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

dherman

Goal

Typed arrays can be copied or transferred between workers, but it's not possible for multiple workers to work with a buffer in parallel without copies. This document describes two ways to improve this without introducing data races: transferring read-write access to disjoint regions of buffers, and transferring read-only access to shared buffers/regions.

This variant of the API enables fine-grained borrowing and sharing, where a single ArrayBuffer can have multiple disjoint regions parceled out. This way individual workers can work with their regions at their original indices. This makes the API more amenable to being a compilation target.

Example

Here is an example that demonstrates sharing a read-only segment and multiple read-write segments with four separate workers.

zhpengg

/* Skip Lists: A Probabilistic Alternative to Balanced Trees */

#include <stdlib.h>
#include <stdio.h>
#include <limits.h>

#define SKIPLIST_MAX_LEVEL 6

typedef struct snode {
    int key;