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Kernel Tuner
Kernel Tuner greatly simplifies the development of highly-optimized and auto-tuned CUDA, OpenCL, and C code, supporting many advanced use-cases and optimization strategies that speed up the auto-tuning process.
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Cite this software
What Kernel Tuner can do for you
- Allows developers to easily unit test and auto-tune GPU code
- Generic auto-tuning of user-defined parameters for CUDA, OpenCL, and C kernels
- Supports more than 20 different search optimization methods to speedup tuning
- Successfully used in 10+ different eScience projects, across various disciplines
Kernel Tuner simplifies the development of efficient GPU programs, or kernels. It does so by making kernels written in C/C++, OpenCL, or CUDA accessible from Python, while taking care of the required synchronization between data kept in host memory and data kept in device memory.
This has a number of advantages. First, it simplifies auto-tuning of the kernel parameters. In fact, Kernel Tuner comes standard with a variety of strategies for efficiently searching the parameter space, leading to greatly improved performance of tuned kernels. Second, it allows for unit testing of GPU code from within Python.
Kernel Tuner does not add any additional dependencies to the kernel code, and does not require extensive code changes. Furthermore, it is noteworthy that kernels tuned by Kernel Tuner do not require any changes after tuning to make them production ready--tuned kernels can be used as-is from any host programming language.
Keywords
Big data
GPU
High performance computing
Multi-scale & multi model simulations
Optimized data handling
Real time data analysis
Programming language
License
- Apache-2.0
</>Source code
Participating organisations
Reference papers
Conference papers
2- 1.Going green: optimizing GPUs for energy efficiency through model-steered auto-tuningAuthor(s): Richard Schoonhoven, Bram Veenboer, Ben Van Werkhoven, K. Joost BatenburgPublished in 2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) by IEEE in 202210.1109/pmbs56514.2022.00010
- 2.Bayesian Optimization for auto-tuning GPU kernelsAuthor(s): Floris-Jan Willemsen, Rob van Nieuwpoort, Ben van WerkhovenPublished in 2021 International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) by IEEE in 202110.1109/pmbs54543.2021.00017
Journal articles
2- 1.Benchmarking Optimization Algorithms for Auto-Tuning GPU KernelsAuthor(s): Richard Arnoud Schoonhoven, Ben van Werkhoven, Kees Joost BatenburgPublished in IEEE Transactions on Evolutionary Computation by Institute of Electrical and Electronics Engineers (IEEE) in 2023, page: 550-56410.1109/tevc.2022.3210654
- 2.Kernel Tuner: A search-optimizing GPU code auto-tunerAuthor(s): Ben van WerkhovenPublished in Future Generation Computer Systems by Elsevier BV in 2019, page: 347-35810.1016/j.future.2018.08.004
Mentions
Versioned documentation using only GitHub actions and GitHub pages
Author(s): Ben van Werkhoven
Published by Netherlands eScience Center in 2022
Kernel Tuner tutorial at Supercomputing 2021
Author(s): Ben van Werkhoven
Published in 2021
Writing Testable GPU Code
Author(s): Ben van Werkhoven
Published in 2018
Book section
5- 1.Alpinist: An Annotation-Aware GPU Program OptimizerAuthor(s): Ömer Şakar, Mohsen Safari, Marieke Huisman, Anton WijsPublished in Tools and Algorithms for the Construction and Analysis of Systems, Lecture Notes in Computer Science by Springer International Publishing in 2022, page: 332-35210.1007/978-3-030-99527-0_18
- 2.To Filter Prune, or to Layer Prune, That Is the QuestionAuthor(s): Sara Elkerdawy, Mostafa Elhoushi, Abhineet Singh, Hong Zhang, Nilanjan RayPublished in Computer Vision – ACCV 2020, Lecture Notes in Computer Science by Springer International Publishing in 2021, page: 737-75310.1007/978-3-030-69535-4_45
- 3.Exploiting Historical Data: Pruning Autotuning Spaces and Estimating the Number of Tuning StepsAuthor(s): Jaroslav Oľha, Jana Hozzová, Jan Fousek, Jiří FilipovičPublished in Euro-Par 2019: Parallel Processing Workshops, Lecture Notes in Computer Science by Springer International Publishing in 2020, page: 295-30710.1007/978-3-030-48340-1_23
