Keywords

Software productivity; code refactoring; software x-ray scanner; HPC applications

Start Date

25-6-2018 9:00 AM

End Date

25-6-2018 10:20 AM

Abstract

The complexity of large scientific models developed for certain machine architectures and application requirements has become a real barrier that impedes continuous software development. In this study, we use experience from several practices, including open-source software engineering, software dependency understanding, compiler technologies, analytical performance modeling, micro-benchmarks, and functional unit testing, to design software toolkits to enhance software productivity and performance. Our software tools collect the information on scientific codes and extract the common features of these codes. In this paper, we focus on the front-end of our system (Software X-ray Scanner): a metric information collection system for better understanding of key scientific functions and associated dependency. We use several science codes from the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program, Exascale Computing Projects (ECPs), Subsurface Biogeochemical Research (SBR) to explore cost-efficient approaches for program understanding and code refactoring. The toolkits increase the software productivity for the Interoperable Design of Extreme-scale Application Software (IDEAS) community which is supported by both US Department of Energy’s Advanced Scientific Computing Research (ASCR) and Biological and Environmental Research (BER) programs. We expect that these toolkits can benefit broader scientific communities that are facing similar challenges.

Stream and Session

Stream A: Advanced Methods and Approaches in Environmental Computing

A1: Towards More Interoperable, Reusable and Scalable Environmental Software

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Jun 25th, 9:00 AM Jun 25th, 10:20 AM

An Application Software Analytics Toolkit for Facilitating the Understanding, Componentization, and Refactoring of Large-Scale Scientific Models

The complexity of large scientific models developed for certain machine architectures and application requirements has become a real barrier that impedes continuous software development. In this study, we use experience from several practices, including open-source software engineering, software dependency understanding, compiler technologies, analytical performance modeling, micro-benchmarks, and functional unit testing, to design software toolkits to enhance software productivity and performance. Our software tools collect the information on scientific codes and extract the common features of these codes. In this paper, we focus on the front-end of our system (Software X-ray Scanner): a metric information collection system for better understanding of key scientific functions and associated dependency. We use several science codes from the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program, Exascale Computing Projects (ECPs), Subsurface Biogeochemical Research (SBR) to explore cost-efficient approaches for program understanding and code refactoring. The toolkits increase the software productivity for the Interoperable Design of Extreme-scale Application Software (IDEAS) community which is supported by both US Department of Energy’s Advanced Scientific Computing Research (ASCR) and Biological and Environmental Research (BER) programs. We expect that these toolkits can benefit broader scientific communities that are facing similar challenges.