Architecture of Computing Systems (Lecture Notes in Computer ...

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192 M. Schindewolf, D. Kramer, and M. Cintra latch edge preheader preheader edge header loop body next basic block exit edge Fig. 2. GCC internal representation of a single exit loop the already described identifier and the value of the loop count (if available from static analysis). Operation Count Analysis. The ma_insert pass investigates the basic blocks. Each statement is examined whether it contains: function calls, floating point or integer operations, or load/store operations. The obtained values are added to operation count data structures. Additionally variable est_cnt holds the estimated number of times a basic block is executed. The results from the iteration analysis of the loops (as far as available) are stored into this variable. Nested loops lead to a multiplication of the trip counts. Loops with indeterminable number of iterations are assumed to be executed ma_loop times. Hence, the user setting the ma_loop parameter influences the effect of such loops on the model. Further, our approach relies on the fact that functions are compiled one at a time. We take advantage of the fact that functions are compiled first, hence, the results from the operation count analysis are made available to later passes. This operation count data gathered by visiting the basic blocks are saved in a per function statistic. This allows the computation of an overall statistic including all compiled functions. The main function is processed last. When the main function is compiled, the global statistic is complete as far as the data is available 1 . The processing of the data evaluates the relative contribution of the functions to the statistics and inserts them into a sorted array. This array is pruned until all remaining functions or basic blocks meet the user constraints passed with the compiler options. The ma_insert pass traverses the array and annotates the remaining parts. Naming Scheme. A naming scheme (using unique names) ensures that annotated call expressions are distinguishable at run time. This naming scheme enables correlating measured values to certain regions of source code, simplifying the implementation of the post-processing tools. Besides, the naming scheme enables an optimized generation of traces, by omitting redundant actions inside the runtime library. 1 Externally linked libraries and third party object files are not subject to the compiler’s operation count analysis.
Compiler-Directed Performance Model Construction 193 3.2 MA Library At run-time the executable, instrumented by the GCC-PME passes, calls the MA library (which is similar regarding the API and the use of PAPI performance counters to the one developed at the Oak Ridge National Laboratory [4]). The library serves two major purposes. First, event traces for the post-processing tools are generated. This includes calling the PAPI library, that starts, resets, reads, and stops hardware event counters. Second, it writes the annotated parts into a file. In order to enable the user to complement the compiler annotated source with manual annotations, all basic functions come with wrapper functions. These wrapper functions are necessary since FORTRAN and C follow different calling conventions. 3.3 Post-processing Tools The post-processing tools evaluate the data provided by the run time library. First, trace files generated by the library have to be prepared. Finally, MATLAB is used to visualize the data and to construct an analytical model. Validation. The validate tool prepares the data obtained from the MA library. A trace file, holding the compiler assumed values from the start calls, is merged with a file containing performance counter values from the end calls. Matching these pairs of calls is simplified by the naming scheme. The outcome of the matching and aggregation process, is written to a file and serves as input for the model construction and visualization using MATLAB. Model construction. A MATLAB program visualizes the data derived from the post-processing tool validate and creates an analytical model. The analytical model enables to predict runs with larger input classes. Floating point, integer or load/store operations are displayed in terms of predicted and measured values. This section presents the construction of the performance model using the method of quadratic programming. The intention is to run a small number of program instances with different input data sets and measure the desired values. Afterwards the quadratic programming method is used to incorporate the values into a model capable of predicting unknown program runs with larger data sets. Both, model construction as well as the prediction, rely on the knowledge of the program input parameters. The model bases on the predicted and measured values for floating point, load/store or integer operations and the input parameters of the benchmark. Since the construction does not depend on the type of information measured, we will refer to the values simply as p for predicted and m for measured. The index i for pi and mi represents the different input classes. The separate program phases are analyzed one at a time. This aims at a fine grained modelling of the phases, correlating the predicted and measured values. The annotated program parts are evaluated with different parameters. Hence, the overall prediction is more accurate. The following mathematical equations outline the problem transformation for one measured program phase. Program parameters are represented as nj with nj ∈{1,...,m}. r is the number of different input parameter sets with m parameters each. Hence, there are r potentially
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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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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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Exploiting Inactive Rename Slots fo
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130 M. Kayaalp et al. INSTRUCTION 1
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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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