Picking the right architecture = Picking the right battles + Managing trade-offs
In this article we compared different behavior of static, inline and static inline free functions in compiled binary.
All the following test was done under g++ 7.1.1 on Linux amd64, ELF64.
#pragma once
inline int only_inline() { return 42; }
static int only_static() { return 42; }| # | |
| # 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 |
| # LVDB - LLOOGG Memory DB | |
| # Copyriht (C) 2009 Salvatore Sanfilippo <antirez@gmail.com> | |
| # All Rights Reserved | |
| # TODO | |
| # - cron with cleanup of timedout clients, automatic dump | |
| # - the dump should use array startsearch to write it line by line | |
| # and may just use gets to read element by element and load the whole state. | |
| # - 'help','stopserver','saveandstopserver','save','load','reset','keys' commands. | |
| # - ttl with milliseconds resolution 'ttl a 1000'. Check ttl in dump! |
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).
Matrix multiplication is a mathematical operation that defines the product of
这里的“流量”一般定义为中间人观测到的一组由（时间，方向，包大小）元数据组成的序列 [Cai2014]。其源头是应用层的读写操作，经过传输层协议的变换（分片、协议状态机、加密等），流量序列产生一定变化。但是这种变化非常有限，因为流量的发生过程本质是确定性的，随机因素较小，因此对于特定环境中的特定应用（浏览器访问 google.com）各种流量特征体现出相当大的一致性和独特性，这就使“从流量特征识别应用”的监督学习问题成为可能。虽然有若干不利因素使得确定性下降，例如多层次上软件多版本的排列组合爆炸、有状态的缓存、流水线和连接复用、用户随机行为，但是因为版本的幂律分布、应用层读写操作间的依赖关系、流量特征和检测算法的改进等原因，分类依然具有相当的可行性。
根据分类的对象产生了两个相近但是不同的研究领域。从流量特征中分类应用类型的被称为流量分类（traffic classification），从流量特征中分类所访问网站或者网页的被称为网站指纹（website fingerprinting）。以机器学习的方法而论前者是比后者更弱但本质相同的一个问题。
这两类攻击的威胁类型不同。流量分类威胁的是可用性，如果GFW检出流量是隧道应用然后进行封锁，则破坏了可用性。而网站指纹威胁的是匿名性和隐私，如果从隐秘流量中检出是谁在访问哪个网站，则破坏了匿名性，丝绸之路就是这样被FBI破获的。
⇐ back to the gist-blog at jrw.fi
Or, 16 cool things you may not have known your stylesheets could do. I'd rather have kept it to a nice round number like 10, but they just kept coming. Sorry.
I've been using SCSS/SASS for most of my styling work since 2009, and I'm a huge fan of Compass (by the great @chriseppstein). It really helped many of us through the darkest cross-browser crap. Even though browsers are increasingly playing nice with CSS, another problem has become very topical: managing the complexity in stylesheets as our in-browser apps get larger and larger. SCSS is an indispensable tool for dealing with this.
This isn't an introduction to the language by a long shot; many things probably won't make sense unless you have some SCSS under your belt already. That said, if you're not yet comfy with the basics, check out the aweso
##Google Interview Questions: Product Marketing Manager
This is a proof of concept of a portable operating system a-la-docker.
It is a single binary that contains an image of an OS in SquashFS and the binary to run it.
a make will build the binary with an embedded alpine image. make havre-xenial will build a binary with an embedded ubuntu image.
What does the binary do?
| # Change YOUR_TOKEN to your prerender token | |
| # Change example.com (server_name) to your website url | |
| # Change /path/to/your/root to the correct value | |
| server { | |
| listen 80; | |
| server_name example.com; | |
| root /path/to/your/root; | |
| index index.html; |
