In this post, we will explore how Unix pipes are implemented in Linux by iteratively optimizing a test program that writes and reads data through a pipe.

We will proceed as follows:
A first slow version of our pipe test bench;
How pipes are implemented internally, and why writing and reading from them is slow;
How the vmsplice and splice syscalls let us get around some (but not all!) of the slowness;
A description of Linux paging, leading up to a faster version using huge pages;
The final optimization, replacing polling with busy looping;
Some closing thoughts.

source: L

Everything below refers to x86 and linux, unless otherwise indicated. History has a tendency to repeat itself, and a lot of things that were new to x86 were old hat to supercomputing, mainframe, and workstation folks.

x86 chips have picked up a lot of new features and whiz-bang gadgets.

Overall, a pretty good introduction to modern CPUs, performance, and concurrency.

In the rest of this post I’ll walk through a typical C mutex API, compare with a typical Rust mutex API, and look at what happens if we change the Rust API to resemble C in various ways.

source: HN

Some days ago I noticed that on a Mac, doing snprintf calls from multiple threads shows curious lack of scaling (see tweet). Replacing snprintf with {fmt} library can speed up the OBJ exporter in Blender 3.2 by 3-4 times. This could have been the end of the story, filed under a “eh, sprintf is bad!” drawer, but I started to wonder why it shows this lack of scaling.

source: HN

Intro to porting games between platforms, then also a deep walkthrough of a custom allocator libary.

We successfully deployed ThreadSanitizer in the Firefox project to eliminate data races in our remaining C/C++ components. In the process, we found several impactful bugs and can safely say that data races are often underestimated in terms of their impact on program correctness. We recommend that all multithreaded C/C++ projects adopt the ThreadSanitizer tool to enhance code quality.

source: HN

This is intended to be a casual introduction to the perils of lock-free programming (which I last wrote about some fifteen years ago), but also some explanation of why ARM’s weak memory model breaks some code, and why that code was probably broken already. I also want to explain why C++11 made the lock-free situation strictly better (objections to the contrary notwithstanding).

The future was multithreading and we had to use the libdispatch to get there. So we did.

As we went down that rabbit hole, things got progressively worse.

source: L

The behavior of the Windows scheduler changed significantly in Windows 10 2004, in a way that will break a few applications, and there appears to have been no announcement, and the documentation has not been updated. This isn’t the first time this has happened, but this change seems bigger than last time.

The short version is that calls to timeBeginPeriod from one process now affect other processes less than they used to, but there is still an effect.

Ammar checked, and sure enough, his code was sending hundreds of thousands of requests per second. It didn’t take him long to figure out why: requests from the watchdog were failing with a 500 error, so it called the login method. The login method had been succeeding, so another watchdog got scheduled. Thirty seconds later, that failed, as did all the previously scheduled watchdogs, which all called login again. Which, on success, scheduled a fresh round of watchdogs. Every thirty seconds, the number of scheduled calls doubled. Before long, Ammar’s code was DoSing the API.

Fun times.

I still believe it would be possible to build a high quality editor based on the original design. But I also believe that this would be quite a complex system, and require significantly more work than necessary.

A few good ideas and observations could be mined out of this post.

source: L

Since the 2.0 kernel release, Linux has supported a large number of SMP systems based on a variety of CPUs. Linux has done an excellent job of abstracting differences among these CPUs, even in kernel code. This article is an overview of one important difference: how CPUs allow memory accesses to be reordered in SMP systems.

Part 2 really gets into it: https://www.linuxjournal.com/article/8212

Also: https://www.cs.utexas.edu/~bornholt/post/memory-models.html

This week I was debugging a misbehaving Python program that makes significant use of Python’s asyncio. The program would eventually take very long periods of time to respond to network requests. My first suspicion was a CPU-heavy coroutine hogging the thread, preventing the socket coroutines from running, but an inspection with pdb showed this wasn’t the case. Instead, the program’s author had made a couple of fundamental mistakes using asyncio. Let’s discuss them using small examples.

I’ve noticed a small pattern across a few of my projects where I had vectorized and parallelized some code. The original algorithm had a “push” approach, the optimized version instead took a “pull” approach. In this article I’ll describe what I mean, though it’s mostly just so I can show off some pretty videos, pictures, and demos.

This investigation started, as so many of mine do, with me minding my own business, not looking for trouble. In this case all I was doing was opening my laptop lid and trying to log on. The first few times that this resulted in a twenty-second delay I ignored the problem, hoping that it would go away. The next few times I thought about investigating, but performance problems that occur before you have even logged on are trickier to solve, and I was feeling lazy. When I noticed that I was avoiding closing my laptop because I dreaded the all-too-frequent delays when opening it I realized it was time to get serious.

A lot of effort for a rather unsatisfactory conclusion, but I won’t spoil the surprise.

This talk shows how two bugs involving somewhat narrow-looking race windows (https://crbug.com/project-zero/1695 in the Linux kernel, https://crbug.com/project-zero/1741 in Android userspace code) can be stretched wide enough to win the race conditions on a Google Pixel 2 phone, running a Linux 4.4 kernel, by making use of the unprivileged sched_*() syscalls.

source: grugq

When I started looking into the relevant standard library code I expected to find that things like net.Dialer.DialContext() had special hooks into the runtime’s network poller (netpoller) to do this. This turns out to not be the case; instead dialing uses an interesting and elegant approach that’s open to everyone doing network IO.

In order to abort an outstanding dial operation if the context is cancelled, the net package simply sets an expired (write) deadline.

async-std is a mature and stable port of the Rust standard library to its new async/await world, designed to make async programming easy, efficient, worry- and error-free.

Today, we’re introducing the new async-std runtime. It features a lot of improvements, but the main news is that it eliminates a major source of bugs and performance issues in concurrent programs: accidental blocking.

source: L

One of Go’s signature features is goroutines, which are lightweight threads that are managed by the Go runtime. The Go runtime implements goroutines using a M:N work stealing scheduler to multiplex goroutines on to operating system threads. The scheduler has special terminology for three important entities; a G is a goroutine, an M is an OS thread (a ‘machine’), and a P is a ‘processor’, which at its core is a limited resource that must be claimed by an M in order to run Go code. Having a limited supply of Ps is how Go limits how many things it will do at once, so as to not overload the overall system; generally there is one P per actual CPU that the OS reports (the number of Ps is GOMAXPROCS).

source: HN

I was on a mail thread today, the topic for which was the meaning—and perhaps lack of comprehensiveness—of the statement: “This type is thread safe.” Similar statements are scattered throughout our product documentation, without any good central explanation of its meaning and any caveats.