Lecture Notes in Computer Science 4917

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250 Y. Sazeides et al. Cumulative Dynamic Branches (%) 100 90 80 70 60 50 40 30 20 10 0 mcf 0 16 32 48 64 80 96 112 128 144 160 176 192 208 224 240 256 272 288 304 320 336 352 368 384 400 Cumulative Dynamic Branches (%) 100 90 80 70 60 50 40 30 20 10 0 mcf Number of Correlations (affectors) Fig. 2. Affectors distribution ammp00 bzip200 crafty00 fma3d00 gcc95 go95 ijpeg95 mcf00 mesa00 twolf00 vpr00 0 16 32 48 64 80 96 112 128 144 160 176 192 208 224 240 256 272 288 304 320 336 352 368 384 400 Number of Correlations (affectees) Fig. 3. Affectees distribution ammp00 bzip200 crafty00 fma3d00 gcc95 go95 ijpeg95 mcf00 mesa00 twolf00 vpr00 determines the outcome of a branch can be found in less than 30 out of the most recent 400 basic blocks preceded by a conditional branch. The outlier is mcf where many branches have large number of affectors. The data about affectees correlations show clearly that for most programs 50% of the branches have 30 or more affectees. This means that a branch frequently checks information that partially or fully has been tested by at least 30 other out of the 400 most recent branches. The data also show few benchmarks, bzip, ijpeg, vpr to have 300 or more affectee correlations. It is noteworthy that mcf00, that has branches with many affectors, has also many branches, about 50%, with 0 affectees. This occurs because mcf loads and tests data from many memory locations where no correlation to the producers can be found within the least 400 branches. The
The Significance of Affectors and Affectees Correlations 251 100% 75% 50% 25% 0% 100% 75% 50% 25% 0% 100% 75% 50% 25% 0% (a) (b) (c) Fig. 4. Most frequent correlation patterns for (a)twolf00, (b)bzip00 and (c)ammp00 graph (not shown due to space) for the distribution of the branches when we consider both affectors and affectees is very similar to the one for the affectees. Overall the data show that for ALL benchmarks there are many branches that have much less than maximum number correlations. Provided: (a) affectors and affectees are the dominant types of correlation that predictors need to learn, and (b) existing predictors are unable to use only the relevant part of history, then these data suggest that there may be room for improving prediction. In Fig. 4 we attempt to give more insight by presenting the dominant patterns of correlation when we consider the combination of affectors and affectees. The figure shows for three benchmarks, twolf, bzip and ammp what are the most frequent 1000 patterns of correlations. To help the reader we present these top patterns sorted from top to bottom according to the oldest position with a correlation (i.e. the most recent correlation position is to the right). The curve that cut-across each graph represents from top to bottom the cumulative branch distribution of the patterns. This line is not reaching 100% since we only display the top 1000 patterns. A given pattern has a gray and white part representing the bit positions with and without correlations. To help the reader we present patterns with 100 positions where each position corresponds to 4 bits (a position is set to one if any of its corresponding four bits is set). These three graphs are representative of 10 of the 11 benchmarks we considered in this paper. Benchmark twolf is representative of crafty, vpr, mesa, gcc and go, bzip of mcf and
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Lecture Notes in Computer Science 4
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Volume Editors Per Stenström Chalm
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VI Preface The planning of a confer
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VIII Organization Mahmut Kandemir P
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Invited Program Table of Contents S
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Table of Contents XIII Turbo-ROB: A
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4 M. Valero and J. Labarta future s
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MIPS MT: A Multithreaded RISC Archi
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2.2 VPEs as Scheduling Domains MIPS
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MIPS MT: A Multithreaded RISC Archi
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6.1 Thread Context Virtualization M
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8 Experimental Results MIPS MT: A M
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Cycles/ Iteration MIPS MT: A Multit
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9 Conclusions MIPS MT: A Multithrea
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MPI: Message Passing on Multicore P
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overhead per word (cycles) 1200 100
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speedup 3.5 3 2.5 2 1.5 1 0.5 0 rMP
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speedup 9 8 7 6 5 4 3 2 1 0 rMPI: M
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Modeling Multigrain Parallelism on
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BRAM-LUT Tradeoff on a Polymorphic
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32 L0 L1 BRAM-LUT Tradeoff on a Pol
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Architecture Enhancements for the A
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Implementation of an UWB Impulse-Ra
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Fast Bounds Checking Using Debug Re
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Studying Compiler Optimizations on
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avg normalized execution time 1.000
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CLI as an Effective Deployment Form
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Compilation Strategies for Reducing
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180% 160% 140% 120% 100% 80% 60% 40
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Experiences with Parallelizing a Bi
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Drug Design Issues on the Cell BE 1
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speed-up 8 6 4 2 0 FFT3D 256 64-A F
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COFFEE: COmpiler Framework for Ener
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COFFEE: COmpiler Framework for Ener
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6.1 Filling Edge Profile from Verte
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Complementing Missing and Inaccurat
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Using Dynamic Binary Instrumentatio
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L1 D-Cache Miss Rate (%) CPI 5 4 3
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CPI Error (%) 10 5 0 -5 -10 FP Resu
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CPI Error (%) 40 30 20 10 0 Using D
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Phase Complexity Surfaces: Characte
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( log10) phases of Number 4.0 3.5 3
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MLP-Aware Dynamic Cache Partitionin
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(a) IPC as we vary the number of as
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Compiler Techniques for Reducing Da
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40 35 30 25 ) te ( % 20 -ra M is s
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References Compiler Techniques for
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Code Arrangement of Embedded Java V
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Aggressive Function Inlining: Preve
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f1(){ ...g();....} f2(){....q();...
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foo:(80 times) BB1: Call bar BB2: C
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400 Author Index Shajrawi, Yousef 3
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Lecture Notes in Computer Science 4917

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