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Hitchhickers Guide to D&D ๐Ÿ‰

south hall 2a
30 minutes


This talk is meant to be an hitchhickers guide to Dungeons and Dragons (D&D) for programmers. We will leverage on our wit and intelligence to explore a very perilious dungeon ๐Ÿง™ , where a venomous dragon is hiding in the shadows ๐Ÿ‰ . Thanks to a magical potion in an ancient flask, our wizardly skills have been enhanced with Pythonic capabilities ๐Ÿ making us the most powerful and geeky magician of the realm. These new acquired power revealed unprecedented strategies (i.e. algorithms ๐Ÿ™ƒ) that will guide us through the maze avoiding all the traps and pitfalls โš”๏ธ, and will help us maximising the power of our fire magic โ˜„๏ธ to finally slay the dragon.

If you would like to know more about this new Pythonic spell, and the secrets it unveiled, or if you're simply interested in new graph algorithms that can run balzingly fast maximising your CPU capabilities, this is the talk for you!

TalkPyData: Software Packages & Jupyter


I am playing D&D since I was 13, and that is indeed a fantastic game for so many reasons. Even more fantastic if you could combine your geeky programming skills to it, whenever you have to explore hidden dungeons. In facts, graphs are the most versatile, and fascinating data abstraction that could help us handling these challenges in a very programmatic way. In Python we would have many solutions to work with Graph problems: from scipy.sparse to networkx. However, none of these solutions are generally known to be fast and efficient, and this can represent a huge impediment when the size of the graph in question (in terms of nodes, and edges) increases. But What if graph algorithms could be expressed as linear algebraic operations ? And what if this translation would make graph algorithms super efficient so that this would represent a viable and scalable alternative for high-performance graph analytics ? And what if we could leverage on these new and blanzingly fast algiorithms, while still using the same libraries (and abstractions) we would normally use with networkx?

In this talk, we will introduce python-graphblas, i.e. the official Python API to GraphBLAS: a powerful framework for creating graph algorithms expressed as linear algebra sparse matrix operations.
We will explore two practical examples (working on our D&D use case) showcasing performance, and how python-graphblas with graphblas-algorithms integrate with networkX.

The speaker

Valerio Maggio

Valerio Maggio

Valerio Maggio is a Researcher, a Data scientist Advocate at Anaconda, and a casual "Magic: The Gathering" wizard. He is well versed in open science and research software, supporting the adoption of best software development practice (e.g. Code Review) in Data Science. Valerio is also an open-source contributor, and an active member of the Python community. Over the last twelve years he has contributed and volunteered to the organization of many international conferences and community meetups like PyCon Italy, PyData, EuroPython, and EuroSciPy. All his talks, workshop materials and random ramblings are publicly available on his Speaker Deck and GitHub profiles.

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