{"id":20399914,"url":"https://github.com/kitware/daxtoolkit","last_synced_at":"2025-04-12T13:50:41.802Z","repository":{"id":4649700,"uuid":"5795072","full_name":"Kitware/DaxToolkit","owner":"Kitware","description":"Data Analysis at Extreme","archived":false,"fork":false,"pushed_at":"2014-12-18T22:41:14.000Z","size":7381,"stargazers_count":24,"open_issues_count":1,"forks_count":15,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-03-26T08:23:11.572Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Kitware.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2012-09-13T13:33:57.000Z","updated_at":"2021-06-22T23:05:15.000Z","dependencies_parsed_at":"2022-09-25T04:41:02.622Z","dependency_job_id":null,"html_url":"https://github.com/Kitware/DaxToolkit","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kitware%2FDaxToolkit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kitware%2FDaxToolkit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kitware%2FDaxToolkit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kitware%2FDaxToolkit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Kitware","download_url":"https://codeload.github.com/Kitware/DaxToolkit/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248575516,"owners_count":21127199,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-15T04:35:05.546Z","updated_at":"2025-04-12T13:50:41.780Z","avatar_url":"https://github.com/Kitware.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Data Analysis at Extreme ##\n\nThe Dax Toolkit is an open source C++ header only library that provides a collection of data analysis and visualization algorithms that run well on multi- and many-core processors.  The Dax Toolkit also makes it easy to design other visualization algorithms that work well on such processors. We currently support CUDA, OpenMP, Intel TBB, and Serial Execution.\n\n### How ###\n\nThe Dax Toolkit uses fine-grained concurrency for data analysis and visualization algorithms.\nThe basic computational unit of the Dax Toolkit is a worklet, a function that implements the algorithm’s behavior on an element of a mesh (that is, a point, edge, face, or cell) or a small local neighborhood.\n\nThe worklet is constrained to be serial and stateless; it can access only the element passed to and from the invocation. With this constraint, the serial worklet function can be concurrently executed on an unlimited number of threads without the complications of threads or race conditions.\n\nAlthough worklets are not allowed communication, many visualization algorithms require operations such as variable array packing and coincident topology resolution that intrinsically require significant coordination among threads. Dax enables such algorithms by classifying and implementing the most common and versatile communicative operations into worklet types which are managed by the Dax dispatcher.\n\n### Why ###\n\n\nThe Dax Toolkit simplifies the development of parallel visualization algorithms. Consider the computation of gradients using finite differences. Because the Dax Toolkit is structured such that it can execute worklets on a GPU, we measure that it performs this operation over 100 times faster than VTK running on a single CPU. Furthermore, the Dax API can be switched to a different device by changing only a single line of code. Dax currently provides support for CUDA (GPU), OpenMP (multicore CPU), and serial execution.\n\n``` cpp\nstruct CellGradient : public dax::exec::WorkletMapCell\n{\n  typedef void ControlSignature(Topology, Field(Point), Field(Point), Field(Out));\n  typedef _4 ExecutionSignature(_1,_2,_3);\n\n  template\u003cclass CellTag\u003e\n  DAX_EXEC_EXPORT\n  dax::Vector3 operator()(\n      CellTag cellTag,\n      const dax::exec::CellField\u003cdax::Vector3,CellTag\u003e \u0026coords,\n      const dax::exec::CellField\u003cdax::Scalar,CellTag\u003e \u0026pointField) const\n  {\n    dax::Vector3 parametricCellCenter =\n        dax::exec::ParametricCoordinates\u003cCellTag\u003e::Center();\n\n    return dax::exec::CellDerivative(parametricCellCenter,\n                                     