###################################################################################
cuSPARSELt: A High-Performance CUDA Library for Sparse Matrix-Matrix Multiplication
###################################################################################
**NVIDIA cuSPARSELt** is a high-performance CUDA library dedicated to general matrix-matrix operations in which at least one operand is a sparse matrix:
.. math::
D = Activation(\alpha op(A) \cdot op(B) + \beta op(C) + bias) \cdot scale
where :math:`op(A)/op(B)` refers to in-place operations such as transpose/non-transpose, and :math:`alpha, beta, scale` are scalars.
The *cuSPARSELt APIs* allow flexibility in the algorithm/operation selection, epilogue, and matrix characteristics, including memory layout, alignment, and data types.
**Download:** `developer.nvidia.com/cusparselt/downloads <https://developer.nvidia.com/cusparselt/downloads>`_
**Provide Feedback:** `Math-Libs-Feedback@nvidia.com <mailto:Math-Libs-Feedback@nvidia.com?subject=cuSPARSELt-Feedback>`_
**Examples**:
`cuSPARSELt Example 1 <https://github.com/NVIDIA/CUDALibrarySamples/tree/master/cuSPARSELt/matmul>`_,
`cuSPARSELt Example 2 <https://github.com/NVIDIA/CUDALibrarySamples/tree/master/cuSPARSELt/matmul_advanced>`_
**Blog post**:
- `Exploiting NVIDIA Ampere Structured Sparsity with cuSPARSELt <https://developer.nvidia.com/blog/exploiting-ampere-structured-sparsity-with-cusparselt/>`_
- `Structured Sparsity in the NVIDIA Ampere Architecture and Applications in Search Engines <https://developer.nvidia.com/blog/structured-sparsity-in-the-nvidia-ampere-architecture-and-applications-in-search-engines/>`__
- `Making the Most of Structured Sparsity in the NVIDIA Ampere Architecture <https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31552/>`__
================================================================================
Key Features
================================================================================
* *NVIDIA Sparse MMA tensor core* support
* Mixed-precision computation support:
+--------------+----------------+-----------------+-------------+
| Input A/B | Input C | Output D | Compute |
+==============+================+=================+=============+
| `FP32` | `FP32` | `FP32` | `FP32` |
+--------------+----------------+-----------------+-------------+
| `FP16` | `FP16` | `FP16` | `FP32` |
+ + + +-------------+
| | | | `FP16` |
+--------------+----------------+-----------------+-------------+
| `BF16` | `BF16` | `BF16` | `FP32` |
+--------------+----------------+-----------------+-------------+
| `INT8` | `INT8` | `INT8` | `INT32` |
+ +----------------+-----------------+ +
| | `INT32` | `INT32` | |
+ +----------------+-----------------+ +
| | `FP16` | `FP16` | |
+ +----------------+-----------------+ +
| | `BF16` | `BF16` | |
+--------------+----------------+-----------------+-------------+
| `E4M3` | `FP16` | `E4M3` | `FP32` |
+ +----------------+-----------------+ +
| | `BF16` | `E4M3` | |
+ +----------------+-----------------+ +
| | `FP16` | `FP16` | |
+ +----------------+-----------------+ +
| | `BF16` | `BF16` | |
+ +----------------+-----------------+ +
| | `FP32` | `FP32` | |
+--------------+----------------+-----------------+-------------+
| `E5M2` | `FP16` | `E5M2` | `FP32` |
+ +----------------+-----------------+ +
| | `BF16` | `E5M2` | |
+ +----------------+-----------------+ +
| | `FP16` | `FP16` | |
+ +----------------+-----------------+ +
| | `BF16` | `BF16` | |
+ +----------------+-----------------+ +
| | `FP32` | `FP32` | |
+--------------+----------------+-----------------+-------------+
* Matrix pruning and compression functionalities
* Activation functions, bias vector, and output scaling
* Batched computation (multiple matrices in a single run)
* GEMM Split-K mode
* Auto-tuning functionality (see `cusparseLtMatmulSearch()`)
* NVTX ranging and Logging functionalities
================================================================================
Support
================================================================================
* *Supported SM Architectures*: `SM 8.0`, `SM 8.6`, `SM 8.9`, `SM 9.0`
* *Supported CPU architectures and operating systems*:
+------------+--------------------+
| OS | CPU archs |
+============+====================+
| `Windows` | `x86_64` |
+------------+--------------------+
| `Linux` | `x86_64`, `Arm64` |
+------------+--------------------+
================================================================================
Documentation
================================================================================
Please refer to https://docs.nvidia.com/cuda/cusparselt/index.html for the cuSPARSELt documentation.
