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[Python] Add pip package setup
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‎README.md

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- [X] Wrapper for Python API
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- [ ] Wrapper syntax sugar
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- [ ] Tests for Python wrapper
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- [ ] Pip package
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- [ ] Code examples
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- [ ] User guide
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- [X] Unit Tests collection
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- [X] Dummy library implementation for testing
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- [ ] Publish built artifacts and shared libs
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- [ ] Publish stable source code archives
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### Cuda & Compiler Setup
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> Skip thia section if you want to build library with only sequential backend
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> Skip this section if you want to build library with only sequential backend
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> without cuda backend support.
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Before the CUDA setup process, validate your system NVIDIA driver with `nvidia-smi`
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:return: The transitive closure adjacency matrix
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"""
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t = a.dup() # Duplicate matrix where to store result
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total = 0 # Current number of values
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t = a.dup() # Duplicate matrix where to store result
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total = 0 # Current number of values
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while total != t.nvals:
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total = t.nvals
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## License
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This project is licensed under MIT License. License text can be found in the
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[license file](https://github.com/JetBrains-Research/cuBool/blob/master/LICENSE).
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[license file](https://github.com/JetBrains-Research/cuBool/blob/master/LICENSE.md).
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## Acknowledgments
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‎cubool/CMakeLists.txt

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message(STATUS "Add unit tests directory to the project")
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add_subdirectory(tests)
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endif()
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endif()
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# todo: Different platforms has specific naming conventions
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set(LIBRARY_FILE_NAME "libcubool.so")
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# Copy cubool library after build
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add_custom_command(
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TARGET cubool POST_BUILD
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COMMAND "${CMAKE_COMMAND}" -E
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copy
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"${CMAKE_BINARY_DIR}/cubool/${LIBRARY_FILE_NAME}"
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"${CMAKE_BINARY_DIR}/python/pycubool"
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COMMENT "Copy cubool lib into python folder")

‎cubool/tests/CMakeLists.txt

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add_executable(test_basic_cuda test_basic_cuda.cu)
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target_link_libraries(test_basic_cuda PUBLIC testing)
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target_compile_features(test_basic_cuda PUBLIC cxx_std_11)
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set_target_properties(test_basic_cuda PROPERTIES CXX_STANDARD 11)
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set_target_properties(test_basic_cuda PROPERTIES CXX_STANDARD_REQUIRED YES)
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set_target_properties(test_basic_cuda PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
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# add_executable(test_basic_cuda test_basic_cuda.cu)
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# target_link_libraries(test_basic_cuda PUBLIC testing)
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# target_compile_features(test_basic_cuda PUBLIC cxx_std_11)
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# set_target_properties(test_basic_cuda PROPERTIES CXX_STANDARD 11)
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# set_target_properties(test_basic_cuda PROPERTIES CXX_STANDARD_REQUIRED YES)
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# set_target_properties(test_basic_cuda PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
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add_executable(test_library_api test_library_api.cpp)
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target_link_libraries(test_library_api PUBLIC testing)

‎python/LICENSE

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MIT License
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Copyright (c) 2020, 2021 JetBrains-Research
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎python/MANIFEST.in

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include LICENSE

‎python/README.md

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# Python Directory
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# pycubool
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This directory is intended to store python wrappers and other
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python utilities, required for using `cubool` library within
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testing infrastructure and etc.
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[![JB Research](https://jb.gg/badges/research-flat-square.svg)](https://research.jetbrains.org/)
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[![Ubuntu](https://github.com/JetBrains-Research/cuBool/workflows/Ubuntu/badge.svg?branch=master)](https://github.com/JetBrains-Research/cuBool/actions)
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[![License](https://img.shields.io/badge/license-MIT-orange)](https://github.com/JetBrains-Research/cuBool/blob/master/LICENSE)
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## Structure
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`pycubool` is a python wrapper for [cuBool](https://github.com/JetBrains-Research/cuBool) library.
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**cuBool** is a linear Boolean algebra library primitives and operations for
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work with sparse matrices written on the NVIDIA CUDA platform. The primary
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goal of the library is implementation, testing and profiling algorithms for
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solving *formal-language-constrained problems*, such as *context-free*
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and *regular* path queries with various semantics for graph databases.
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The library provides C-compatible API, written in the GraphBLAS style,
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as well as python high-level wrapper with automated resources management and fancy syntax sugar.
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**The primary library primitive** is a sparse boolean matrix. The library provides
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the most popular operations for matrix manipulation, such as construction from
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values, transpose, sub-matrix extraction, matrix-to-vector reduce, matrix-matrix
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element-wise addition, matrix-matrix multiplication and Kronecker product.
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**As a fallback** library provides sequential backend for mentioned above operations
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for computations on CPU side only. This backend is selected automatically
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if Cuda compatible device is not presented in the system. This can be quite handy for
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prototyping algorithms on a local computer for later running on a powerful server.
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## Example
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The following Python code snippet demonstrates, how the `pycubool`
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wrapper can be used to compute the transitive closure problem for the
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directed graph within python environment:
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```python
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import pycubool
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def transitive_closure(a: pycubool.Matrix):
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"""
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Evaluates transitive closure for the provided
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adjacency matrix of the graph.
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:param a: Adjacency matrix of the graph
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:return: The transitive closure adjacency matrix
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"""
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t = a.dup() # Duplicate matrix where to store result
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total = 0 # Current number of values
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while total != t.nvals:
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total = t.nvals
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t.mxm(t, out=t, accumulate=True) # t += t * t
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return t
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```
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## Directory structure
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```
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pycubool
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├── docs - documents, text files and various helpful stuff
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├── demo - examples and basic programs
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├── pycubool - package with `cuBool` C API wrapper
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└── tests - tests for python wrapper
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```
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## License
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This project is licensed under MIT License. License text can be found in the
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[license file](https://github.com/JetBrains-Research/cuBool/blob/master/python/LICENSE.md).
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- `pycubool` - python wrapper for the `cubool c api`
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- `tests` - unit tests for pycubool wrapper

‎python/data/placeholder.txt

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Replace me

‎python/demo/placeholder.txt

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Replace me

‎python/pycubool/README.md

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‎python/pycubool/__init__.py

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"""
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Entry point for `pycubool` package.
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pycubool is python wrapper for cuBool library.
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The backend cuBool compiled library is initialized automatically
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on package import into runtime. State of the library is managed
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by the wrapper class. All resources are unloaded automatically on
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python runtime exit.
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Exported primitives:
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- matrix (sparse matrix of boolean values)
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For more information refer to:
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- cuBool project: https://github.com/JetBrains-Research/cuBool
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- bug tracker: https://github.com/JetBrains-Research/cuBool/issues
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License:
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- MIT
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"""
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from .wrapper import *
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from .matrix import *
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