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CALIPER: A Coarse Grain Parallel Performance Estimator and Predictor

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Emerging Technologies in Computing (iCETiC 2020)

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Abstract

Empirical studies of Program Performance, are limited by the choice and the resulting bias, from the input samples used in the experiment. Estimation and Prediction based on static analysis, are more universal, superior and widely accepted. However the higher language artifacts such as Procedures, Loops, Conditionals and Recursion which ease program development can be an hindrance to quality analysis and performance study, both in terms of time and effort spent and in some extreme cases making it impractical. However, we could transform the program, eliminate the constraints imposed by these program structures and greatly ease the process of quality analysis and performance study. This process may also reduce the errors in the estimation, and help deliver timely results, when there is still an opportunity to use them in a later analysis phase. We propose transformations prior to estimation, such as Procedure Call Expansion, Loop Unrolling and Control Predication collectively referred to as Program Shape Flattening here with the structural hindrances themselves referred to as the Program Shape. The outcome of this transformation, is sequential code that is easy to work with. Specifically, for parallel performance estimations, we now have code that is free from Control Dependencies. We use the concept of Equivalence Classes to group statements based on their Data Dependence behavior. Statements that belong to an Equivalence Class are mutually dependent directly or transitively. On the other hand statements that belong to separate Equivalence Classes are dependence free and can be run in parallel without compromising on the program correctness. With this arrangement of program statements we claim that the program run time is now equal to the run time of the class that runs the longest. While this scheme of grouping program instructions, can be viewed as a method of parallel conversion, we use this method here specifically for parallel performance estimation and prediction. After surveying the published literature, and searching for similar commercial products, we did not find a comparable technology, to assess the contributions made by Caliper, at the time of writing, and so we claim that Caliper is the only product of its kind today.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, R. V. College of Engineering, VTU, Bangalore, India

    Sesha Kalyur & G.S Nagaraja

Authors
  1. Sesha Kalyur
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  2. G.S Nagaraja
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Correspondence to Sesha Kalyur .

Editor information

Editors and Affiliations

  1. CFRED, Chinese University of Hong Kong, Hong Kong, China

    Mahdi H. Miraz

  2. Professor Emeritus, Wrexham Glyndwr University, Bradford, UK

    Peter S. Excell

  3. Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd, Mid Glamorgan, UK

    Andrew Ware

  4. AMA International University, Salmabad, Bahrain

    Safeeullah Soomro

  5. Epoka University, Tirana, Albania

    Maaruf Ali

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Kalyur, S., Nagaraja, G. (2020). CALIPER: A Coarse Grain Parallel Performance Estimator and Predictor. In: Miraz, M.H., Excell, P.S., Ware, A., Soomro, S., Ali, M. (eds) Emerging Technologies in Computing. iCETiC 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 332. Springer, Cham. https://doi.org/10.1007/978-3-030-60036-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-60036-5_2

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