© 2015 Continuum Analytics- Confidential & Proprietary

High Performance Python with Numba

Stan Seibert

May 3, 2016

The Platform to Accelerate, Connect & Empower

Continuum Analytics is the company behind Anaconda and offers:

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is….the leading open data science platform

powered by Python the fastest growing open data science language

• Consulting

• Training

• Open-Source Software

• Enterprise Software

Why Compile Python?

• Pure, interpreted Python is slow.

• Python can easily interface with fast code written in

C, C++, FORTRAN

• Do we always need to switch languages for speed?

• Answer: No, Numba provides an alternative.

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How Does Numba Work?

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Python Function

(bytecode)

Bytecode

Analysis

Functions

Arguments

Numba IR

Machine

Code

Execut

e!

Type

Inference

LLVM/NVVM JIT LLVM IR

Lowering

Rewrite IR

Cache

@jit

def do_math(a, b):

…

>>> do_math(x, y)

Supported Platforms

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OS HW SW

• Windows (7 and later) • 32 and 64-bit x86 CPUs • Python 2 and 3

• OS X (10.9 and later) • CUDA-capable NVIDIA GPUs • NumPy 1.7 through 1.10

• Linux (~RHEL 5 and

later)• HSA-capable AMD GPUs

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Example: Filter an array

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Array Allocation

Looping over ndarray x as an iterator

Using numpy math functions

Returning a slice of the array

Numba decorator

(nopython=True not required)

Example: Filter an array

2.7x speedup

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Type Specialization

Numba compiles a new version for each set of argument types

Automatically dispatches to correct version in subsequent calls.

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Function Inlining

LLVM can inline functions, optimizing away overhead

No clamp function call is found in mean_with_clamp

assembly.

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Multi-threadingNumba will release the GIL if requested.

Allows multithreaded execution to use multiple cores.

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1 Core 2 Cores 4 Cores

Speedup

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NumPy Ufuncs

Turn a scalar function into an array function (“ufunc”):

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Compiling with Multi-threaded Target

Only change required to generate multithreaded ufunc!

3.2x speedup on quad-core system

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Using Numba in Distributed Computing

• Numba-compiled functions can be serialized and sent to remote systems.

• Functions are recompiled on remote system(and specialized to that CPU!)

• Works with systems that rely use pickle to send functions. Tested with Spark and Dask.

Dask

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Summary

• Numba compiles numerical Python with large speedups

• Supports a wide range of Python and NumPy use cases.

• Create ufuncs that can be parallelized automatically!

• Numba works with distributed compute frameworks like

Spark and Dask.

Copyright © 2016, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others.

Optimization Notice15

Additional Materials

Intel® Distribution for Python*:

Visit webpage for download, documentation, and support

Intel® VTune™ Amplifier:

Now supports profiling Python apps!

Visit webpage for more information and download

More Intel products for developers on Intel Developer Zone

Visit Intel® Software Development Tools webpage

Numba JIT Compiler for Python

Project page: http://numba.pydata.org

Code examples: https://anaconda.org/seibert/intel_continuum_webinar_may3/notebook