syuu1228

# setting up irq affinity according to /proc/interrupts
# 2008-11-25 Robert Olsson
# 2009-02-19 updated by Jesse Brandeburg
#
# > Dave Miller:
# (To get consistent naming in /proc/interrups)
# I would suggest that people use something like:
#  char buf[IFNAMSIZ+6];
#
#	sprintf(buf, "%s-%s-%d",

wmealing

A non-maskable interrupt (NMI) is an interrupt type which differs from standard interrupt mechanism by enforcing attention from the interrupt processor (usually the CPU).  This solution discusses an NMI is in more depth and how they are handled.

### What is an Interrupt ? ###

Modern systems architecture has created tightly coupled connect between system components.  Work for components can be handed off to a component for completion.  Rather than wait for the component the main CPU can be tasked to do other pending work.

When the component has completed its work it will raise a signal to the main processor.  The main processor considers this signal an "interrupt", as the current work on the CPU will be interrupted immediately   Each component has a number assigned to it.

### Why "mask" an interrupt ? ### 

badboy

Original link: http://www.concentric.net/~Ttwang/tech/inthash.htm
Taken from: http://web.archive.org/web/20071223173210/http://www.concentric.net/~Ttwang/tech/inthash.htm
Reformatted using pandoc

Integer Hash Function

Thomas Wang, Jan 1997

last update Mar 2007

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

fsodogandji

#To create a classic TCP listening daemon, similar to netcat -l, use a variation of the following command.
socat TCP-LISTEN:8080 stdout

#use remotly a command shell
socat TCP4-LISTEN:1234,reuseaddr,fork 'SYSTEM:/bin/cat /home/infos.txt'

#sslify a server
socat OPENSSL-LISTEN:443,reuse‐addr,pf=ip4,fork,cert=server.pem,cafile=client.crt TCP4-CONNECT:localhost:80

soarez

How to setup your own CA with OpenSSL

For educational reasons I've decided to create my own CA. Here is what I learned.

First things first

Lets get some context first.

myusuf3

To remove a submodule you need to:

• Delete the relevant section from the .gitmodules file.
• Stage the .gitmodules changes git add .gitmodules
• Delete the relevant section from .git/config.
• Run git rm --cached path_to_submodule (no trailing slash).
• Run rm -rf .git/modules/path_to_submodule (no trailing slash).
• Commit git commit -m "Removed submodule "
• Delete the now untracked submodule files rm -rf path_to_submodule

martinmoene

// Sean Parent. Inheritance Is The Base Class of Evil. Going Native 2013
// Video: https://www.youtube.com/watch?v=bIhUE5uUFOA
// Code : https://github.com/sean-parent/sean-parent.github.io/wiki/Papers-and-Presentations

/*
    Copyright 2013 Adobe Systems Incorporated
    Distributed under the MIT License (see license at
    http://stlab.adobe.com/licenses.html)

    This file is intended as example code and is not production quality.

brendangregg

#!/usr/bin/python
#
# chaintest   Summarize off-CPU time by kernel stack + 2 waker stacks
#             WORK IN PROGRESS. For Linux, uses BCC, eBPF.
#
# USAGE: chaintest [-h] [-u] [-p PID] [-i INTERVAL] [-T] [duration]
#
# PLEASE DO NOT RUN THIS IN PRODUCTION! This is a work in progress, intended to
# explore chain graphs on Linux, using eBPF capabilities from a particular
# kernel version (4.3ish). This tool will eventually get much better.

shagunsodhani

The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.

Issues With Training Deep Neural Networks

Internal Covariate shift

Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.

It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.