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<!doctype html>

<html>

<head>

<title>reveal.js</title>

var link = document.createElement( 'link' );

link.rel = 'stylesheet';

link.type = 'text/css';

link.href = window.location.search.match( /print-pdf/gi ) ? 'css/print/pdf.css' : 'css/print/paper.css';

document.getElementsByTagName( 'head' )[0].appendChild( link );

</script>

<style>

.reveal section img {

border: none;

background-color: initial;

margin: 10px;

}

.reveal .normal-paragraph {

text-align: left;

margin: 0 auto 0 auto;

width: 60%;

}

.reveal .normal-paragraph p {

text-align: left;

}

.header-left {

position: absolute;

top: 2%;

left: 2%;

}

.header-right {

position: absolute;

top: 2%;

right: 2%;

}

.footer-left {

position: absolute;

bottom: 2%;

left: 2%;

}

.reveal .genericon {

font-family: "genericons";

}

.reveal .bio-card {

background: #333;

border-radius: 2px;

display: inline-block;

width: 260px;

margin: 1rem;

position: relative;

font-size: 20px;

}

.reveal .card-photo {

display: block;

width: 200px;

border-top-left-radius: 2px;

border-top-right-radius: 2px;

margin: 28px auto 0 auto;

}

.reveal .card-header {

width: 100%;

font-size: 20px;

margin-top: 10px;

text-align: center;

}

.reveal .card-info {

width: 100%;

float: left;

font-size: 20px;

margin-top: 0;

padding: 0 4px;

text-align: center;

}

.reveal .card-info a {

text-decoration: none;

color: #999;

}

.reveal .card-info em {

vertical-align: top;

}

.revral .card-info a .genericon {

vertical-align: middle;

}

.reveal .bio-social a {

text-decoration: none;

}

.reveal .bio-photo {

width: 200px;

margin: 0 40px 40px -40px;

float: left;

}

.reveal section img {

box-shadow: none;

}

</style>

</head>

<body>

</div>

<p style="display: inline; vertical-align: top; margin-left: 4px; font-size: 20px;">@QuantStack</p>

</div>

<p>Going native: C++ as a first-class citizen of the Jupyter ecosystem</p>

</section>

<p class="card-header">Sylvain Corlay</p>

<em>@SylvainCorlay</em>

</a>

</div>

<em>@SylvainCorlay</em>

</a>

</div>

<p class="card-header">Johan Mabille</p>

<em>@JohanMabille</em>

</a>

</div>

<em>@JohanMabille</em>

</a>

</div>

</section>

<h2>Project Jupyter</h2>

<ul>

<li>Consistent set of tools to improve and unify scientific computing workflows,</li>

<li>An interface between metal and humans, metal and flesh.</li>

</ul>

<p>From day 1, Jupyter was developed by scientists for scientists and educators</p>

</div>

</section>

<h3>Interactive workflows</h3>

<p style="text-align: left;">Programming languages are not only used to <strong>build complex

systems</strong>, but also to <strong>explore</strong> and <strong>gain insight</strong> about</p>

<ul>

<li>A computing resource</li>

<li>The outcome of a simulation</li>

</ul>

<p style="text-align: left;">Interactive workflows:<p>

<ul>

<li>Loading some data</li>

<li>Running some code</li>

<li>Showing a visualization</li>

<li>Running some more code...</li>

</ul>

</div>

</section>

<h3>The C++ programming language</h3>

<ul>

<li>Taylored for performances</li>

<li>With a massive community</li>

<li>Especially in HPC</li>

</ul>

</div>

</section>

<p><strong>(Outside of ROOT) We lack a good story for interative computing in C++</strong></p>

<p>This hurts productivity of C++ software developers.</p>

<ul>

<li>C++ is generally considered as a difficult programming language</li>

<li>Heterogeneous set of tools...</li>

<li>... that don't always play well together</li>

<li>making scientific workflows hard to reproduce</li>

</ul>

</div>

</section>

<div>

</div>

<p>A C++ kernel for Jupyter based on</p>

<ul>

<p>A modern C++ implementation of the Jupyter protocol</p>

</li>

<p>Cling, the C++ interpreter developed at CERN</p>

</li>

</ul>

</section>

<h2>Live demo of Xeus-Cling</h2>

</section>

<h2>Jupyter interactive widgets</h2>

<p>Another area where Jupyter shines is Jupyter interactive widgets</p>

</section>

<p>Jupyter interactive widgets: thick front-end and thin back-end</p>

</section>

<div>

</div>

<ul>

<li>A modern C++ back-end for Jupyter interactive widgets</li>

<li>Uses the front-end implementation of ipywidgets</li>

</ul>

</section>

<h2>Live demo of xwidgets</h2>

</section>

<p>Next goal: implement C++ back-end for other widgets libraries</p>

<ul>

<li>bqplot</li>

<li>ipyleaflet</li>

<li>pythreejs</li>

<li>ipyvolume</li>

</ul>

</section>

<div>

</div>

<ul>

<li>A modern C++ back-end for ipyleaflet</li>

<li>Uses the front-end implementation of ipyleaflet</li>

</ul>

</section>

<h2>Live demo of xleaflet</h2>

</section>

<h2>Voila</h2>

</section>

<p>The Lazy Tensor Algebra Expression System</p>

<p>Xtensor is a flexible expression system in which any data structure can be plugged, offering the most expressive API to the users.</p>

