Each C++ expression (an operator with its arguments, a literal, a variable name, etc) is characterized by two independent properties: a type and a value category. Each expression has some non-reference type, and each expression belongs to exactly one of the three primary value categories.

The Linux kernel is an incredible circus performer, carefully juggling many processes and their resource needs to keep your server humming along. The kernel is also all about equity: when there is competition for resources, the kernel tries to distribute those resources fairly.

However, what if you've got an important process that needs priority? What about a low-priority process? Or what about limiting resources for a group of a processes?

A vulnerability in the guest network web interface of the Belkin N750 DB Wi-Fi Dual-Band N+ Gigabit Router with firmware F9K1103_WW_1.10.16m, allows an unauthenticated remote attacker to gain root access to the operating system of the affected device. The guest network functionality is default functionality and is delivered over an unprotected wifi network.

Successful exploitation of the vulnerability enables the attacker to gain full control of the affected router.

One of the new features in C++11 aimed at increased code efficiency is the emplace family of methods in containers. std::vector, for example, has an emplace_back method to parallel push_back, and emplace to parallel insert.

3,000 syscalls a second, on an idle machine? That doesn't seem right.

rr is a lightweight tool for recording and replaying execution of applications (trees of processes and threads). More information about the project, including instructions on how to install, run, and build rr, is at http://rr-project.org.

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns on recent CPU
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 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs 4X memory
Round trip within same datacenter ...... 500,000 ns = 0.5 ms 20x datacenter roundtrip
Read 1 MB sequentially from SSD* ..... 1,000,000 ns = 1 ms 80x memory, 20X SSD
Disk seek ........................... 10,000,000 ns = 10 ms
Read 1 MB sequentially from disk .... 20,000,000 ns = 20 ms
Send packet CA->Netherlands->CA .... 150,000,000 ns = 150 ms
LTO4 tape seek/access time ...... 55.000.000.000 ns = 55 s

The development of caches and caching is one of the most significant events in the history of computing. Virtually every modern CPU core from ultra-low power chips like the ARM Cortex-A5 to the highest-end Intel Core i7 use caches. Even higher-end microcontrollers often have small caches or offer them as options — the performance benefits are too significant to ignore, even in ultra low-power designs.

How does unicode works, pitfall etc

Namely, how can we merge multiple type-erased interfaces into one single interface. A similar question is also asked in the end of the first talk: How to apply type erasure to types with overlapping interfaces?