Abstract
We introduce and describe PerPI, a software tool analyzing the instruction level parallelism (ILP) of a program. ILP measures the best potential of a program to run in parallel on an ideal machine – a machine with infinite resources. PerPI is a programmer-oriented tool the function of which is to improve the understanding of how the algorithm and the (micro-) architecture will interact. PerPI fills the gap between the manual analysis of an abstract algorithm and implementation-dependent profiling tools. The current version provides reproducible measures of the average number of instructions per cycle executed on an ideal machine, histograms of these instructions and associated data-flow graphs for any x86 binary file. We illustrate how these measures explain the actual performance of core numerical subroutines when measured run times cannot be correlated with the classical flop count analysis.
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References
Hennessy, J.L., Patterson, D.A.: Computer Architecture – A Quantitative Approach, 4th edn. Morgan Kaufmann (2007)
Higham, N.J.: Accuracy and Stability of Numerical Algorithms. SIAM (2002)
Langlois, P., Louvet, N.: More Instruction Level Parallelism Explains the Actual Efficiency of Compensated Algorithms. Technical Report, DALI Research Team (2007), http://hal.archives-ouvertes.fr/hal-00165020
Luk, C., Cohn, R., Muth, R., Patil, H., Klauser, A., Lowney, G., Wallace, S., Reddi, V., Hazelwood, K.: Pin: Building Customized Program Analysis Tools with Dynamic Instrumentation. In: PLDI 2005: Proceedings of the 2005 ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 190–200 (2005)
Ogita, T., Rump, S.M., Oishi, S.: Accurate Sum and Dot Product. SIAM J. Sci. Comput. 26(6), 1955–1988 (2005)
Rump, S.M.: Ultimately fast accurate summation. SIAM J. Sci. Comput. 31(5), 3466–3502 (2009)
Rump, S.M., Ogita, T., Oishi, S.: Accurate floating-point summation – part I: Faithful rounding. SIAM J. Sci. Comput. 31(1), 189–224 (2008)
Touati, S.: Towards a Statistical Methodology to Evaluate Program Speedups and their Optimisation Techniques. Technical Report, PRISM, UVSQ (2009), http://hal.archives-ouvertes.fr/hal-00356529/en/
Weaver, V., Dongarra, J.: Can Hardware Performance Counters Produce Expected, Deterministic Results? In: 3rd Workshop on Functionality of Hardware Performance Monitoring, Atlanta, GA (December 4, 2010)
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Goossens, B., Langlois, P., Parello, D., Petit, E. (2012). PerPI: A Tool to Measure Instruction Level Parallelism. In: Jónasson, K. (eds) Applied Parallel and Scientific Computing. PARA 2010. Lecture Notes in Computer Science, vol 7133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28151-8_27
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DOI: https://doi.org/10.1007/978-3-642-28151-8_27
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