2023
Kernel Tuning Toolkit
PETROVIČ, Filip and Jiří FILIPOVIČBasic 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
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
References:
Impact factor
Impact factor: 2.400
RIV identification code
RIV/00216224:14610/23:00130522
Organization unit
Institute of Computer Science
UT WoS
000966092000001
EID Scopus
2-s2.0-85151460189
Keywords in English
Autotuning; GPU optimization; CUDA; OpenCL; Vulkan
Tags
International impact, Reviewed
Changed: 5/4/2024 11:07, Mgr. Alena Mokrá
Abstract
In the original language
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 |
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