In part two of this presentation, continue to learn about the latest developments and tools for high-performance Python* for scikit-learn*, NumPy, SciPy, Pandas, mpi4py, and Numba*. Apply low-overhead profiling tools to analyze mixed C, C++, and Python applications to detect performance bottlenecks in the code and to pinpoint hotspots as the target for performance tuning. Get the best performance from your Python application with the best-known methods, tools, and libraries.

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Numba: A Python Compiler
Stan Seibert
Continuum Analytics
2016-11-12

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Numba: A JIT Compiler for Python Functions
▪ An open-source, function-at-a-time compiler library for Python
▪ Compiler toolbox for different targets and execution models:
– single-threaded CPU, multi-threaded CPU, GPU
– regular functions, “universal functions” (array functions), GPU kernels
▪ Speedup: 2x (compared to basic NumPy code) to 200x (compared to pure Python)
▪ Combine ease of writing Python with speeds approaching FORTRAN
▪ Empowers data scientists who make tools for themselves and other data scientists

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How does Numba work?
Python Function
(bytecode)
Bytecode
Analysis
Functions
Arguments
Numba IR
Machine
Code
Execute!
Type
Inference
LLVM/NVVM JIT LLVM IR
Lowering
Rewrite IR
Cache
@jit
def do_math(a, b):
…
>>> do_math(x, y)

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Supported Platforms and Hardware
OS HW SW
Windows (7 and later) 32 and 64-bit x86
CPUs
Python 2 and 3
OS X (10.9 and later) CUDA & HSA Capable
GPUs
NumPy 1.7 through
1.11
Linux (RHEL 5 and later) Experimental support
for ARM, Xeon Phi,
AMD Fiji GPUs

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Tutorial Acknowledgements
▪ These Numba tutorial materials are adapted from the Numba Tutorial at SciPy
2016 by Gil Forsyth and Lorena Barba
▪ I’ve made some adjustments and additions, and also had to skip quite a bit of
material for time.
▪ Check out https://github.com/barbagroup/numba_tutorial_scipy2016 for more
details.

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Notebook 1: Numba Basics

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Notebook 2: How Numba Works

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Ex01: Intro to JIT

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That’s it?
▪ Mostly, yes.
▪ The Secret of Numba is:
▪ If it doesn’t need to be fast, leave it alone. (See the profiler section of this
tutorial.)
▪ Stick to the well-worn path: Numba works best on loop-heavy numerical
algorithms.
▪ Choose the right data structures: Numba works best on NumPy arrays and
scalars.

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Ex02: Direct Summation

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There is more, though.
▪ Numba can compile other kinds of functions:
▪ Universal function (ufuncs) apply a scalar function to elements of the input
arrays according to the broadcast rules:
numpy.add([1, 2, 3], 1) == [2, 3, 4]
numpy.add([1, 2, 3], [10, 20, 30]) == [11, 12, 13]

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Notebook 3: Ufuncs

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More Advanced Topics
▪ Generalized ufuncs:
▪ Instead of broadcasting all dimensions into a scalar function, you can control how input
dimensions are broadcast.
▪ Example: Writing a norm() function
▪ http://numba.pydata.org/numba-doc/0.29.0/user/vectorize.html#the-guvectorize-decorator
▪ Calling external code:
▪ Numba can call C code that has been wrapped with ctypes or CFFI
▪ http://numba.pydata.org/numba-doc/0.29.0/reference/pysupported.html#ctypes
▪ http://numba.pydata.org/numba-doc/0.29.0/reference/pysupported.html#cffi
▪ Ahead of time compilation:
▪ http://numba.pydata.org/numba-doc/dev/user/pycc.html