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TornadoVM\n[![Build \u0026 Test JDK 21](https://github.com/beehive-lab/TornadoVM/actions/workflows/build-test-jdk21.yml/badge.svg)](https://github.com/beehive-lab/TornadoVM/actions/workflows/build-test-jdk21.yml)\n[![Build \u0026 Test JDK 25](https://github.com/beehive-lab/TornadoVM/actions/workflows/build-test-jdk25.yml/badge.svg?branch=jdk25)](https://github.com/beehive-lab/TornadoVM/actions/workflows/build-test-jdk25.yml)\n\n[![API Version](https://img.shields.io/maven-central/v/io.github.beehive-lab/tornado-api?logo=apache-maven\u0026color=blue\u0026label=Tornado%20API)](https://central.sonatype.com/artifact/io.github.beehive-lab/tornado-api)\n[![Runtime Version](https://img.shields.io/maven-central/v/io.github.beehive-lab/tornado-runtime?logo=apache-maven\u0026color=blue\u0026label=Tornado%20Runtime)](https://central.sonatype.com/artifact/io.github.beehive-lab/tornado-runtime)\n\n[![SDKMAN!](https://img.shields.io/badge/Install%20with-SDKMAN!-2c7a7b?logo=sdkman\u0026logoColor=white)](https://sdkman.io/sdks/tornadovm/)\n\n\u003cimg align=\"left\" width=\"180\" height=\"180\" src=\"etc/tornadoVM_Logo.jpg\"\u003e\n\nTornadoVM is a plug-in to OpenJDK and GraalVM that allows programmers to automatically run Java programs on\nheterogeneous hardware.\nTornadoVM targets **OpenCL, PTX, Metal and SPIR-V** compatible devices which include multi-core CPUs, dedicated\nGPUs (Intel, NVIDIA, AMD), integrated GPUs (Apple Silicon, Intel HD Graphics and ARM Mali), and FPGAs (Intel and Xilinx).\n\nTornadoVM has four backends that generate OpenCL C, NVIDIA CUDA PTX assembly, SPIR-V binary, and Metal Shading Language.\nDevelopers can choose which backends to install and run.\n\n----------------------\n\n**Website**: [tornadovm.org](https://www.tornadovm.org)\n\n**Documentation**: [https://tornadovm.readthedocs.io/en/latest/](https://tornadovm.readthedocs.io/en/latest/)\n\nFor a quick introduction please read the following [FAQ](https://tornadovm.readthedocs.io/en/latest/).\n\n**Latest Release:** TornadoVM 4.0.0 - 02/04/2026 :\nSee [CHANGELOG](https://tornadovm.readthedocs.io/en/latest/CHANGELOG.html).\n\n----------------------\n\n## 1. Quick Start - Install \u0026 Use the TornadoVM SDK\n\n### Prerequisites\n- **JDK 21** (or GraalVM based on JDK 21)\n- GCC/G++ \u003e= 13.0\n- C toolchain / build tools (for native parts, depending on backend)\n- OpenCL Headers, CUDA Toolkit\n\n**Note:** Make sure `JAVA_HOME` points to your JDK/GraalVM 21 installation.\n\n### Install TornadoVM (via SDKMAN!)\n\nTornadoVM is distributed through our [**official website**](https://www.tornadovm.org/downloads) and **SDKMAN!**. Install a version that matches your OS, architecture, and accelerator backend.\n\nAll TornadoVM SDKs are available on the [SDKMAN! TornadoVM page](https://sdkman.io/sdks/tornadovm/).\n\nYou can choose a backend-specific build:\n\nBackend   | SDKMAN! Latest Version\n--------- | --------------\nOpenCL   | `2.2.0-opencl` **(default)**\nPTX      | `2.2.0-ptx`\nSPIR-V   | `2.2.0-spirv`\nMetal    | `Next-release`\nAll Backends     | `2.2.0-full`\n\n\nInstall **TornadoVM**:\n```bash\nsdk install tornadovm \u003cversion\u003e\n```\n\n### Verify Available Devices\nCheck that TornadoVM can detect your CPU/GPU accelerators and that all required drivers are installed:\n```bash\ntornado --devices\n```\nThis command lists all supported devices available on your system.\n\n### Optional: Containers and Cloud\nIf you are planning to use Docker with TornadoVM on GPUs, you can also\nfollow [these](https://github.com/beehive-lab/docker-tornado#docker-for-tornadovm) guidelines.\n\nYou can also run TornadoVM on Amazon AWS CPUs, GPUs, and FPGAs following the\ninstructions [here](https://tornadovm.readthedocs.io/en/latest/cloud.html).\n\n## 2. Usage Instructions\n\nTornadoVM is currently being used to accelerate machine learning and deep learning applications, computer vision,\nphysics simulations, financial applications, computational photography, and signal processing.\n\nFeatured use-cases:\n\n- [GPULlama3.java](https://github.com/beehive-lab/GPULlama3.java): GPU-accelerated Llama3.java inference in pure Java using TornadoVM.