netj

#!/usr/bin/env bash
# memusg -- Measure memory usage of processes
# Usage: memusg COMMAND [ARGS]...
#
# Author: Jaeho Shin <netj@sparcs.org>
# Created: 2010-08-16
############################################################################
# Copyright 2010 Jaeho Shin.                                               #
#                                                                          #
# Licensed under the Apache License, Version 2.0 (the "License");          #

henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

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

0XDE57

ABOUT

about:config settings to harden the Firefox browser. Privacy and performance enhancements.
To change these settings type 'about:config' in the url bar. Then search the setting you would like to change and modify the value. Some settings may break certain websites from functioning and rendering normally. Some settings may also make firefox unstable. I am not liable for any damages/loss of data.

Not all these changes are necessary and will be dependent upon your usage and hardware. Do some research on settings if you don't understand what they do. These settings are best combined with your standard privacy extensions (HTTPS Everywhere No longer required: Enable HTTPS-Only Mode, NoScript/Request Policy, uBlock origin, agent spoofing, Privacy Badger etc), and all plugins set to "Ask To Activate".

simonw

How to recover lost Python source code if it's still resident in-memory

I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6

Attach a shell to the docker container

Install GDB (needed by pyrasite)

apt-get update && apt-get install gdb

kennwhite

package main

import (
	"crypto/tls"
	"golang.org/x/crypto/acme/autocert"
	"log"
	"net"
	"net/http"
)

aparrish

djspiewak

Quick Tips for Fast Code on the JVM

I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.

This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea

enricofoltran

package main

import (
	"context"
	"flag"
	"fmt"
	"log"
	"net/http"
	"os"
	"os/signal"

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