Zobrazeno 1 - 8
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pro vyhledávání: '"Tørring, Jacob O."'
Bayesian optimization is a powerful method for automating tuning of compilers. The complex landscape of autotuning provides a myriad of rarely considered structural challenges for black-box optimizers, and the lack of standardized benchmarks has limi
Externí odkaz:
http://arxiv.org/abs/2406.17811
Autor:
Tørring, Jacob O., van Werkhoven, Ben, Petrovic, Filip, Willemsen, Floris-Jan, Filipovic, Jirí, Elster, Anne C.
As computing system become more complex, it is becoming harder for programmers to keep their codes optimized as the hardware gets updated. Autotuners try to alleviate this by hiding as many architecture-based optimization details as possible from the
Externí odkaz:
http://arxiv.org/abs/2303.08976
Autor:
Tørring, Jacob O., Elster, Anne C.
Modern computing systems are increasingly more complex, with their multicore CPUs and GPUs accelerators changing yearly, if not more often. It thus has become very challenging to write programs that efficiently use the associated complex memory syste
Externí odkaz:
http://arxiv.org/abs/2203.13577
Autor:
Willemsen, Floris-Jan, Schoonhoven, Richard, Filipovič, Jiří, Tørring, Jacob O., van Nieuwpoort, Rob, van Werkhoven, Ben
Publikováno v:
In Future Generation Computer Systems October 2024 159:489-504
The 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 mod
Externí odkaz:
http://arxiv.org/abs/2103.14409
Publikováno v:
Norsk Informatikkonferanse; 2021, Issue 1, p1-14, 14p
Publikováno v:
Norsk Informatikkonferanse; 2021, Issue 1, p1-14, 14p