Solving Maxwell's Equations with Modern C++ and SYCL: A Case Study

Afzal, Ayesha; Schmitt, Christian; Alhaddad, Samer; Grynko, Yevgen; Teich, Jürgen; Förstner, Jens; Hannig, Frank

doi:10.1109/asap.2018.8445127

Cited by 7 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature generation: To extract the static code analyzer features, we used two programs: SonarQube [35] and CCCC [39,40]. These tools are widely used in the literature for the purpose of code analysis [61][62][63]. SonarQube is an automatic code review tool.…”

Section: Datasetmentioning

confidence: 99%

Efficient Feature Selection for Static Analysis Vulnerability Prediction

Filus

Boryszko

Domańska

et al. 2021

Sensors

View full text Add to dashboard Cite

Common software vulnerabilities can result in severe security breaches, financial losses, and reputation deterioration and require research effort to improve software security. The acceleration of the software production cycle, limited testing resources, and the lack of security expertise among programmers require the identification of efficient software vulnerability predictors to highlight the system components on which testing should be focused. Although static code analyzers are often used to improve software quality together with machine learning and data mining for software vulnerability prediction, the work regarding the selection and evaluation of different types of relevant vulnerability features is still limited. Thus, in this paper, we examine features generated by SonarQube and CCCC tools, to identify those that can be used for software vulnerability prediction. We investigate the suitability of thirty-three different features to train thirteen distinct machine learning algorithms to design vulnerability predictors and identify the most relevant features that should be used for training. Our evaluation is based on a comprehensive feature selection process based on the correlation analysis of the features, together with four well-known feature selection techniques. Our experiments, using a large publicly available dataset, facilitate the evaluation and result in the identification of small, but efficient sets of features for software vulnerability prediction.

show abstract

Section: Datasetmentioning

confidence: 99%

Efficient Feature Selection for Static Analysis Vulnerability Prediction

Filus

Boryszko

Domańska

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Hence, the OpenCL implementations of the benchmark suite were ported to SYCL manually. The SYCL benchmark suite is an open-source project 1 for tracking the development of the mainstream SYCL compilers [13,14,15], and for developers and researchers interested in programming productivity, performance analysis, and portability across different computing platforms [16,17,18,19,20,21,22,23,24,25,26,27].…”

Section: Introductionmentioning

confidence: 99%

The Rodinia Benchmarks in SYCL

Jin¹

2021

View full text Add to dashboard Cite

The Rodinia Benchmark Suite in SYCL was first published as a technical report at Argonne National Laboratory in June 2020. While porting more programs to SYCL, the Rodinia benchmark suites were merged into the heterogenous computing benchmarks (HeCBench). Since then, the source codes and scripts of the benchmark suite have been updated to fix compile-and run-time issues that users encountered. With the recent SYCL compilers from Codeplay and Intel, this report presents the experimental results of evaluating these benchmarks on the Intel CPU and GPU devices.I.

show abstract

“…Hence, we port the OpenCL implementations of the benchmark suite to SYCL manually. The SYCL benchmark suite is an opensource project 1 for tracking the development of the mainstream SYCL compilers [13,14,15], and for developers and researchers interested in programming productivity, performance analysis, and portability across different computing platforms [16,17,18,19,20,21,22,23,24,25,26,27].…”

Section: Introductionmentioning

confidence: 99%

The Rodinia Benchmark Suite in SYCL

Jin

2020

View full text Add to dashboard Cite

is a U.S. Department of Energy laboratory managed by UChicago Argonne, LLC under contract DE-AC02-06CH11357. The Laboratory's main facility is outside Chicago, at 9700 South Cass Avenue, Argonne, Illinois 60439. For information about Argonne and its pioneering science and technology programs, see www.anl.gov. DOCUMENT AVAILABILITYOnline Access: U.S. Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free at OSTI.GOV (http://www.osti.gov/), a service of the US Dept. of Energy's Office of Scientific and Technical Information.

show abstract

Solving Maxwell's Equations with Modern C++ and SYCL: A Case Study

Cited by 7 publications

References 18 publications

Efficient Feature Selection for Static Analysis Vulnerability Prediction

Efficient Feature Selection for Static Analysis Vulnerability Prediction

The Rodinia Benchmarks in SYCL

The Rodinia Benchmark Suite in SYCL

Contact Info

Product

Resources

About