coords,\n                                     pointField,\n                                     cellTag);\n  }\n};\n\n```\n## Getting Dax ##\n\n\nThe Dax repository is located at [https://github.com/kitware/DaxToolkit](https://github.com/kitware/DaxToolkit)\n\nDax dependencies are:\n\n\n+  [CMake 2.8.8](http://cmake.org/cmake/resources/software.html)\n+  [Boost 1.49.0](http://www.boost.org) or greater\n+  [Cuda Toolkit 4+](https://developer.nvidia.com/cuda-toolkit) or [Thrust 1.4 / 1.5](https://thrust.github.com)\n   depending if you want Cuda and/or OpenMP\n\n```\ngit clone git://github.com/Kitware/DaxToolkit.git dax\nmkdir dax-build\ncd dax-build\ncmake-gui ../dax\n```\n\nA detailed walk-through of installing and building Dax can be found on our [Install page](http://www.daxtoolkit.org/index.php/Building_the_Dax_Toolkit)\n\nA walk-through of integrating Dax into your project can be found on our\n[Integration page] (http://www.daxtoolkit.org/index.php/How_To_Use_Dax_In_Your_Project)\n\n\n## Example ##\n\n\nThis basic worklet finds the magnitude of a vector\n\n``` cpp\nclass Magnitude : public dax::exec::WorkletMapField\n{\npublic:\n  typedef void ControlSignature(Field(In), Field(Out));\n  typedef void ExecutionSignature(_1,_2);\n\n  DAX_EXEC_EXPORT void operator()(const dax::Vector3 \u0026inValue,\n                                  dax::Scalar \u0026outValue) const\n    { outValue = dax::math::Magnitude(inValue); }\n};\n```\n\nAnd here is how to execute the worklet:\n\n``` cpp\n\n#include \u003cdax/cont/ArrayHandle.h\u003e\n#include \u003cdax/cont/DispatcherMapField.h\u003e\n#include \u003cdax/cont/UniformGrid.h\u003e\n\n//construct a handle to store the results\ndax::cont::ArrayHandle\u003cdax::Scalar\u003e magnitudeHandle;\n\n//input grid of cubes 1,1,1 at origin 0,0,0 with extents 0,0,0 =\u003e 128,128,128\ndax::cont::UniformGrid\u003c\u003e inputGrid;\ninputGrid.SetExtent(dax::make_Id3(0), dax::make_Id3(127));\n\n//construct and launch the magnitude worklet.\ndax::cont::DispatcherMapField\u003c dax::worklet::Magnitude \u003e dispatcher;\ndispatcher.Invoke(grid-\u003eGetPointCoordinates(),\n                 magnitudeHandle);\n\n\n```\n\nMore examples can be found on our [Getting Started page](http://www.daxtoolkit.org/index.php/Getting_Started)\n\n\n## Publications ##\n\n\n[Flexible Analysis Software for Emerging Architectures](http://www.sandia.gov/~kmorel/documents/DaxPDAC2012). Kenneth Moreland, Brad King, Robert Maynard, and Kwan-Liu Ma. In ''Petascale Data Analytics: Challenges and Opportunities (PDAC-12), November 2012.\n\n[Dax Toolkit: A Proposed Framework for Data Analysis and Visualization at Extreme Scale](http://www.sandia.gov/~kmorel/documents/DaxLDAV2011.pdf). Kenneth Moreland, Utkarsh Ayachit, Berk Geveci, and Kwan-Liu Ma. In ''IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), October 2011.\n\n [Dax: Data Analysis at Extreme](http://www.sandia.gov/~kmorel/documents/SciDAC2011-Dax.pdf). Kenneth Moreland, Utkarsh Ayachit, Berk Geveci, and Kwan-Liu Ma. In ''Proceedings of SciDAC 2011'', July 2011.\n\n\n## Contacts ##\n\nKenneth Moreland\nSandia National Laboratories\nkmorel@sandia.gov\n\nRobert Maynard\nKitware, Inc.\nrobert.maynard@kitware.com\n\nBerk Geveci\nKitware, Inc.\nberk.geveci@kitware.com\n\nKwan-Liu Ma\nUniversity of California at Davis\nma@cs.ucdavis.edu\n\n\n\n## License ##\n\nCopyright (c) Kitware, Inc.\nAll rights reserved.\n[See LICENSE.txt for details](LICENSE.txt).\n\nCopyright 2011 Sandia Corporation.\nUnder the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,\nthe U.S. Government retains certain rights in this software.\n\nThis software is distributed WITHOUT ANY WARRANTY; without even\nthe implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n[See the copyright file for more information](LICENSE.txt).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkitware%2Fdaxtoolkit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkitware%2Fdaxtoolkit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkitware%2Fdaxtoolkit/lists"}