# Source: https://developer.nvidia.com/nvpl.md

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# NVIDIA Performance Libraries

NVIDIA Performance Libraries (NVPL) are a collection of essential mathematical libraries optimized for [Arm 64-bit architectures](/arm). NVPL allows you to easily port HPC applications to [NVIDIA Grace™ CPU](https://www.nvidia.com/en-us/data-center/grace-cpu/) platforms to achieve industry-leading performance and efficiency.

[Download](https://developer.nvidia.com/nvpl-downloads)[Documentation](https://docs.nvidia.com/nvpl/)[Feedback](nvpl-interest-ext@exchange.nvidia.com)

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## Key Features

### Seamlessly Port to Grace CPU

NVPL math libraries are drop-in replacements for standard C and Fortran mathematical APIs. This allows existing HPC applications to be easily ported to Grace-based systems with no source code changes.

### Optimized for Arm Architecture

NVPL is specifically tuned for Arm 64-bit CPUs and ensures mathematical applications achieve peak efficiency on the Grace microarchitecture. NVPL allows you to fully harness NVIDIA silicon in the data center.

### Use Standard Math Subroutines

Many HPC applications rely on standardized mathematical APIs like BLAS, FFTW, and LAPACK that are critical to application performance. NVPL provides optimized implementations of these essential libraries for the Grace CPU.

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## NVPL Libraries (Beta)

### NVPL BLAS

Industry standard Basic Linear Algebra Subprograms (BLAS) optimized for the NVIDIA Grace CPU architecture with single- and multi-threaded interfaces.

![NVPL BLAS efficiency chart](https://developer.download.nvidia.com/images/nvpl/nvpl-blas-dgemm-efficiency-chart@2x.png)

![NVPL LAPACK Performance chart](https://developer.download.nvidia.com/images/nvpl/nvpl-lapack-chart@2x(1).png)

### NVPL LAPACK

Dense direct linear solvers and eigen-solvers for computer vision, linear optimization, and more on NVIDIA Grace CPUs.

### NVPL FFT

Fast Fourier Transforms (FFT) for applications including computational physics and quantum chemistry.

![NVPL FFT Performance chart](https://developer.download.nvidia.com/images/nvpl/nvpl-fft-performance@2x.png)

### NVPL TENSOR

Tensor contraction, reduction, and elementwise operations for deep learning and inference on Grace CPUs.

### NVPL RAND

High-performance random number generation (RNG) for fast and high-quality random numbers.

### NVPL SPARSE

Sparse linear algebra subroutines for accelerating machine learning, fluid dynamics, and more.

### NVPL ScaLAPACK

A LAPACK extension designed for distributed memory parallel computing environments.

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## Resources

- [NVPL Documentation](https://docs.nvidia.com/nvpl/)
- [NVPL Samples (GitHub)](https://github.com/NVIDIA/NVPLSamples)

- [Unlock the Power of NVIDIA Grace and NVIDIA Hopper™ Architectures with Foundational HPC Software](https://developer.nvidia.com/blog/unlock-the-power-of-nvidia-grace-and-nvidia-hopper-architectures-with-foundational-hpc-software/)
- [NVPL Feedback](mailto:nvpl-interest-ext@exchange.nvidia.com)

## Ready to get started with NVIDIA Performance Libraries?

[Download](https://developer.nvidia.com/nvpl-downloads)

Quick Links

- [Download ](https://developer.nvidia.com/nvpl-downloads)
- [Documentation](https://docs.nvidia.com/nvpl/)

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