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dc.contributor.authorLars, Bjertnes
dc.contributor.authorTørring, Jacob
dc.contributor.authorElster, Anne C.
dc.date.accessioned2022-07-07T08:10:01Z
dc.date.available2022-07-07T08:10:01Z
dc.date.created2021-07-20T17:05:47Z
dc.date.issued2021
dc.identifier.citationApplied Artificial Intelligence. 2021, 67-72.en_US
dc.identifier.issn0883-9514
dc.identifier.urihttps://hdl.handle.net/11250/3003390
dc.description.abstractThe effectiveness of Machine Learning (ML) methods depend on access to large suitable datasets. In this article, we present how we build the LS-CAT (Large-Scale CUDA AutoTuning) dataset sourced from GitHub for the purpose of training NLP-based ML models. Our dataset includes 19 683 CUDA kernels focused on linear algebra. In addition to the CUDA codes, our LS-CAT dataset contains 5 028 536 associated runtimes, with different combinations of kernels, block sizes and matrix sizes. The runtime are GPU benchmarks on both Nvidia GTX 980 and Nvidia T4 systems. This information creates a foundation upon which NLP-based models can find correlations between source-code features and optimal choice of thread block sizes. There are several results that can be drawn out of our LS-CAT database. E.g., our experimental results show that an optimal choice in thread block size can gain an average of 6% for the average case. We thus also analyze how much performance increase can be achieved in general, finding that in 10% of the cases more than 20% performance increase can be achieved by using the optimal block. A description of current and future work is also included.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.titleLS-CAT: A Large-Scale CUDA AutoTuning Dataseten_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber67-72en_US
dc.source.journalApplied Artificial Intelligenceen_US
dc.identifier.doi10.1109/ICAPAI49758.2021.9462050
dc.identifier.cristin1922271
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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