- 4.Lessons Learned in a Decade of Research Software Engineering GPU ApplicationsAuthor(s): Ben van Werkhoven, Willem Jan Palenstijn, Alessio ScloccoPublished in Lecture Notes in Computer Science, Computational Science – ICCS 2020 by Springer International Publishing in 2020, page: 399-41210.1007/978-3-030-50436-6_29
- 5.Accelerating Overlapping Community Detection: Performance Tuning a Stochastic Gradient Markov Chain Monte Carlo AlgorithmAuthor(s): Ismail El-Helw, Rutger Hofman, Henri E. BalPublished in Euro-Par 2020: Parallel Processing, Lecture Notes in Computer Science by Springer International Publishing in 2020, page: 510-52610.1007/978-3-030-57675-2_32
Computer programs
4Conference papers
21- 1.Towards a Benchmarking Suite for Kernel TunersAuthor(s): Jacob O. Tørring, Ben van Werkhoven, Filip Petrovč, Floris-Jan Willemsen, Jiří Filipovič, Anne C. ElsterPublished in 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) by IEEE in 202310.1109/ipdpsw59300.2023.00124
- 2.Revisiting Temporal Blocking Stencil OptimizationsAuthor(s): Lingqi Zhang, Mohamed Wahib, Peng Chen, Jintao Meng, Xiao Wang, Toshio Endo, Satoshi MatsuokaPublished in Proceedings of the 37th International Conference on Supercomputing by ACM in 202310.1145/3577193.3593716
- 3.Enable Energy Efficient Data CentersAuthor(s): Maximilian Höb, Dieter KranzlmüllerPublished in Proceedings of the HPC Asia 2023 Workshops by ACM in 202310.1145/3581576.3581601
- 4.PERKS: a Locality-Optimized Execution Model for Iterative Memory-bound GPU ApplicationsAuthor(s): Lingqi Zhang, Mohamed Wahib, Peng Chen, Jintao Meng, Xiao Wang, Toshio Endo, Satoshi MatsuokaPublished in Proceedings of the 37th International Conference on Supercomputing by ACM in 202310.1145/3577193.3593705
- 5.Performance Tuning for GPU-Embedded Systems: Machine-Learning-Based and Analytical Model-Driven Tuning MethodologiesAuthor(s): Adrián P. Diéguez, Margarita Amor LópezPublished in 2023 IEEE 35th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) by IEEE in 202310.1109/sbac-pad59825.2023.00022
- 6.Kernel Launcher: C++ Library for Optimal-Performance Portable CUDA ApplicationsAuthor(s): Stijn Heldens, Ben van WerkhovenPublished in 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) by IEEE in 202310.1109/ipdpsw59300.2023.00126
- 7.Improve the Performance of Parallel Reduction on General-Purpose Graphics Processor Units Using Prediction ModelsAuthor(s): Chin-Fu Kuo, Zong-Ru Zhung, Yung-Feng LuPublished in Proceedings of the International Conference on Research in Adaptive and Convergent Systems by ACM in 202310.1145/3599957.3606208
- 8.Scalable Tuning of (OpenMP) GPU Applications via Kernel Record and ReplayAuthor(s): Konstantinos Parasyris, Giorgis Georgakoudis, Esteban Rangel, Ignacio Laguna, Johannes DoerfertPublished in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis by ACM in 202310.1145/3581784.3607098
- 9.ML-based Performance Portability for Time-Dependent Density Functional Theory in HPC EnvironmentsAuthor(s): Adrian P. Dieguez, Min Choi, Xinran Zhu, Bryan M. Wong, Khaled Z. IbrahimPublished in 2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) by IEEE in 202210.1109/pmbs56514.2022.00006
- 10.Going green: optimizing GPUs for energy efficiency through model-steered auto-tuningAuthor(s): Richard Schoonhoven, Bram Veenboer, Ben Van Werkhoven, K. Joost BatenburgPublished in 2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) by IEEE in 202210.1109/pmbs56514.2022.00010
- 11.Lightning: Scaling the GPU Programming Model Beyond a Single GPUAuthor(s): Stijn Heldens, Pieter Hijma, Ben Van Werkhoven, Jason Maassen, Rob V. van NieuwpoortPublished in 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS) by IEEE in 202210.1109/ipdps53621.2022.00054
- 12.Analyzing Search Techniques for Autotuning Image-based GPU Kernels: The Impact of Sample SizesAuthor(s): Jacob O. Torring, Anne C. ElsterPublished in 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) by IEEE in 202210.1109/ipdpsw55747.2022.00155