</div>

</section>

<h2>What is xtensor?</h2>

<ul>

<p>A C++ template library for multi-dimensional array manipulation</p>

<ul>

<li>

<p>Followings the idioms of the C++ STL</p>

<p>(iterator pairs, clear value semantics)</p>

</li>

<li>But also an API similar to that of numpy</li>

</ul>

</li>

</ul>

</div>

</section>

<h2>What is xtensor?</h2>

<ul>

<p>Python bindings to enable xtensor APIs on numpy arrays.</p>

</li>

<p>Julia bindings to enable xtensor APIs on Julia arrays.</p>

</li>

<p>R bindings to enable xtensor APIs on R arrays.</p>

</li>

<p>Cookiecutter projects for authoring of Python, Julia, and R extensions.</p>

</li>

</ul>

</div>

</section>

<h2>What is xtensor?</h2>

<ul>

<p>BLAS bindings to enable BLAS operations on xtensor expressions.</p>

</li>

<p>SIMD acceleration kernels.</p>

</li>

<li class="fragment">All open-source (BSD License).</li>

</ul>

</div>

</section>

<style>

div.splitting {

background-color: rgb(20, 98, 135);

width: 50%;

height: 800%;

position: absolute;

top: -200%;

left: 0;

z-index: -1;

}

div.left-panel {

width: 45%;

display: inline-block;

margin-right: 5%;

vertical-align: top;

}

div.right-panel {

width: 45%;

display: inline-block;

}

code.panel {

background-color: initial;

}

pre.panel {

box-shadow: none;

}

</style>

<div>

<p style="text-align: center; vertical-align: top;">Ever heard of numpy ?</p>

</div>

<div>

<p style="text-align: center;"><b>Python 3 - numpy</b></p>

</div>

<p style="text-align: center;"><b>C++ 14 - xtensor</b></p>

</div>

np.array([[3, 4], [5, 6]])

arr.reshape([3, 4])

</code></pre>

</div>

xt::xarray<double>({{3, 4}, {5, 6}})

xt::xtensor<double, 2>({{3, 4}, {5, 6}})

arr.reshape({3, 4});

</code></pre>

</div>

np.linspace(1.0, 10.0, 100)

np.logspace(1.0, 10.0, 100)

np.arange(3, 7)

np.eye(4)

np.zeros([3, 4])

np.ones([3, 4])

</code></pre>

</div>

xt::linspace<double>(1.0, 10.0, 100)

xt::logspace<double>(1.0, 10.0, 100)

xt::arange(3, 7)

xt::eye(4)

xt::zeros<double>({3, 4})

xt::ones<double>({3, 4})

</code></pre>

</div>

</section>

<div>

<p style="text-align: center;"><b>Python 3 - numpy</b></p>

</div>

<p style="text-align: center;"><b>C++ 14 - xtensor</b></p>

</div>

a[:, np.newaxis]

a[:5, 1:]

a[5:1:-1, :]

np.broadcast(a, [4, 5, 7])

np.vectorize(f)

a[a > 5]

a[[0, 1], [0, 0]]

</code></pre>

</div>

xt::view(a, xt::all(), xt::newaxis())

xt::view(a, xt::range(_, 5), xt::range(1, _))

xt::view(a, xt::range(5, 1, -1), xt::all())

xt::broadcast(a, {4, 5, 7})

xt::vectorize(f)

xt::filter(a, a > 5)

xt::index_view(a, {{0, 0}, {1, 0}})

</code></pre>

</div>

np.sum(a, axis=[0, 1])

np.sum(a)

np.prod(a, axis=1)

np.prod(a)

np.mean(a, axis=1)

np.mean(a)

</code></pre>

</div>

xt::sum(a, {0, 1})

xt::sum(a)

xt::prod(a, {1})

xt::prod(a)

xt::mean(a, {1})

xt::mean(a)

