Kernel Tuning Toolkit

PETROVIČ, Filip and Jiří FILIPOVIČ. Kernel Tuning Toolkit. SoftwareX. Elsevier, 2023, vol. 22, neuveden, p. 1-6. ISSN 2352-7110. Available from: https://dx.doi.org/10.1016/j.softx.2023.101385.

Basic information

• Original name: Kernel Tuning Toolkit
• Authors: PETROVIČ, Filip (703 Slovakia, belonging to the institution) and Jiří FILIPOVIČ (203 Czech Republic, guarantor, belonging to the institution).
• Edition: SoftwareX, Elsevier, 2023, 2352-7110.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Netherlands
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 3.400 in 2022
• RIV identification code: RIV/00216224:14610/23:00130522
• Organization unit: Institute of Computer Science
• Doi: http://dx.doi.org/10.1016/j.softx.2023.101385
• UT WoS: 000966092000001
• Keywords in English: Autotuning; GPU optimization; CUDA; OpenCL; Vulkan
• Tags: J-Q2, rivok
• Tags: International impact, Reviewed

Abstract

Kernel Tuning Toolkit (KTT) is an autotuning framework for CUDA, OpenCL and Vulkan kernels. KTT provides advanced autotuning features such as support for both dynamic (online) and offline tuning, and an ability to tune multiple kernels together with shared tuning parameters. Furthermore, it offers customization features that make integration into larger software suites possible. The framework handles all major steps required for autotuning implementation, including configuration space creation and exploration, kernel code execution and output validation. The public API is available natively in C++ and via bindings in Python.

Links

• LM2018140, research and development project: Name: e-Infrastruktura CZ (Acronym: e-INFRA CZ)

Investor: Ministry of Education, Youth and Sports of the CR