- 13.Bayesian Optimization for auto-tuning GPU kernelsAuthor(s): Floris-Jan Willemsen, Rob van Nieuwpoort, Ben van WerkhovenPublished in 2021 International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) by IEEE in 202110.1109/pmbs54543.2021.00017
- 14.Shrinking Sample Search Algorithm for Automatic Tuning of GPU KernelsAuthor(s): Xiang Li, Gagan AgrawalPublished in 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC) by IEEE in 202110.1109/hipc53243.2021.00040
- 15.DeepCuts: a deep learning optimization framework for versatile GPU workloadsAuthor(s): Wookeun Jung, Thanh Tuan Dao, Jaejin LeePublished in Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation by ACM in 202110.1145/3453483.3454038
- 16.BAT: A Benchmark suite for AutoTunersPublished in 2021
- 17.csTuner: Scalable Auto-tuning Framework for Complex Stencil Computation on GPUsAuthor(s): Qingxiao Sun, Yi Liu, Hailong Yang, Zhonghui Jiang, Xiaoyan Liu, Ming Dun, Zhongzhi Luan, Depei QianPublished in 2021 IEEE International Conference on Cluster Computing (CLUSTER) by IEEE in 202110.1109/cluster48925.2021.00037
- 18.Towards automated kernel selection in machine learning systems: A SYCL case studyAuthor(s): John LawsonPublished in 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) by IEEE in 202010.1109/ipdpsw50202.2020.00086
- 19.One-Shot Layer-Wise Accuracy Approximation For Layer PruningAuthor(s): Sara Elkerdawy, Mostafa Elhoushi, Abhineet Singh, Hong Zhang, Nilanjan RayPublished in 2020 IEEE International Conference on Image Processing (ICIP) by IEEE in 202010.1109/icip40778.2020.9191238
- 20.Performance tuning case study on graphics processing unit-accelerated monte carlo simulations for proton therapyAuthor(s): Yi-Shen Chen, Sheng-Wei Cheng, Tei-Wei KuoPublished in Proceedings of the Conference on Research in Adaptive and Convergent Systems by ACM in 201910.1145/3338840.3355638
- 21.A spatial column-store to triangulate the Netherlands on the fly.Author(s): Romulo Goncalves, Tom van Tilburg, Kostis Kyzirakos, Foteini Alvanaki, Panagiotis Koutsourakis, Ben van Werkhoven, Willem van HagePublished in Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems by ACM in 201610.1145/2996913.2997005
Journal articles
26- 1.Kernel Tuning ToolkitAuthor(s): Filip Petrovič, Jiří FilipovičPublished in SoftwareX by Elsevier BV in 2023, page: 10138510.1016/j.softx.2023.101385
- 2.Benchmarking Optimization Algorithms for Auto-Tuning GPU KernelsAuthor(s): Richard Arnoud Schoonhoven, Ben van Werkhoven, Kees Joost BatenburgPublished in IEEE Transactions on Evolutionary Computation by Institute of Electrical and Electronics Engineers (IEEE) in 2023, page: 550-56410.1109/tevc.2022.3210654
- 3.Umpalumpa: a framework for efficient execution of complex image processing workloads on heterogeneous nodesAuthor(s): David Střelák, David Myška, Filip Petrovič, Jan Polák, Jaroslav Ol’ha, Jiří FilipovičPublished in Computing by Springer Science and Business Media LLC in 2023, page: 2389-241710.1007/s00607-023-01190-w
- 4.Applications of different machine learning methods on nuclear charge radius estimationsAuthor(s): Tuncay Bayram, Cafer Mert Yeşilkanat, Serkan AkkoyunPublished in Physica Scripta by IOP Publishing in 2023, page: 12531010.1088/1402-4896/ad0434
- 5.Optimization Techniques for GPU ProgrammingAuthor(s): Pieter Hijma, Stijn Heldens, Alessio Sclocco, Ben van Werkhoven, Henri E. BalPublished in ACM Computing Surveys by Association for Computing Machinery (ACM) in 2023, page: 1-8110.1145/3570638
- 6.Principles for an Implementation of a Complete CT Reconstruction Tool Chain for Arbitrary Sized Data Sets and Its GPU OptimizationAuthor(s): Jürgen Hofmann, Alexander Flisch, Robert ZborayPublished in Journal of Imaging by MDPI AG in 2022, page: 1210.3390/jimaging8010012
- 7.A Survey of Performance Tuning Techniques and Tools for Parallel ApplicationsAuthor(s): Dheya MustafaPublished in IEEE Access by Institute of Electrical and Electronics Engineers (IEEE) in 2022, page: 15036-1505510.1109/access.2022.3147846
