Release Notes

cuTensorNet v2.0.0

  • We are on NVIDIA/cuQuantum GitHub Discussions! For any questions regarding (or exciting works built upon) cuQuantum, please feel free to reach out to us on GitHub Discussions.

  • Major release:

    • A conda package is released on conda-forge: conda install -c conda-forge cutensornet. Users can still obtain both cuTensorNet and cuStateVec with conda install -c conda-forge cuquantum, as before.

    • A pip wheel is released on PyPI: pip install cutensornet-cu11. Users can still obtain both cuTensorNet and cuStateVec with pip install cuquantum, as before.

      • Currently, the cuquantum meta-wheel points to the cuquantum-cu11 meta-wheel (which then points to cutensornet-cu11 and custatevec-cu11 wheels). This may change in a future release when a new CUDA version becomes available. Using wheels with the -cuXX suffix is encouraged.

  • New functionalities:

    • Initial support for Hopper users. This requires CUDA 11.8.

    • New APIs to create, query, and destroy tensor descriptor objects.

    • New APIs and functionalities for approximate tensor network algorithms. cuTensornet now supports the computational primitives mentioned below to enable users to develop approximate tensor network simulators for quantum circuits including MPS, PEPS, and more:

      • Tensor decomposition via QR or SVD. Both exact and truncated SVD supported.

      • Application of a gate to a pair of connected tensors followed by compression.

    • New APIs to create, tune, query, and destroy tensor SVD truncation settings.

    • New APIs to create, query, and destroy runtime tensor SVD truncation information.

    • Automatic distributed execution: cuTensorNet API is extended to include functions enabling automated distributed parallelization of tensor network contractions across multiple GPUs. Once activated, the parallelization is applied to both tensor network contraction path finding (when hyper-sampling is enabled) and contraction execution, without making any changes to the original serial source code.

  • Functionalities introduced that break previous APIs:

  • Bugs fixed:

    • Memory access error when running cuda-memcheck in a few corner cases.

    • Logging related bug upon setting some attributes.

    • Inaccurate flops computed by cuTensorNet with user-provided path & slicing.

    • “Undefined symbol” error when using cuTensorNet in the NVIDIA HPC SDK container.

    • Incorrect handling of extent-1 modes in the deprecated cutensornetGetOutputTensorDetails() API.

  • Performance enhancements:

    • Improved performance of the contraction path optimization process. On average, about 3X speedup was observed on many problems.

    • Improved performance of the contraction auto-tuning process.

    • Improved the quality of the slicing algorithm. We now select the configuration with the minimum number of slices that has the minimal flops overhead.

    • More auto-tuning heuristics added that improves tensor contraction performance.

  • Other changes:

Compatibility notes:

  • cuTensorNet requires cuTENSOR 1.6.1 or above, but cuTENSOR 1.6.2 or above is recommended, for performance improvements and bug fixes.

  • cuTensorNet requires CUDA 11.x, but CUDA 11.8 is recommended, for Hopper support, performance improvements, and bug fixes.

Known issues:

  • With CUDA 11.7 or lower, cutensornetTensorQR() can potentially fail for certain extents.

  • cutensornetTensorQR() can potentially fail when users provide a customized memory pool to compute the QR factorization of double complex data with certain extent combinations.

  • With cuTENSOR 1.6.1 and Turing, broadcasting tensor modes with extent-1 might fail in certain cases.

cuTensorNet v1.1.1

  • Bugs fixed:

    • The version constraint cuTENSOR>=1.5,<2 as promised elsewhere in the documentation was not correctly respected. Both the code and various package sources are now fixed.

cuTensorNet v1.1.0

  • New APIs and functionalities introduced:

  • Functionality/performance improvements:

    • Since near optimal paths are easily found for small networks without simplification, and since simplification does not guarantee an optimal path, the simplification phase has been turned OFF by default when the simplified network is sufficiently small.

    • A new slicing algorithm has been developed, leading to potentially more efficient slicing solutions.

    • Improve contraction performance by optimizing intermediate mode-ordering.

    • Improve contraction performance of networks that have many singleton mode labels.

  • Bugs fixed:

    • Previously, in rare circumstances, the slicing algorithm could fail to make progress toward finding a valid solution, resulting in an infinite loop. This has been fixed.

    • A bug in the deprecated cutensornetContraction() API that accepted sliceId >= numSlices.

  • Other changes:

    • Provide a distributed (MPI-based) C sample that shows how easy it is to use cuTensorNet and create parallelism.

    • Update the (non-distributed) C sample by improving memory usage and employing the new contraction API cutensornetContractSlices().

cuTensorNet v1.0.1

  • Bugs fixed:

  • Performance enhancements:

    • This release improved the support for generalized einsum expression to provide a better contraction path.

  • Other changes:

    • The Overview and Getting Started pages are significantly improved!

    • Clarify in the documentation and sample that the contraction over slices needs to be done in ascending order, and that when parallelizing over the slices the output tensor should be zero-initialized.

    • Clarify in the documentation that the returned FLOP count assumes real-valued inputs.

    • Several issues in the C++ sample (samples/cutensornet/ are fixed.

cuTensorNet v1.0.0

Compatibility notes:

  • cuTensorNet requires CUDA 11.x.

  • cuTensorNet requires cuTENSOR 1.5.0 or above.

  • cuTensorNet requires OpenMP runtime (GOMP).

  • cuTensorNet no longer requires NVIDIA HPC SDK.

Limitation notes:

  • If multiple slices are created, the order of contracting over slices using cutensornetContraction() should be ascending starting from slice 0. If parallelizing over slices manually (in any fashion: streams, devices, processes, etc.), please make sure the output tensors (that are subject to a global reduction) are zero-initialized.

cuTensorNet v0.1.0

  • Initial public release

  • Add support for Linux ppc64le

  • Add new APIs and functionalities for:

    • Fine-tuning the slicing algorithm

    • Reconfiguring a tensor network

    • Simplifying a tensor network

    • Optimizing pathfinder parameters using the hyperoptimizer

    • Retrieving the optimizer configuration parameters

  • API changes:

Compatibility notes:

  • cuTensorNet requires cuTENSOR 1.4.0 or above

  • cuTensorNet requires NVIDIA HPC SDK 21.11 or above

cuTensorNet v0.0.1

  • Initial release

  • Support Linux x86_64 and Linux Arm64

  • Support Volta and Ampere architectures (compute capability 7.0+)

Compatibility notes:

  • cuTensorNet requires CUDA 11.4 or above

  • cuTensorNet requires cuTENSOR 1.3.3 or above

  • cuTensorNet supports NVIDIA HPC SDK 21.7 or above

Limitation notes:

  • This release is optimized for NVIDIA A100 and V100 GPUs.