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190 M. Schindewolf, D. Kramer, and M. Cintra On the other hand application level knowledge that is still available at compile time (names of loop variables, trip counts, origin of code fragments (source file and line), etc.) may be lost at binary level or is difficult to reconstruct. Compared with the surveyed related work based on simulation, statistical methods or incorporating an array of different models, our approach contributes compiler-directed models. In contrast to the complex model construction process favored in the related work, we restrict ourselves to present simplistic models based on quadratic programming to show the validity of the compiler-based approach. Our approach is expected to integrate with the modeling assertions approach and, thus, may greatly simplify the construction of more sophisticated performance models (e.g. symbolic models) of unknown workloads. 3 Framework The following section describes the implementation aspects of our work. Figure 1 shows the tool chain and the section is organized top-down. First, a description of the compiler passes is given. Afterwards, this section describes the implementation of the MA library sketching the actions taken at run-time. At last, the post-processing tools are covered, addressing the tool to validate predictions against information retrieved from performance counters as well as aspects of the model construction. 3.1 GCC-PME This section shows the basic design decisions for the implementation of the compiler extension. The ”PME” extension to the name GCC underlines the program modeling enhancements to the tree structure of GCC (version 4.2.0). Since the user needs some means to influence the modeling process, new compiler options are introduced. These options influence the instrumentation process so that only selected parts are instrumented. These parts are determined by an operation count pass, which visits all basic blocks and gathers information. After processing this information, the user-specified code parts remain and are instrumented by a separate instrumentation pass. Compiler Options. This section briefly introduces the additional compiler options. The optimization switch -O1 enables GCC’s loop analysis and additional PME passes rely on the results of this analysis. The options ma-float-cov, ma-int-cov, andma-ls-cov are used to specify the relative amount of floating point, integer, or load/store operations to be annotated in the executable. Additionally, the ma-call-cov annotates the function calls with a call to the MA-library containing the function name. To provide an intuitive interface for the programmer, command line values are given in per cent of the estimated total amount of operations. The amount of operations a program performs, depends on the control flow. Loops, for instance, are prominent programming constructs introducing control flow and, thus, influencing the amount of operations. Depending on the exit condition of a loop, loop bounds may be derived at compile
Compiler-Directed Performance Model Construction 191 Source Code GCC-PME instrumented executable MA library performance trace files validation MATLAB Compiletime Run-time Post Processing Tools Fig. 1. Developed tool chain to create the performance model time. To benefit from this fact, the results of GCC’s loop analysis are incorporated in the PME process. This includes the results of scalar evolutions (SCEV) and induction variables analysis (IV). In case these analysis passes fail to provide a loop bound, we fall back to a user-specified value: The ma-loop switch defines the default value that is assumed for loops whose number of iterations was not derivable from static analysis. In order to avoid over instrumentation, steer the instrumentation process and, hence, decrease time and space overhead, the user may set a threshold value (called ma-barrier). The estimated amount of operations is divided by the total estimated amount of operations and compared to the threshold value. Every function or basic block must exceed the threshhold to qualify for annotation. GCC’s infrastructure delivers information from the added passes to the user. Specific switches trigger the writing of the internal program representation into a file. These files contain the modifications carried out by the corresponding passes, enabling the user to examine the results of the instrumentation process. Taking a look at these files, the user will quickly and easily learn to adjust the additional compiler parameters to gain decent instrumentation. Loop Annotation. The first PME pass annotates every loop with a call to the MA library. Herewith the identification of the loop nesting level and the frequently executed parts of an application at run time is straightforward. The arrangement of loop components in GCC’s intermediate representation is shown in Figure 2. The preheader edge leads to the loop header and is mandatory for every loop. Loops are strongly connected parts of the control flow graph, which have exactly one entry block (header) [15]. The loop header holds the condition deciding to enter the loop body. After executing the loop body the latch edge is taken to the loop header. If the loop has multiple exits, the loop body may be left through an additional exit edge as well. The annotation of the loop adds the ma_loop_start call at the preheader edge and the ma_loop_end call to all exit edges. The start call takes three parameters: an unique identifier, variable name of the loop bound (if derivable), expected loop count (passing the corresponding variable). To complement this information the end call takes two arguments:
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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General Chair Organization Christia
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Organization IX Hartmut Schmeck Kar
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Keynote Table of Contents HyVM - Hy
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Table of Contents XIII JetBench:AnO
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How to Enhance a Superscalar Proces
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4 J. Mische et al. The Real-time Vi
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6 J. Mische et al. 4.1 Instruction
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10 J. Mische et al. % of unused pip
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26 T.B. Preußer, P. Reichel, and R
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38 P. Bellasi, W. Fornaciari, and D
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62 B. Jakimovski, B. Meyer, and E.
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74 M. Bonn and H. Schmeck Fig. 1. J
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76 M. Bonn and H. Schmeck 2.2 Node
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88 J.-P. Steghöfer et al. � �
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96 J.-P. Steghöfer et al. 1. Defin
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100 J.-P. Steghöfer et al. 19. Kim
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102 K. Kloch et al. large-scale sys
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104 K. Kloch et al. a�t� 1.0 0.
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106 K. Kloch et al. constant. This
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108 K. Kloch et al. Relative number
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110 K. Kloch et al. (a) infection r
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112 K. Kloch et al. (ii) Phase of a
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118 P. Petoumenos et al. IQ: n 4-in
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120 P. Petoumenos et al. downsizing
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122 P. Petoumenos et al. As long as
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Exploiting Inactive Rename Slots fo
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128 M. Kayaalp et al. In a supersca
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130 M. Kayaalp et al. INSTRUCTION 1
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132 M. Kayaalp et al. time. Alterna
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134 M. Kayaalp et al. Fig. 3. Numbe
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136 M. Kayaalp et al. The results o
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Efficient Transaction Nesting in Ha
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242 F. Xudong et al. Speedup 14 12
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244 F. Xudong et al. 5 Related Work
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Abdullah, Tariq 174 Alima, Luc Onan
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