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Anaconda Accelerate

Fast Python for GPUs and multi-core with NumbaPro and MKL Optimizations

Accelerate is an add-on to Continuum’s free enterprise Python distribution, Anaconda. It opens up the full capabilities of your GPU or multi-core processor to Python. Accelerate includes two packages that can be added to your Python installation: NumbaPro and MKL Optimizations. MKL Optimizations makes linear algebra, random number generation, Fourier transforms, and many other operations run faster and in parallel. NumbaPro builds fast GPU and multi-core machine code from easy-to-read Python and NumPy code with a Python-to-GPU compiler.

Install Anaconda Accelerate behind your firewall, and get tools to manage the deployment of Python, R, and internal packages with Anaconda Server.

NumbaPro Features

  • NumbaPro compiler targets multi-core CPU and GPUs directly from simple Python syntax
  • Easily move vectorized NumPy functions to the GPU
  • CUDA-Python library for writing arbitrary CUDA code (including thread synchronization and shared memory allocation)
  • Multiple CUDA device support
  • Support for array slicing and fast array math
  • Parallel “prange” primitive for writing multi-threaded loops in Python
  • Use multiple threads without worrying about the GIL
  • Support for closures, extension classes, and structures
  • Supported on NVIDIA CUDA-enabled GPUs with compute capability 2.0 or above.
    List of CUDA GPUs.

Get Started with the NumbaPro Quick Start Guide [pdf]

Get Started with the MKL Optimizations Quick Start Guide [pdf]

Additional Features

  • MKL Optimizations (built on Intel Math Kernel Library) gives a speed boost for NumPy, SciPy, NumExpr and scikit-learn
  • Compile Python modules into a shared-library + header file, usable from C, with no Python runtime dependency
  • Use multiple threads without worrying about the GIL
  • Support for closures, extension classes, and structures

In addition to receiving all of the above valuable features, your purchase of Accelerate supports the development of Numba and many other open-source projects. Learn more.

Faster than NumPy?

NumPy is the fundamental array library of choice for scientists and engineers. It has popularized array-oriented, vector programming techniques to a wide variety of Python programmers. Scientists and engineers regularly use NumPy's fast vectorized operations for processing and analyzing vast datasets.

Numba, our open source compiler, allows scientists and engineers to easily compile NumPy expressions to efficient parallel machine code, without needing explicit code-generation and recompilation steps. NumbaPro, the premium compiler in Accelerate, builds on Numba and comes with CUDA Python, which enables full CUDA programming with Python syntax. Write your code with arrays, and execute it on GPUs and multi-core by adding just a single decorator.

Supported GPUs

NVIDIA CUDA enabled GPU with compute capability 2.0 or above. List of CUDA GPUs.

License Details

For $129, you will receive one license/copy of Anaconda Accelerate, which includes a license for MKL Optimizations and NumbaPro. This license entitles the licensee (a singular user) to use Accelerate on as many machines as are personally used by the licensee. (For example, you CAN use Accelerate on your home laptop and your office desktop.) If Anaconda Accelerate is to be on a cluster, or used by/available for more than one user, please contact us with the details of your situation and we will help determine the right solution for you.

Anaconda Accelerate End User License Agreement