jhasse

#include <iostream>
#include <boost/uuid/sha1.hpp>

void display(char* hash)
{
	std::cout << "SHA1: " << std::hex;
    for(int i = 0; i < 20; ++i)
    {
        std::cout << ((hash[i] & 0x000000F0) >> 4) 
                  <<  (hash[i] & 0x0000000F);

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

peterjmit

#!/bin/bash
# REQUIRES SUDO
# Benchmark runner

repeats=20
output_file='benchmark_results.csv'
command_to_run='echo 1'

run_tests() {
    # --------------------------------------------------------------------------

bytespider

#!/bin/bash
# REQUIRES SUDO
# Benchmark runner

repeats=20
output_file='benchmark_results.csv'
command_to_run='echo 1'

run_tests() {
    # --------------------------------------------------------------------------

skyscribe

#                                                                                                        
#   STL GDB evaluators/views/utilities - 1.03
#
#   The new GDB commands:                                                         
# 	    are entirely non instrumental                                             
# 	    do not depend on any "inline"(s) - e.g. size(), [], etc
#       are extremely tolerant to debugger settings
#                                                                                 
#   This file should be "included" in .gdbinit as following:
#   source stl-views.gdb or just paste it into your .gdbinit file

adewes

import random

def get_random_color(pastel_factor = 0.5):
    return [(x+pastel_factor)/(1.0+pastel_factor) for x in [random.uniform(0,1.0) for i in [1,2,3]]]

def color_distance(c1,c2):
    return sum([abs(x[0]-x[1]) for x in zip(c1,c2)])

def generate_new_color(existing_colors,pastel_factor = 0.5):
    max_distance = None

krebernisak

git log [TAG1]..[TAG2] --oneline

ddemidov

lorenz: lorenz.cpp
	g++ -o $@ $^ -std=c++11 -O3 \
	    -I${HOME}/work/odeint-v2 \
	    -I${HOME}/work/opencl/compute/include/ \
	    -lOpenCL -lboost_system -lboost_timer

blackfalcon

Git-workflow vs feature branching

When working with Git, there are two prevailing workflows are Git workflow and feature branches. IMHO, being more of a subscriber to continuous integration, I feel that the feature branch workflow is better suited, and the focus of this article.

If you are new to Git and Git-workflows, I suggest reading the atlassian.com Git Workflow article in addition to this as there is more detail there than presented here.

I admit, using Bash in the command line with the standard configuration leaves a bit to be desired when it comes to awareness of state. A tool that I suggest using follows these instructions on setting up GIT Bash autocompletion. This tool will assist you to better visualize the state of a branc

sukinull

#include <boost/bind.hpp>
#include "EventBus.h"

std::hash<std::string> Event::key;

void EventBus::Init()
{
	is_io_running = true;

	m_work.reset(new boost::asio::io_service::work(io_service));