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  1. Proceedings of the Fourth International Workshop on Software Engineering for HPC in Computational Science and Engineering (SE-HPCCSE '16)
  2. Code complexity versus performance for GPU-accelerated scientific applications
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Towards an empirical study design for concurrent software testing
A case study: test-driven development in a microscopy image-processing project
Computational efficiency vs. maintainability and portability. experiences with the sparse grid code SG++
The scalability-efficiency/maintainability-portability trade-off in simulation software engineering: examples and a preliminary systematic literature review
Single-sided statistic multiplexed high performance computing
Advantages, disadvantages and misunderstandings about document driven design for scientific software
Towards automatic and flexible unit test generation for legacy HPC code
Code complexity versus performance for GPU-accelerated scientific applications

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Code complexity versus performance for GPU-accelerated scientific applications

Content Provider ACM Digital Library
Author Munipala, A WK. Umayanganie Moore, Shirley V.
Abstract Graphics Processing Units (GPUs) are becoming widely used as parallel accelerators in high-performance computing. GPU programming until recently, has been done by using low-level programming models such as CUDA and OpenCL. The directive-based OpenACC programming model has been growing in popularity due to its higher level of abstraction. This technique, which uses "directive" or "pragma" statements to annotate source code written in traditional high-level languages such as Fortran, C, and C++, is intended to allow a single code base to work across multiple computational platforms. We attempt to compare code complexity and performance of CUDA, OpenCL, and OpenACC implementations for three benchmark codes - the Game of Life (GOL) example code, the LULESH hydrodynamics proxy application, and the CloverLeaf mini-app from the Mantevo suite For the GOL C, CUDA C, and OpenCL codes and the LULESH C++, CUDA, and OpenCL codes, we measured source lines of code (SLOC) and cyclomatic complexity using the Oxbow toolkit static analysis tools. We ran the commercial McCabe IQ tool on the CloverLeaf Fortran90, Fortran90 + OpenACC, and Fortran portion of CloverLeaf_CUDA to measure cyclomatic complexity, design complexity, and essential complexity. Essential complexity measures the degree of structuredness and is used to predict the maintenance effort. We used the Oxbow tools to measure cyclomatic complexity of the CUDA portion of CloverLeaf_CUDA. We obtained the runtimes by using internal timers that called either gettimeofday() or MPI_Wtime(). The timings did not include initialization and setup parts of the codes. The distribution of cyclomatic complexities for the CUDA and OpenACC versions of LULESH are shown in Figures 1 and 2. The CUDA versions tend to have a larger number of functions with low cyclomatic complexity since CUDA kernels have few branches. The software engineering community has developed metrics that are used to assess design and maintenance costs and risks for large software development projects. Our research is a first step at assessing how well some of these metrics apply to GPU programming.
Starting Page 50
Ending Page 50
Page Count 1
ISBN 9781509052240
DOI 10.1109/SE-HPCCSE.2016.12
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-11-13
Access Restriction Subscribed
Content Type Text
Resource Type Article
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