</code></pre>

</div>

</section>

<div>

<p style="text-align: center;"><b>Python 3 - numpy</b></p>

</div>

<p style="text-align: center;"><b>C++ 14 - xtensor</b></p>

</div>

np.where(a > 5, a, b)

np.where(a > 5)

np.any(a)

np.all(a)

np.logical_and(a, b)

np.logical_or(a, b)

</code></pre>

</div>

xt::where(a > 5, a, b)

xt::where(a > 5)

xt::any(a)

xt::all(a)

a && b

a || b

</code></pre>

</div>

np.absolute(a)

np.exp(a)

np.sqrt(a)

np.cos(a)

np.cosh(a)

scipy.special.erf(a)

np.isnan(a)

</code></pre>

</div>

xt::abs(a)

xt::exp(a)

xt::sqrt(a)

xt::cos(a)

xt::cosh(a)

xt::erf(a)

xt::isnan(a)

</code></pre>

</div>

</section>

<div>

<p style="text-align: center;"><b>Python 3 - numpy</b></p>

</div>

<p style="text-align: center;"><b>C++ 14 - xtensor</b></p>

</div>

np.random.seed(0)

np.random.randn(10, 10)

np.random.randint(10, 10)

np.random.rand(3, 4)

</code></pre>

</div>

xt::random::seed(0)

xt::random::randn<double>({10, 10})

xt::random::randint<int>({10, 10}})

xt::random::rand<double>({3, 4}})

</code></pre>

</div>

np.stack([a, b, c], axis=1)

np.concatenate([a, b, c], axis=1)

</code></pre>

</div>

xt::stack(xtuple(a, b, c), 1)

xt::concatenate(xtuple(a, b, c), 1)

</code></pre>

</div>

</section>

<p>Broadcasting</p>

xarray<int> a = {{1, 2, 3, 4},

{5, 6, 7, 8},

{9, 10, 11, 12}};

xarray<int> b = { 1, 3, 5, 7};

</code></pre>

</div>

</div>

xarray<int> res = a + b;

</code></pre>

</div>

</div>

</section>

<p>Broadcasting</p>

xarray<double> a = {1., 2., 3., 4.};

</code></pre>

</div>

</div>

auto res = xt::broadcast(a, {3, 4});

</code></pre>

</div>

</div>

xarray<double> a, b, c;

// ... initialization of a, b, and c ...

auto res = broadcast(a + b * c, {3, 4});

</code></pre>

</div>

</div>

</section>

<p>Iteration</p>

Row-major iteration over the array <code>for x in np.nditer(a)</code>

</div>

for(auto it=a.begin(); it!=a.end(); ++it)

</code></pre>

</div>

Iterating over <code>a</code> with a prescribed broadcasting shape

</div>

a.begin({3, 4})

a.end({3, 4})

</code></pre>

</div>

Iterating over <code>a</code> in a column-major fashion

</div>

a.template begin<layout_type::column_major>()

a.template end<layout_type::column_major>()

</code></pre>

</div>

Iterating over <code>a</code> in a column-major fashion with a prescribed broadcasting shape

</div>

a.template begin<layout_type::column_major>({3, 4})

a.template end<layout_type::column_major>({3, 4})

</code></pre>

</div>

</section>

<h3>How do I try it without installing anything?</h3>

<p>http://quantstack.net/xtensor</pre>

<div>

</div>

</section>

</section>

<h3>Language bindings with xtensor</h3>

</section>

<p>A Simple Python extension (1/2)</p>

<p>C++: Using an algorithm from the STL on a numpy array</p>

#include <numeric> // Standard library import for std::accumulate

#include "pybind11/pybind11.h" // Pybind11 import to define Python bindings

#include "xtensor/xmath.hpp" // xtensor import for the C++ universal functions

#define FORCE_IMPORT_ARRAY // numpy C api loading

#include "xtensor-python/pyarray.hpp" // Numpy bindings

double sum_of_sines(xt::pyarray<double>& m)

{

auto sines = xt::sin(m);

// sines does not actually hold any value, which are only computed upon access

return std::accumulate(sines.begin(), sines.end(), 0.0);

}

PYBIND11_PLUGIN(xtensor_python_test)

{

xt::import_numpy();

pybind11::module m("xtensor_python_test", "Test module for xtensor python bindings");

m.def("sum_of_sines", sum_of_sines,

"Computes the sum of the sines of the values of the input array");

return m.ptr();

}

</code>

</pre>

</div>

</section>

<p>A simple Python extension (1/2)</p>

<p>Python: Using an algorithm from the STL on a numpy array</p>

import numpy as np

import xtensor_python_test as xt

a = np.arange(15).reshape(3, 5)

xt.sum_of_sines(v)

</code>

</pre>

</div>

</section>

<p>A simple Python extension (2/2)</p>

<p>C++: Create a universal function from a C++ scalar function</p>

#include "pybind11/pybind11.h"

