Anaconda Enterprise: Documentation

# Anaconda Accelerate

Accelerate provides access to numerical libraries optimized for performance on Intel CPUs and NVidia GPUs.

## Features

- Bindings to CUDA libraries: cuBLAS, cuFFT, cuSPARSE, cuRAND, and sorting algorithms from the CUB and Modern GPU libraries
- Speed-boosted linear algebra operations in NumPy, SciPy, scikit-learn and NumExpr libraries using Intel’s Math Kernel Library (MKL).
- Increased-speed Fast Fourier Transformations (FFT) in NumPy.

## Requirements

- 64-bit operating system: Linux, OS X or Windows

- Supported Python and Numpy combinations:
    - Python 2.7 with Numpy 1.9 or 1.10
    - Python 3.4 with Numpy 1.9 or 1.10
    - Python 3.5 with Numpy 1.9 or 1.10

- Numba 0.23.1

For the CUDA features:

- NVidia driver version 349.00 or later
- CUDA toolkit 7.0
- At least one CUDA GPU with compute capability 2.0 or above

## Installation

Accelerate is included with Anaconda Workgroup and Anaconda Enterprise subscriptions.

To start a 30-day free trial just download and install the Anaconda Accelerate package.

If you already have Anaconda (free Python distribution) installed:

    conda update conda
    conda install accelerate

If you do not have Anaconda installed, you can download it here.

Anaconda Accelerate can also be installed into your own (non-Anaconda) Python environment. For more information about Accelerate please contact sales@continuum.io.

## Update Instructions

If you have Anaconda (free Python distribution) installed:

    conda update conda
    conda update accelerate

If you already have NumbaPro installed, you must manually upgrade NumbaPro to install the NumbaPro compatibility layer:

    conda update conda
    conda update numbapro

## Contents

- Release Notes
  - Version 2.0
    - CUDA Libraries
    - Code Generation
    - Intel MKL
- CUDA Libraries
  - cuBLAS
    - BLAS Level 1
    - BLAS Level 2
    - BLAS Level 3
  - cuSPARSE
    - BLAS Level 1
    - BLAS Level 2
    - BLAS Level 3
    - Extra Functions
    - Preconditioners
    - Format Conversion
  - cuFFT
    - Forward FFT
    - Inverse FFT
    - FFTPlan
  - cuRAND
    - class PRNG
    - class QRNG
    - Top Level PRNG Functions
    - Top Level QRNG Functions
  - CUDA Sorting
    - Sorting Large Arrays
    - Sorting Many Small Arrays
- MKL
  - MKL utility functions
    - Reference
  - MKL FFT

## License Agreement

- Anaconda Accelerate END USER LICENSE AGREEMENT