Lecture Notes in Computer Science 4917

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Phase Complexity Surfaces: Characterizing Time-Varying Program Behavior 331 and 512M) — there are 24 characteristics in total. Intuitively speaking, we sample the phase complexity surface. This yields a data matrix with the rows being the benchmarkinput pairs and the columns being the 24 phase characteristics. This data matrix serves as input to Principal Components Analysis (PCA) [15] — the goal of PCA is (i) to remove corand a given clustering threshold, relation from the data set and (ii) to reduce the dimensionality. PCA computes new dimensions, called principal components, which are linear combinations of the original phase characteristics. In other words, PCA tranforms the p =24phase characteristics X1,X2,...,Xp into p principal components Z1,Z2,...,Zp with Zi = �p j=1 aijXj. This transformation has the properties (i) Var[Z1] ≥ Var[Z2] ≥ ... ≥ Var[Zp] — this means Z1 contains the most information and Zp the least; and (ii) Cov[Zi,Zj] =0, ∀i �= j — this means there is no information overlap between the principal components. Some principal components have a higher variance than others. By removing the principal components with the lowest variance from the analysis, we reduce the dimensionality of the data set while controlling the amount of information that is thrown away. On our data set we retain 3 principal components that collectively explain 87.4% of the total variance in the original data set. Note that prior to PCA we normalize the data matrix (the columns have a zero mean and variance of one) to put all characteristics on a common scale; also after PCA, we normalize the principal components to give equal weight to the underlying mechanisms extracted by PCA. We now have a reduced data matrix, i.e., we are left with three principal component values for all benchmark-input pairs. This reduced data set now serves as input to cluster analysis which groups benchmark-input pairs that exhibit similar phase behavior. We use linkage clustering here because it allows to visualize the clustering through a dendrogram. Linkage clustering starts with a matrix of distances between the benchmarks. As a starting point for the algorithm, each benchmark is considered as a group. In each iteration of the algorithm, groups that are closest to each other are merged and groups are gradually merged until we are left with a single group. This can be represented in a so called dendrogram, which graphically represents the linkage distance for each group merge at each iteration of the algorithm. Having obtained a dendrogram, it is up to the user to decide how many clusters to take. This decision can be made based on the linkage distance. Indeed, small linkage distances imply strong clustering while large linkage distances imply weak clustering. There exist several methods for calculating the distance between clusters. In this paper we use the weighted pair-group average method which computes the distance between two clusters as the weighted average distance between all pairs of program-input points in the two different clusters. The weighting of the average is done by considering the cluster size, i.e., the number of program-input points in the cluster. 6.2 Results Figure 4 shows the dendrogram obtained from clustering the benchmarks based on their phase behavior. Classifying the benchmarks using this dendrogram with a critical threshold of 2.5, results in four major clusters representing the most diverse phase behaviors across the SPEC CPU2000 benchmarks, see also Table 1. Note that in case a more fine-grained distinction needs to be made among the benchmarks in terms of their
332 F. Vandeputte and L. Eeckhout 3,5 3.5 3,0 3.0 2,5 2.5 2,0 2.0 1,5 1.5 1,0 1.0 0,5 0.5 0,0 0.0 MCF mcf gcc-integrate gcc-expr parser gcc-scilab gcc-200 vpr-place perl-make wupwise sixtrack twolf eon-rush mesa eon-kajiya vortex-2 gzip-source gzip-program vortex-3 vortex-1 perl-diff art-470 art-110 gzip-graphic gzip-random eon-cook vpr-route crafty swim fma3d equake applu mgrid facerec galgel apsi lucas gap ammp perl-split perl-split perl-split perl-split gzip-log bzip-source bzip2-program gcc-166 bzip2-graphic GCC.EXPR GCC.SCIL GC VPR1 PA WUPWISE GC TWOLF PE MESA SIX VORTEX2 EO GZIP.PRO EO VORTEX1 GZ ART2 VO GZIP.GRA PER cluster 1 cluster 2 cluster 3 cluster 4 EON2 AR CRAFTY GZ FMA3D VP APPLU SW FACEREC EQ APSI MG GAP GA Fig. 4. Dendrogram visualizing the clustering Table 1. Classifying benchmarks in terms of their phase behavior; cluster representatives are shown in bold ID benchmarks 1 gcc-200, gcc-scilab, gcc-expr, gcc-integrate, parser, mcf 2 eon-kajiya, eon-rush, mesa, twolf, sixtrack, wupwise, perl-make, vpr-place 3 crafty, vpr-route, eon-cook, gzip-program, gzip-source, gzip-graphic, gzip-random,art, perl-diff, vortex 4 bzip2, gcc-166, gzip-log, perl-split, ammp, gap, lucas, apsi, galgel, facerec, mgrid, applu, equake, fma3d, swim phase behavior, the critical threshold should be made smaller; this will result in more fine-grained types of phase behavior. We observe the following key phase characteristics in each of the four major clusters: – cluster 1 :: very poor phase predictability and a very large number of phases – cluster 2 :: very small number of phases and very good phase predictability; – cluster 3 :: a relatively poor predictability and a high number of phases at small time granularities, in combination with relatively better predictability and relatively fewer phases at large time granularities; – cluster 4 :: a moderate number of phases across all time granularities, and mostly good to excellent predictability. PERL.3.S LU PERL.4.S AM GZIP.LOG PER BZIP2.PR PER BZIP2.GR BZI GC
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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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Benchmark Source Code * transformed
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RTL for each Component * Gate−lev
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Normalized Cycle Count 1.00 0.90 0.
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Integrated CPU Cache Power Manageme
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Variation-Aware Software Techniques
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Average leakage-power saving (%) Va
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244 Y. Sazeides et al. of mispredic
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246 Y. Sazeides et al. Affector BB1
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248 Y. Sazeides et al. 2.3 How to U
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250 Y. Sazeides et al. Cumulative D
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252 Y. Sazeides et al. ijpeg, andbo
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254 Y. Sazeides et al. Misses/KI 12
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256 Y. Sazeides et al. Mahlke and N
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260 P. Akl and A. Moshovos Original
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262 P. Akl and A. Moshovos Oldest I
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264 P. Akl and A. Moshovos new firs
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266 P. Akl and A. Moshovos throttli
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268 P. Akl and A. Moshovos 80% 70%
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270 P. Akl and A. Moshovos 25% 20%
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272 P. Akl and A. Moshovos [4] Burg
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274 A. García et al. execution tim
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276 A. García et al. whenever LPA
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278 A. García et al. @12: load r2,
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Code Arrangement of Embedded Java V
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Aggressive Function Inlining: Preve
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400 Author Index Shajrawi, Yousef 3
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