Lecture Notes in Computer Science 4917

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Aggressive Function Inlining: Preventing Loop Blockings 395 1.00 0.90 0.80 0.70 0.60 0.50 0.40 Fraction of L1 I-cache fetches reduced on CINT2000 (lower is better) Code Reordering Aggressive Inlining gzip gcc crafty eon vortex twolf vpr mcf parser gap bzip2 Avg. Fig. 8. Amount of L1 Icache fetches reduced on CINT2000 (lower is better) 2.00 1.50 1.00 0.50 0.00 Fraction of branch target mispredictions reduced on CINT2000 (lower is better) Code Reordering Aggressive Inlining gzip gcc crafty eon vortex twolf vpr mcf parser gap bzip2 Avg. Fig. 9. Amount of branch target mispredictions reduced on CINT2000 (lower is better) of the IBM PCIX Cryptographic Coprocessor. We tested the csulcca (Common Support Utility Linux Common Cryptographic Architecture) application and obtained 2% improvements over code reordering due to the use of inlining. 5 Related Work Methods for selective inlining have been studied and implemented in the last 10 years. These works should be separated from the works in code reordering wherein inlining is only a pre-stage to code reordering. The goal of code reordering is to rearrange code segments to minimize Icache misses. As explained in Section 1 this differs from the the goal of selecting “safe” yet aggressive inlinings. Scheifler [12] proposed to inline functions based on: a) function size, b) number of calls versus function size and c) dominant calls. Scheifler showed that computing optimal inlining is at least NP hard. Ball [4] proposed to inline functions based on their utility for constant propagation and other optimizations.
396 Y. Ben Asher et al. McFarling [9] describes a statistical method to predict which calls should be inlined. The method is based on the distribution of relative “hotness” of calls versus their size. This method refines the method of inlining “hot” functions. The method proposed here differs in several aspects: – it does not depend on a cache model nor does it depend on the size of the inlined functions. – it considers the structure of the input program (preventing duplication in hot cycles). whereas [9] scheme is based on statistical predictions. [8] considers the issue of selecting among different “inlined versions” of a given function. Different versions of a function f() are created during the inline processes as a result of different inline decisions of functions called from f(). For example, for f(){ ...; g(); h(); ...} there are four immediate versions: the original f(){...}, f(){ ...; inlined g(); h(); ...}, f(){ ...; g(); inlined h(); ...} and f(){ ...; inlined g(); inlined h(); ...}. Three methods are compared: cv- use maximally inlined version, ov- original version of the callee and current version of the caller and av- use any version. There is a correspondence between these methods and actual inlining systems such as the one used in GCC or in [12]. A probabilistic model to estimate the benefit of inlining given call sequences is devised. This model is based entirely on calling frequencies. Based on this model and experimental results the authors show that the ov- method is at least as powerful as the other two options. The method proposed here is orthogonal to the classification used in [8] as it is based on the structural properties of the call-graph and thus, any combination such as av-ILB, ov-ILP and cv-ILB can be devised. Ayers et al. [3] and Das [5] found an analogy between the code expansion problem and the Knapsack problem. They used this analogy to help identify appropriate candidates for function inlining. Arnold et al. [1] tried to find the best candidates that fit a given code size budget for the Jalapeño dynamic optimizing compiler. Way et al. [15] suggest different inlining method that are based on the idea of region-based analysis. The use of regions is designed to enable the compiler to operate on reduced control flow graphs of inlined functions before applying the register allocation and scheduling algorithms. The formation of a region is guided by profiling information and is similar to the way the code reordering algorithm determines the new order of the basic blocks. Their heuristics reduce compilation complexity but are bound by code size limitations. For function inlining optimization, Way et al. [14] describe a new profile-based method for determining which functions to inline to avoid cases of code bloat, by collecting path profiling information on the call graph of the program. Another work in this direction is [16] introducing two heuristics for the ORC compiler called “adaptation” and “cycle density”. Both methods are based on temperature and size, e.g, cycle density prevent inlining of hot functions whose calling frequency is low. The post-link tools PLTO [13] and Alto [10] address the issue of code inlining as part of their post-link optimizations. PLTO uses a cache model for determining which functions to inline (similar to McFarling’s [9]). We have also chosen to
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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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Turbo-ROB: A Low Cost Checkpoint/Re
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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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280 A. García et al. Target Loop E
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282 A. García et al. reduction 100
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284 A. García et al. IPC speedup 7
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286 A. García et al. SPECfp benchm
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Complementing Missing and Inaccurat
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6.1 Filling Edge Profile from Verte
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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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Page 406: 400 Author Index Shajrawi, Yousef 3
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