- 8.Using hardware performance counters to speed up autotuning convergence on GPUsAuthor(s): Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Ol'ha, Filip PetrovičPublished in Journal of Parallel and Distributed Computing by Elsevier BV in 2022, page: 16-3510.1016/j.jpdc.2021.10.003
- 9.Efficient Auto-Tuning of Parallel Programs with Interdependent Tuning Parameters via Auto-Tuning Framework (ATF)Author(s): Ari Rasch, Richard Schulze, Michel Steuwer, Sergei GorlatchPublished in ACM Transactions on Architecture and Code Optimization by Association for Computing Machinery (ACM) in 2021, page: 1-2610.1145/3427093
- 10.3D particle averaging and detection of macromolecular symmetry in localization microscopyAuthor(s): Hamidreza Heydarian, Maarten Joosten, Adrian Przybylski, Florian Schueder, Ralf Jungmann, Ben van Werkhoven, Jan Keller-Findeisen, Jonas Ries, Sjoerd Stallinga, Mark Bates, Bernd RiegerPublished in Nature Communications by Springer Science and Business Media LLC in 202110.1038/s41467-021-22006-5
- 11.Using hardware performance counters to speed up autotuning convergence on GPUsPublished in 2021
- 12.On the Anatomy of Predictive Models for Accelerating GPU Convolution Kernels and BeyondAuthor(s): Paolo Sylos Labini, Marco Cianfriglia, Damiano Perri, Osvaldo Gervasi, Grigori Fursin, Anton Lokhmotov, Cedric Nugteren, Bruno Carpentieri, Fabiana Zollo, Flavio VellaPublished in ACM Transactions on Architecture and Code Optimization by Association for Computing Machinery (ACM) in 2021, page: 1-2410.1145/3434402
- 13.Performance portability through machine learning guided kernel selection in SYCL librariesAuthor(s): John Lawson, Mehdi GoliPublished in Parallel Computing by Elsevier BV in 2021, page: 10281310.1016/j.parco.2021.102813
- 14.Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimizationAuthor(s): Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul Hovland, Valerie Taylor, Mary HallPublished in Concurrency and Computation: Practice and Experience by Wiley in 202110.1002/cpe.6683
- 15.Bayesian Optimization for auto-tuning GPU kernelsPublished in 2021
- 16.ParaFROST, ParaFROST CBT, ParaFROST HRE, ParaFROST ALL at the SAT Race 2020Published in 2020
- 17.An automatic optimizer for heterogeneous devicesAuthor(s): Jorge Fernández-Fabeiro, Diego Andrade, Basilio B. Fraguela, Ramón DoalloPublished in Future Generation Computer Systems by Elsevier BV in 2020, page: 572-58410.1016/j.future.2020.01.018
- 18.iScore: An MPI supported software for ranking protein–protein docking models based on a random walk graph kernel and support vector machinesAuthor(s): Nicolas Renaud, Yong Jung, Vasant Honavar, Cunliang Geng, Alexandre M.J.J. Bonvin, Li C. XuePublished in SoftwareX by Elsevier BV in 2020, page: 10046210.1016/j.softx.2020.100462
- 19.Kernel-Based Resource Allocation for Improving GPU Throughput While Minimizing the Activity Divergence of SMsAuthor(s): Zois-Gerasimos Tasoulas, Iraklis AnagnostopoulosPublished in IEEE Transactions on Circuits and Systems I: Regular Papers by Institute of Electrical and Electronics Engineers (IEEE) in 2020, page: 428-44010.1109/tcsi.2019.2933245
- 20.A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning ToolkitAuthor(s): Filip Petrovič, David Střelák, Jana Hozzová, Jaroslav Ol’ha, Richard Trembecký, Siegfried Benkner, Jiří FilipovičPublished in Future Generation Computer Systems by Elsevier BV in 2020, page: 161-17710.1016/j.future.2020.02.069
- 21.Lessons learned in a decade of research software engineering GPU applicationsPublished in 2020
- 22.Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning stepsAuthor(s): Jaroslav Oľha, Jana Hozzová, Jan Fousek, Jiří FilipovičPublished in Concurrency and Computation: Practice and Experience by Wiley in 202010.1002/cpe.5962
- 23.Performance and Aging Aware Resource Allocation for Concurrent GPU Applications Under Process VariationAuthor(s): Zois-Gerasimos Tasoulas, Iraklis AnagnostopoulosPublished in IEEE Transactions on Nanotechnology by Institute of Electrical and Electronics Engineers (IEEE) in 2019, page: 717-72710.1109/tnano.2019.2928268
