Continuum Analytics: Documentation

# Anaconda Accelerate

## High Performance Computing

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).
- Accelerated variants of Numpy’s built-in UFuncs.
- 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, 1.10 or 1.11
    - Python 3.4 with Numpy 1.9, 1.10 or 1.11
    - Python 3.5 with Numpy 1.9, 1.10 or 1.11

- Numba 0.26

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.

Accelerate licenses can be installed, viewed and removed with the graphical Anaconda Navigator license manager or manually with your operating system. For more information please see the License installation page.

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 2.3.0
  - Version 2.2.0
  - Version 2.1.0
  - Version 2.0.2
  - Version 2.0.1
  - Version 2.0
    - CUDA Libraries
    - Code Generation
    - Intel MKL
- Environment variables
- 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 BLAS functions
    - Reference
    - Example
  - MKL FFT
  - Accelerated UFuncs
    - Arithmetic Functions
    - Power and Root Functions
    - Exponential and Logarithmic Functions
    - Trigonometric Functions
    - Hyperbolic Functions
- Profiling
  - The accelerate.profiler API

## License Agreement

- Anaconda Accelerate END USER LICENSE AGREEMENT

## Documentation for Past Versions

- Accelerate 2.2
- Accelerate 2.1
- Accelerate 2.0