\n- [kfusion-tornadovm](https://github.com/beehive-lab/kfusion-tornadovm): Java application for accelerating a\n  computer-vision application using the Tornado-APIs to run on discrete and integrated GPUs.\n- [Java Ray-Tracer](https://github.com/Vinhixus/TornadoVM-Ray-Tracer): Java application accelerated with TornadoVM for\n  real-time ray-tracing.\n\n### Run your First TornadoVM Program\nYou can run a Matrix-Vector example by adapting the classpath to point to your TornadoVM version:\n\n#### Unix (Linux/macOS)\n```bash\njava @$TORNADOVM_HOME/tornado-argfile -cp $TORNADOVM_HOME/share/java/tornado/tornado-examples-2.2.0.jar uk.ac.manchester.tornado.examples.compute.MatrixVectorRowMajor\n```\n\n#### Windows (10+)\n```bash\njava @%TORNADOVM_HOME%\\tornado-argfile -cp %TORNADOVM_HOME%\\share\\java\\tornado\\tornado-examples-2.2.0.jar uk.ac.manchester.tornado.examples.compute.MatrixVectorRowMajor\n```\n\nWe also have a set\nof [examples](https://github.com/beehive-lab/TornadoVM/tree/master/tornado-examples/src/main/java/uk/ac/manchester/tornado/examples)\nthat includes NBody, DFT, KMeans computation and matrix computations.\n\n**Additional Information**\n\n- [General Documentation](https://tornadovm.readthedocs.io/en/latest/introduction.html)\n- [Benchmarks](https://tornadovm.readthedocs.io/en/latest/benchmarking.html)\n- [How TornadoVM executes reductions](https://tornadovm.readthedocs.io/en/latest/programming.html#parallel-reductions)\n- [Execution Flags](https://tornadovm.readthedocs.io/en/latest/flags.html)\n- [FPGA execution](https://tornadovm.readthedocs.io/en/latest/fpga-programming.html)\n- [Profiler Usage](https://tornadovm.readthedocs.io/en/latest/profiler.html)\n\n## 3. Use TornadoVM in Your Project (Maven)\n\n```xml\n\u003cdependencies\u003e\n  \u003cdependency\u003e\n    \u003cgroupId\u003eio.github.beehive-lab\u003c/groupId\u003e\n    \u003cartifactId\u003etornado-api\u003c/artifactId\u003e\n    \u003cversion\u003e4.0.0\u003c/version\u003e\n  \u003c/dependency\u003e\n  \u003cdependency\u003e\n    \u003cgroupId\u003eio.github.beehive-lab\u003c/groupId\u003e\n    \u003cartifactId\u003etornado-runtime\u003c/artifactId\u003e\n    \u003cversion\u003e4.0.0\u003c/version\u003e\n  \u003c/dependency\u003e\n\u003c/dependencies\u003e\n```\n\n## 4. Programming Model\n\nTornadoVM offers two complementary ways to express parallelism:\n\n### Loop Parallel API\nUse Java annotations such as `@Parallel` and `@Reduce` to parallelize loops.\n\n### Kernel API\nUse `KernelContext` for explicit GPU-style programming (thread IDs, local memory, barriers), similar to CUDA/OpenCL/SYCL.\n\nBoth models can be combined inside a `TaskGraph`.\n\nRead more in our [documentation](https://tornadovm.readthedocs.io/en/latest/programming.html).\n\n## 5. Contributing to TornadoVM\n\nWe welcome contributions from the community — bug reports, documentation improvements, new features, and backends.\n\n### Building From Source\nFor contributors and developers, full build instructions are in:\n\n👉 [INSTALL_FROM_SOURCE.md](INSTALL_FROM_SOURCE.md)\n\n### How to Contribute\n- Read the [CONTRIBUTING](CONTRIBUTING.md) page.\n- Write your questions in [GitHub discussions](https://github.com/beehive-lab/TornadoVM/discussions) or in the TornadoVM [Slack community](https://join.slack.com/t/tornadovmcommunity/shared_invite/zt-3ai2wyqva-bKz~cQRFlaJ~ZnPrbkwIEw).\n- Share your proposals with us in [GitHub discussions](https://github.com/beehive-lab/TornadoVM/discussions) or as a Google document.\n- Submit pull requests — all contributions are welcome.\n\n### Collaborations:\n\nFor Academic \u0026 Industrial collaborations, please contact [here](https://www.tornadovm.org/contact-us).\n\n## 6. Additional Resources\n\n[Here](https://tornadovm.readthedocs.io/en/latest/resources.html) you can find videos, presentations, tech-articles and\nartefacts describing TornadoVM, and how to use it.\n\n## 7. Academic Publications\n\nIf you are using **TornadoVM \u003e= 0.2** (which includes the Dynamic Reconfiguration, the initial FPGA support and CPU/GPU\nreductions), please use the following citation:\n\n```bibtex\n@inproceedings{Fumero:DARHH:VEE:2019,\n author = {Fumero, Juan and Papadimitriou, Michail and Zakkak, Foivos S. and Xekalaki, Maria and Clarkson, James and Kotselidis, Christos},\n title = {{Dynamic Application Reconfiguration on Heterogeneous Hardware.