jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

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

tylerneylon

-- Two dashes start a one-line comment.

--[[
     Adding two ['s and ]'s makes it a
     multi-line comment.
--]]

----------------------------------------------------
-- 1. Variables and flow control.
----------------------------------------------------

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

macintux

• https://dancres.github.io/Pages/
• https://ferd.ca/a-distributed-systems-reading-list.html
• http://the-paper-trail.org/blog/distributed-systems-theory-for-the-distributed-systems-engineer/
• https://github.com/palvaro/CMPS290S-Winter16/blob/master/readings.md
• http://muratbuffalo.blogspot.com/2015/12/my-distributed-systems-seminars-reading.html
• http://christophermeiklejohn.com/distributed/systems/2013/07/12/readings-in-distributed-systems.html
• http://michaelrbernste.in/2013/11/06/distributed-systems-archaeology-works-cited.html
• http://rxin.github.io/db-readings/
• http://research.microsoft.com/en-us/um/people/lamport/pubs/pubs.html
• http://pdos.csail.mit.edu/dsrg/papers/

gregsh

Here are my attempts to script an IntelliJ-based IDE using javax.script.* API (ex-JSR-223).

The list of available scripting languages and engines:

1. Groovy - built-in, via Groovy jars and <app>/lib/groovy-jsr223-xxx.jar
2. JavaScript (Nashorn) - built-in, via Java Runtime <app>/jbr/... (deprecated and will be removed soon)
3. JavaScript (GraalJS) - https://plugins.jetbrains.com/plugin/12548-intellij-scripting-javascript
4. JPython - https://plugins.jetbrains.com/plugin/12471-intellij-scripting-python
5. JRuby - https://plugins.jetbrains.com/plugin/12549-intellij-scripting-ruby
6. Clojure - https://plugins.jetbrains.com/plugin/12469-intellij-scripting-clojure

markhibberd

package argonaut.example

import argonaut._, Argonaut._
import scalaz._, Scalaz._

object UnionExample extends {

  sealed trait Thing
  final case class One(n: String, i: Int) extends Thing
  final case class Two(n: String) extends Thing

djspiewak

Getting Started in Scala

This is my attempt to give Scala newcomers a quick-and-easy rundown to the prerequisite steps they need to a) try Scala, and b) get a standard project up and running on their machine. I'm not going to talk about the language at all; there are plenty of better resources a google search away. This is just focused on the prerequisite tooling and machine setup. I will not be assuming you have any background in JVM languages. So if you're coming from Python, Ruby, JavaScript, Haskell, or anywhere…  I hope to present the information you need without assuming anything.

Disclaimer It has been over a decade since I was new to Scala, and when I was new to Scala, I was coming from a Java and Ruby background. This has probably caused me to unknowingly make some assumptions. Please feel free to call me out in comments/tweets!

One assumption I'm knowingly making is that you're on a Unix-like platform. Sorry, Windows users.

Getting the JVM

sigrlami

WARNING This list outdated, for the up to date version visit https://haskellcosm.com

List of companies that use Haskell in Production

Types of work:

• RD - research&development
• PR - product
• IP - in-house product
• CO - consulting