- 24.Kernel Tuner: A search-optimizing GPU code auto-tunerAuthor(s): Ben van WerkhovenPublished in Future Generation Computer Systems by Elsevier BV in 2019, page: 347-35810.1016/j.future.2018.08.004
- 25.A Jungle Computing approach to common image source identification in large collections of imagesAuthor(s): B. van Werkhoven, P. Hijma, C.J.H. Jacobs, J. Maassen, Z.J.M.H. Geradts, H.E. BalPublished in Digital Investigation by Elsevier BV in 2018, page: 3-1610.1016/j.diin.2018.09.002
- 26.Template-Free 2D Particle Fusion in Localization MicroscopyPublished in 2018
Presentations
13- 1.GPU code optimization and auto-tuning made easy with Kernel TunerPublished in 2021
- 2.eScience 2021: GPU code optimization and auto-tuning made easy with Kernel Tuner: A hands-on, bring your own code tutorialPublished in 2021
- 3.CUDA Code Optimization and Auto-Tuning Made EasyPublished in 2021
- 4.SC21: GPU code optimization and auto-tuning made easy with Kernel Tuner: A hands-on, bring your own code tutorialPublished in 2021
- 5.Bayesian Optimization for auto-tuning GPU kernelsPublished in 2021
- 6.Preliminary results of auto-tuning GPU applications for energy efficiency using Kernel Tuner and PowerSensorPublished in 2019
- 7.Testing and Auto-Tuning GPU code with Kernel TunerPublished in 2019
- 8.Kernel Tuner: A Search-Optimizing GPU Code Auto-TunerPublished in 2018
- 9.Test-driven development of auto-tuned GPU code with Kernel TunerPublished in 2017
- 10.λ/100 resolution by template-free 2D particle fusion in localization microscopyPublished in 2017
- 11.GPU Computing at the Netherlands eScience CenterPublished in 2017
- 12.Template-free 2D-particle fusion of localization microscopy images produces λ/150 resolutionPublished in 2017
- 13.Kernel Tuner: A simple CUDA/OpenCL tuner in PythonPublished in 2016
Video recordings
1Other
9- 1.Kernel Launcher: C++ Library for Optimal-Performance Portable CUDA ApplicationsAuthor(s): Stijn Heldens, Ben van WerkhovenPublished by arXiv in 202310.48550/arxiv.2303.12374
- 2.Towards a Benchmarking Suite for Kernel TunersAuthor(s): Jacob O. Tørring, Ben van Werkhoven, Filip Petrovic, Floris-Jan Willemsen, Jirí Filipovic, Anne C. ElsterPublished by arXiv in 202310.48550/arxiv.2303.08976
- 3.Using hardware performance counters to speed up autotuning convergence on GPUsAuthor(s): Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip PetrovičPublished in 2021
- 4.Bayesian Optimization for auto-tuning GPU kernelsAuthor(s): Floris-Jan Willemsen, Rob van Nieuwpoort, Ben van WerkhovenPublished in 2021
- 5.Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version)Author(s): Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul Hovland, Valerie Taylor, Mary HallPublished by arXiv in 202110.48550/arxiv.2104.13242
- 6.Performance portability through machine learning guided kernel selection in SYCL librariesAuthor(s): John LawsonPublished in 2020
- 7.Lessons learned in a decade of research software engineering GPU applicationsAuthor(s): Ben van Werkhoven, Willem Jan Palenstijn, Alessio ScloccoPublished in 2020
- 8.Towards automated kernel selection in machine learning systems: A SYCL case studyAuthor(s): John LawsonPublished in 2020
- 9.To filter prune, or to layer prune, that is the questionAuthor(s): Sara Elkerdawy, Mostafa Elhoushi, Abhineet Singh, Hong Zhang, Nilanjan RayPublished in 2020
Testimonials
With Kernel Tuner, we were able to accelerate our CUDA kernels by a factor of 10 in just a few weeks
– Chiel van Heerwaarden, Wageningen University & Research
Contributors
Contact person
Ben van Werkhoven
AS
Alessio Sclocco
Netherlands eScience Center
BV
Bram Veenboer
Astron
Felipe Zapata
Netherlands eScience Center
Floris-Jan Willemsen
Netherlands eScience Center
Inti Pelupessy
Netherlands eScience Center
Jisk Attema
Netherlands eScience Center
Johannes Hidding
Netherlands eScience Center
LO
Leon Oostrum
Netherlands eScience Center
Nicolas Renaud
Netherlands eScience Center
Patrick Bos
Netherlands eScience Center
RS
Richard Schoonhoven
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