}},\n booktitle = {Proceedings of the 15th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments},\n series = {VEE '19},\n year = {2019},\n doi = {10.1145/3313808.3313819},\n publisher = {Association for Computing Machinery}\n}\n```\n\nIf you are using **Tornado 0.1** (Initial release), please use the following citation in your work.\n\n```bibtex\n@inproceedings{Clarkson:2018:EHH:3237009.3237016,\n author = {Clarkson, James and Fumero, Juan and Papadimitriou, Michail and Zakkak, Foivos S. and Xekalaki, Maria and Kotselidis, Christos and Luj\\'{a}n, Mikel},\n title = {{Exploiting High-performance Heterogeneous Hardware for Java Programs Using Graal}},\n booktitle = {Proceedings of the 15th International Conference on Managed Languages \\\u0026 Runtimes},\n series = {ManLang '18},\n year = {2018},\n isbn = {978-1-4503-6424-9},\n location = {Linz, Austria},\n pages = {4:1--4:13},\n articleno = {4},\n numpages = {13},\n url = {http://doi.acm.org/10.1145/3237009.3237016},\n doi = {10.1145/3237009.3237016},\n acmid = {3237016},\n publisher = {ACM},\n address = {New York, NY, USA},\n keywords = {Java, graal, heterogeneous hardware, openCL, virtual machine},\n}\n```\n\nSelected publications can be found [here](https://tornadovm.readthedocs.io/en/latest/publications.html).\n\n## 8. Acknowledgments\n\nThis work is partially funded by [Intel corporation](https://www.intel.com/content/www/us/en/homepage.html).\nIn addition, it has been supported by the following EU \u0026 UKRI grants (most recent first):\n\n- EU Horizon Europe \u0026 UKRI [AERO 101092850](https://aero-project.eu/).\n- EU Horizon Europe \u0026 UKRI [P2CODE 101093069](https://p2code-project.eu/).\n- EU Horizon Europe \u0026 UKRI [ENCRYPT 101070670](https://encrypt-project.eu).\n- EU Horizon Europe \u0026 UKRI [TANGO 101070052](https://tango-project.eu).\n- EU Horizon 2020 [ELEGANT 957286](https://www.elegant-h2020.eu/).\n- EU Horizon 2020 [E2Data 780245](https://e2data.eu).\n- EU Horizon 2020 [ACTiCLOUD 732366](https://acticloud.eu).\n\nFurthermore, TornadoVM has been supported by the following [EPSRC](https://www.ukri.org/councils/epsrc/) grants:\n\n- [PAMELA EP/K008730/1](http://apt.cs.manchester.ac.uk/projects/PAMELA/).\n- [AnyScale Apps EP/L000725/1](https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/L000725/1).\n\n## 9. TornadoVM Team\n\nVisit our [website](https://tornadovm.org) to meet the [team](https://www.tornadovm.org/about-us).\n\n## 10. Licenses Per Module\n\nTo use TornadoVM, you can link the TornadoVM API to your application which is under [Apache 2](https://github.com/beehive-lab/TornadoVM/blob/master/LICENSE_APACHE2).\n\nEach Java TornadoVM module is licensed as follows:\n\n\n| Modules                                                                                                                                                                       | License                                                                                                                                                         |\n| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Tornado-API, Tornado-Assembly, Tornado-scripts, \u003cbr/\u003eTornado-Annotation, Tornado-Unittests, Tornado-Benchmarks, \u003cbr/\u003eTornado-Examples, Tornado-Matrices, Tornado-Drivers-OpenCL-Headers | [![License: Apache 2](https://img.shields.io/badge/License-Apache%202.0-red.svg)](https://github.com/beehive-lab/TornadoVM/blob/master/LICENSE_APACHE2)         |\n| Tornado-Runtime, Tornado-Drivers                                                                                                                                              | [![License: GPL v2](https://img.shields.io/badge/License-GPL%20v2%20Classpath%20Exception-blue.svg)](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html) |\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbeehive-lab%2Ftornadovm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbeehive-lab%2Ftornadovm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbeehive-lab%2Ftornadovm/lists"}