nadavrot

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

Faheetah

### Generic Dockerfile demonstrating good practices


### Imports

# Bad-ish, we do not need Ubuntu for this, nor do we want latest if we are using in a build system, predictable is better
FROM ubuntu:latest

# Better, using a small image since our app has no dependency on Ubuntu
FROM alpine:3.3

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

dutc

themes

1. CPython for greater understanding of the Python programming language (but "reference implementations always overspecify") Reading source to solve problems
2. getting involved, contributing to the project

introduction

This workshop will cover the basics of the CPython runtime and interpreter. There is an enormous amount of material to cover, and I'll try to to rush through as much as I can.