QuantumLiquids/TensorToolkit

TensorToolit is a high-performance tensor basic operation library written in C++. It serves as the foundation for tensor network algorithms (like Density Matrix Renormalization Group, Project Entanglement Pair State) and provides essential functionalities such as tensor contraction, transpose, and more. The package is header-only.

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Functionality

TensorToolit offers the following key features:

• Abelian Quantum Numbers
• MPI Parallelization
• Grassmann Tensor network
• CUDA Single Card

To-Do List

• CUDA Multi-Card Support

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Dependencies

TensorToolkit itself is header-only and requires no installation dependencies. To install the CPU code, It needs to compile the HPTT for CPU-based tensor transpose operation, while HPTT only requires a basic C++ compiler and CMake.

To build test cases or develop programs like TRG, DMRG or TDVP based on TensorToolkit, the following are required:

• C++ Compiler: C++17 or later
• Build System: CMake 3.12 or newer
• Math Libraries: BLAS and LAPACK
• Parallelization: MPI
• GPU Acceleration (optional): CUDA compiler, CUDA toolkit (cuBLAS, cuSolver, cuTensor2)
• Testing Framework (optional): GoogleTest

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Installation

To install TensorToolkit and its dependencies:

1. Clone the repository:

   git clone https://github.com/QuantumLiquids/TensorToolkit.git
   cd TensorToolkit

2. Build using CMake:

   mkdir build && cd build
   cmake .. -DCMAKE_CXX_COMPILER=g++/icpc/clang \
            -DCMAKE_INSTALL_PREFIX=/path/to/install/location
   make -j4 && make install

This will build HPTT and install both HPTT and TensorToolkit into the specified directory.

Building Tests

To build and run the unit tests:

1. From the build directory:

   cmake .. -DQLTEN_BUILD_UNITTEST=ON \
            -DGTest_DIR=/path/to/googletest \
            -DQLTEN_USE_GPU=ON/OFF \
            -DCUTENSOR_ROOT=/path/to/cutensor/if/using/cuda
   make -j16

2. Run the tests:

   ctest

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Author

Hao-Xin Wang

For any inquiries or questions regarding the project, you can reach out to Hao-Xin via email at wanghaoxin1996@gmail.com.

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Acknowledgments

TensorToolit is built upon the foundation laid by the GraceQ/tensor project. While initially inspired by GraceQ/tensor, TensorToolit expands upon its capabilities by adding additional basic tensor operations, improving performance, and most importantly, introducing support for MPI parallelization. We would like to express our gratitude to the following individuals for their contributions and guidance:

• Rong-Yang Sun, the author of GraceQ/tensor, for creating the initial framework that served as the basis for TensorToolit.
• Yi-Fan Jiang, providing me with extensive help and guidance in writing parallel DMRG
• Hong Yao, my PhD advisor. His encouragement and continuous support of computational resources played crucial roles in the implementation of parallel DMRG in TensorToolit.
• Zhen-Cheng Gu, my postdoc advisor, one of the pioneers in the field of tensor network.

Their expertise and support have been invaluable in the development of TensorToolit.

License

TensorToolit is released under the LGPL3 License. Please see the LICENSE file for more details.