#define FORCE_IMPORT_ARRAY

#include "xtensor-python/pyvectorize.hpp"

#include <numeric>

#include <cmath>

namespace py = pybind11;

double scalar_func(double i, double j)

{

return std::sin(i) - std::cos(j);

}

PYBIND11_PLUGIN(xtensor_python_test)

{

xt::import_numpy();

py::module m("xtensor_python_test", "Test module for xtensor python bindings");

m.def("vectorized_func", xt::pyvectorize(scalar_func), "");

return m.ptr();

}

</code>

</pre>

</div>

</section>

<p>A simple Python extension (2/2)</p>

<p>Python: Create a numpy-style universal function from a C++ scalar function</p>

import numpy as np

import xtensor_python_test as xt

x = np.arange(15).reshape(3, 5)

y = [1, 2, 3, 4, 5]

xt.vectorized_func(x, y)

</code>

</pre>

</div>

</section>

<p>A Simple Julia extension (1/2)</p>

<p>C++: Using an algorithm from the STL on a Julia array</p>

#include <numeric> // Standard library import for std::accumulate

#include "jlcxx/jlcxx.hpp¨ // CxxWrap import to define Julia bindings

#include "xtensor-julia/jltensor.hpp" // Import the jltensor container definition

#include "xtensor/xmath.hpp" // xtensor import for the C++ universal functions

double sum_of_sines(xt::jltensor<double, 2> m)

{

auto sines = xt::sin(m); // sines does not actually hold values.

return std::accumulate(sines.cbegin(), sines.cend(), 0.0);

}

JULIA_CPP_MODULE_BEGIN(registry)

jlcxx::Module mod = registry.create_module("xtensor_julia_test");

mod.method("sum_of_sines", sum_of_sines);

JULIA_CPP_MODULE_END

</code>

</pre>

</div>

</section>

<p>A simple Julia extension (1/2)</p>

<p>Julia: Using an algorithm from the STL on a Julia array</p>

using xtensor_julia_test

arr = [[1.0 2.0]

[3.0 4.0]]

sum_of_sines(arr)

</code>

</pre>

</div>

</section>

<p>A simple Julia extension (2/2)</p>

<p>C++: Create a numpy-style universal function from a C++ scalar function</p>

#include "jlcxx/jlcxx.hpp"

#include "xtensor-julia/jlvectorize.hpp"

double scalar_func(double i, double j)

{

return std::sin(i) - std::cos(j);

}

JULIA_CPP_MODULE_BEGIN(registry)

jlcxx::Module mod = registry.create_module("xtensor_julia_test");

mod.method("vectorized_func", xt::jlvectorize(scalar_func));

JULIA_CPP_MODULE_END

</code>

</pre>

</div>

</section>

<p>A simple Julia extension (2/2)</p>

<p>Julia: Create a numpy-style universal function from a C++ scalar function</p>

using xtensor_julia_test

x = [[ 0.0 1.0 2.0 3.0 4.0]

[ 5.0 6.0 7.0 8.0 9.0]

[10.0 11.0 12.0 13.0 14.0]]

y = [1.0, 2.0, 3.0, 4.0, 5.0]

xt.vectorized_func(x, y)

</code>

</pre>

</div>

</section>

<p>A Simple R extension</p>

<p>C++: Using an algorithm from the STL on a R array</p>

// [[Rcpp::export]]

double sum_of_sines(xt::rarray<int> m)

{

auto sines = xt::sin(m); // sines does not actually hold values.

return std::accumulate(sines.cbegin(), sines.cend(), 0.0);

}

</code>

</pre>

</div>

</section>

<p>A simple R extension</p>

<p>R: Using an algorithm from the STL on a R array</p>

library('xtensor_r_test')

arr <- array(c(c(1, 2), c(3, 4)), dim = c(2, 2)

xtensor_r_test::sum_of_sines(arr)

</code>

</pre>

</div>

</section>

<p>Generate your packaged xtensor extension</p>

<ul>

<li>With a few examples from the documentation</li>

<li>Unit-tests</li>

<li>HTML documentation</li>

<li>Packaging boilerplate (setup.py, build.jl)</li>

</ul>

</div>

</section>

<p>Bindings with BLAS libraries</p>

<p>BLAS-based implementation of <code>numpy.linalg</code></p>

<ul>

<li>ISO results with <code>numpy.linalg</code> is main goal</li>

<li>Seeking adoption by Python to C++ compilers (Pythran, Jet)</li>

<li>Works with any BLAS implementation (openblas, mkl, netlib)</li>

<li>See the <a href="http://xtensor.readthedocs.io/en/latest/numpy.html">numpy to xtensor cheat sheet</a></li>

</ul>

</div>

</section>

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