================================================================================
Installation
================================================================================
The cuSPARSELt wheel can be installed as follows:
.. code-block:: bash
pip install nvidia-cusparselt-cuXX
where XX is the CUDA major version (currently CUDA 12 only is supported).
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"description": "###################################################################################\ncuSPARSELt: A High-Performance CUDA Library for Sparse Matrix-Matrix Multiplication\n###################################################################################\n\n**NVIDIA cuSPARSELt** is a high-performance CUDA library dedicated to general matrix-matrix operations in which at least one operand is a sparse matrix:\n\n.. math::\n\n D = Activation(\\alpha op(A) \\cdot op(B) + \\beta op(C) + bias) \\cdot scale\n\nwhere :math:`op(A)/op(B)` refers to in-place operations such as transpose/non-transpose, and :math:`alpha, beta, scale` are scalars.\n\nThe *cuSPARSELt APIs* allow flexibility in the algorithm/operation selection, epilogue, and matrix characteristics, including memory layout, alignment, and data types.\n\n**Download:** `developer.nvidia.com/cusparselt/downloads <https://developer.nvidia.com/cusparselt/downloads>`_\n\n**Provide Feedback:** `Math-Libs-Feedback@nvidia.com <mailto:Math-Libs-Feedback@nvidia.com?subject=cuSPARSELt-Feedback>`_\n\n**Examples**:\n`cuSPARSELt Example 1 <https://github.com/NVIDIA/CUDALibrarySamples/tree/master/cuSPARSELt/matmul>`_,\n`cuSPARSELt Example 2 <https://github.com/NVIDIA/CUDALibrarySamples/tree/master/cuSPARSELt/matmul_advanced>`_\n\n**Blog post**:\n\n- `Exploiting NVIDIA Ampere Structured Sparsity with cuSPARSELt <https://developer.nvidia.com/blog/exploiting-ampere-structured-sparsity-with-cusparselt/>`_\n- `Structured Sparsity in the NVIDIA Ampere Architecture and Applications in Search Engines <https://developer.nvidia.com/blog/structured-sparsity-in-the-nvidia-ampere-architecture-and-applications-in-search-engines/>`__\n- `Making the Most of Structured Sparsity in the NVIDIA Ampere Architecture <https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31552/>`__\n\n================================================================================\nKey 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+--------------+----------------+-----------------+-------------+\n | `E4M3` | `FP16` | `E4M3` | `FP32` |\n + +----------------+-----------------+ +\n | | `BF16` | `E4M3` | |\n + +----------------+-----------------+ +\n | | `FP16` | `FP16` | |\n + +----------------+-----------------+ +\n | | `BF16` | `BF16` | |\n + +----------------+-----------------+ +\n | | `FP32` | `FP32` | |\n +--------------+----------------+-----------------+-------------+\n | `E5M2` | `FP16` | `E5M2` | `FP32` |\n + +----------------+-----------------+ +\n | | `BF16` | `E5M2` | |\n + +----------------+-----------------+ +\n | | `FP16` | `FP16` | |\n + +----------------+-----------------+ +\n | | `BF16` | `BF16` | |\n + +----------------+-----------------+ +\n | | `FP32` | `FP32` | |\n +--------------+----------------+-----------------+-------------+\n\n* Matrix pruning and compression functionalities\n* Activation functions, bias vector, and output scaling\n* Batched computation (multiple matrices in a single run)\n* GEMM Split-K mode\n* Auto-tuning functionality (see `cusparseLtMatmulSearch()`)\n* NVTX ranging and Logging functionalities\n\n================================================================================\nSupport\n================================================================================\n\n* *Supported SM Architectures*: `SM 8.0`, `SM 8.6`, `SM 8.9`, `SM 9.0`\n* *Supported CPU architectures and operating systems*:\n\n+------------+--------------------+\n| OS | CPU archs |\n+============+====================+\n| `Windows` | `x86_64` |\n+------------+--------------------+\n| `Linux` | `x86_64`, `Arm64` |\n+------------+--------------------+\n\n\n================================================================================\nDocumentation\n================================================================================\n\nPlease refer to https://docs.nvidia.com/cuda/cusparselt/index.html for the cuSPARSELt 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