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Compile-Time Library Call Detection Using CAASCADE and XALT

  • Jisheng Zhao
  • Oscar R. HernandezEmail author
  • Reuben D. Budiardja
  • M. Graham Lopez
  • Vivek Sarkar
  • Jack C. Wells
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11203)

Abstract

CAASCADE Compiler-Assisted Application Source Code Analysis and DatabasE—is a tool that summarizes the use of parallel programming language features in application source code using compiler technology. This paper discusses the library detection capability within CAASCADE to find information about the usage of scientific libraries within the source code. The information that CAASCADE collects provides insights into the usage of library calls in an applications. CAASCADE can classify the APIs by scientific libraries (e.g. LAPACK, BLAS, FFTW, etc). It can also detect the context in which a library API is being invoked, for example within a serial or multi-threaded region. To collect this information, CAASCADE uses compiler plugins that summarize procedural information and uses Apache Spark to do inter-procedural analysis to reconstruct call chains. In addition to this, we also integrated CAASCADE to work with XALT to collect library information based on linkage and modules installed on a system.

Keywords

HPC scientific libraries Source code analysis Libraries usage in applications 

Notes

Acknowledgment

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DE-AC05-00OR22725. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This project is sponsored by the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy via the LDRD project 8277: “Understanding HPC Applications for Evidence-based Co-design”.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jisheng Zhao
    • 2
  • Oscar R. Hernandez
    • 1
    Email author
  • Reuben D. Budiardja
    • 1
  • M. Graham Lopez
    • 1
  • Vivek Sarkar
    • 2
  • Jack C. Wells
    • 1
  1. 1.Oak Ridge National LaboratoryOak RidgeUSA
  2. 2.Georgia TechAtlantaUSA

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