, using a just in time compiler to generate platform specific machine language code before each method is invoked. This virtual machine also performs a number of aggressive optimizations by recompiling those methods that account for the greatest proportion of application runtime. The final virtual machine that was considered was Sun’s Java Development Kit version j2sdk1.4.2 02. This virtual machine is classified as mixed mode, meaning that it makes use of both an interpreter and a JIT compiler. The interpreter is used initially. This allows the application to begin executing immediately without requiring the user to wait for JIT compilation to be performed. As the application executes, hot methods are identified which represent a large proportion of the application’s execution time. The JIT compiler is applied to these methods, generating optimized platform specific machine code. Using this technique prevents time from being wasted by compiling code that is executed infrequently while still providing the performance benefits of a JIT compiler for those portions of the application that execute with greatest frequency. These virtual machines were executed on a dual processor Intel Pentium III running Redhat Linux kernel 2.4.20-20.9smp. The machine had a clock rate of 600MHz and 256 megabytes of main memory. 4.2.2 Benchmarks Tested Performance testing was conducted using the benchmarks from the widely accepted SPEC JVM98 Benchmark Suite. It includes 7 distinct applications that stress differing aspects of the virtual machine. The applications include: 201 compress: Performs a modified Lempel-Ziv method of data compression. This involves examining the data stream in order to find common substrings that are replaced by a variable size code. The specific algorithm used is described in [120]. 202 jess: A expert system based on NASA’s CLIPS expert shell system [9]. The benchmark solves a series of puzzles by applying a set of rules to a set of facts in order to determine the result. 209 db: Performs a series of operations on a memory resident database. The database is populated with one megabyte of names, addresses and
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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
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: 59 Specialized Bytecode load store
Page 81 and 82: 63 Test Total Weighted Condition Pe
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,
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111 The aload 0 bytecode was the mo
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113 function being integrated. Beca
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115 Implementing alignment rules un
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117 these data types in the list of
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Figure 6.3: Distribution of Local V
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121 are never executed by any of th
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123 load and store bytecodes. It is
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125 the time because the index bein
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127 6.2.5 Fields Only four bytecode
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129 Figure 6.8: Data types Accessed
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131 lar, the new set of specialized
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133 multicode block is defined to b
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135 Figure 7.1: Two Bytecodes and t
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137 because of the presence of the
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139 cache performance. As a result,
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141 Block 1: Block 2: aload 0 → g
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143 Sequence Count Score getfield a
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Figure 7.3: Iterative Multicode Ide
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147 number of bytecodes that will b
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149 BlockList: A list of the byteco
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151 Both the Total Bytecodes Score
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153 ... i − 1 ❄ ifeq 0x00 0x15
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155 Figure 7.7: Number of Unique Ca
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Figure 7.9: Cumulative Multicode Bl
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159 However, while considering long
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161 Figure 7.10: 201 compress Perfo
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163 Figure 7.14: Length 209 db Perf
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165 Figure 7.18: 222 mpegaudio Perf
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167 7.6.2 Overall Performance Acros
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169 a larger portion of the instruc
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171 // iload_1: stack[sp+1] = local
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173 // iload_1: // iconst_1: // iad
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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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