NVIDIA’s latest innovation, nvmath-python, is a game-changer for Python applications seeking high-performance mathematical operations. This open-source library provides direct access to NVIDIA’s CUDA-X Math Libraries, allowing developers to harness the power of NVIDIA hardware without needing intermediary C or C++ bindings. With nvmath-python, users can leverage accelerated math capabilities in deep learning, data processing, and more, while maintaining their familiar workflows and data structures.
This cutting-edge technology offers seamless integration with popular Python packages like CuPy, PyTorch, RAPIDS, NumPy, SciPy, and scikit-learn. Additionally, nvmath-python’s APIs are designed for minimal overhead, ensuring near-native performance for Python applications. The library also provides advanced features such as linear algebra operations, fast Fourier transforms, and customizable device callback functions.
By bridging the gap between Python applications and NVIDIA hardware, nvmath-python is poised to revolutionize various fields that rely heavily on mathematical computations.
Accelerating Python Applications with nvmath-python
nvmath-python is an open-source library that provides high-performance, Pythonic access to the core mathematical operations implemented in the NVIDIA CUDA-X Math Libraries. This allows Python applications across deep learning, data processing, and more to leverage the power of NVIDIA hardware for computations out-of-the-box.
Truly Pythonic Accelerated Math
nvmath-python offers a unique advantage by providing pythonic host and device APIs for using the highly optimized NVIDIA math libraries in Python applications, without the need for intermediary C or C++ bindings. This allows developers to write Python code that can seamlessly interact with NVIDIA hardware, enabling accelerated computations without the need for low-level programming.
The library provides freedom of using either stateless (function-style) or stateful (class-style) APIs, depending on how much control and expressiveness a user wants. Additionally, nvmath-python integrates with the standard logger library to offer visibility into computational details, making it easier to debug and optimize code.
Interoperability with Python Ecosystem
nvmath-python is designed to work in conjunction with popular Python packages, including GPU-based packages like CuPy, PyTorch, and RAPIDS, as well as CPU-based packages like NumPy, SciPy, and scikit-learn. This allows developers to keep using familiar data structures and workflows while benefiting from accelerated math through nvmath-python.
In combination with Python compilers, such as Numba, users can implement device callback functions to customize nvmath-python behavior. This enables advanced users to fine-tune the library’s performance for specific use cases.
High Performance
nvmath-python delivers performance comparable to the underlying C libraries, thanks to its minimal overhead design. The APIs are optimized for near-native performance, allowing Python applications to take full advantage of NVIDIA hardware acceleration.
Callback functions enable device kernel fusion without extra overheads, further improving performance. This makes nvmath-python an attractive solution for computationally intensive tasks in deep learning, data processing, and other fields.
Supported Operations
Linear Algebra
nvmath-python provides advanced matrix-matrix multiplication operations with a bias, different scaling factors, and epilog functions. These operations are available in a variety of precisions, both as host and device APIs.
The library’s linear algebra performance is demonstrated through benchmarks on H100 PCIe hardware, showcasing its ability to handle large matrices with high precision.
Fast Fourier Transforms
Backed by the NVIDIA cuFFT library, nvmath-python provides a powerful set of APIs to perform N-dimensional discrete Fourier Transformations. These include forward and inverse transformations for complex-to-complex, complex-to-real, and real-to-complex cases.
The operations are available in a variety of precisions, both as host and device APIs. Additionally, nvmath-python provides utility functions to facilitate user-specific caching for FFT plans, further improving performance.
The library’s FFT performance is demonstrated through benchmarks on H100 PCIe hardware, showcasing its ability to handle large FFTs with high precision.
nvmath-python provides extensive documentation, including a GitHub repository and demo code at the GTC conference. The library’s resources are designed to help developers get started quickly and make the most of its features.
With nvmath-python, Python applications can tap into the power of NVIDIA hardware acceleration, enabling faster computations and more efficient processing of large datasets. Its unique blend of pythonic APIs, high performance, and interoperability with popular Python packages makes it an attractive solution for a wide range of use cases.
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