glibc VS libjxl

I am writing a short paper for school and the topic is Fast Inverse Square Root and alternatives. One of the questions is how the sqrt-function is implemented in glibc. Here is the code of that function.

The code isn't the easiest to read but in glibc it seems that the syscall is only performed if waiter are detected in userspace during an unlock operation

https://github.com/lattera/glibc/blob/master/nptl/pthread_mu...

You might still not realize, but floats are a large topic. Have a look at eg. this implementation here: https://github.com/lattera/glibc/blob/master/stdlib/strtod_l.c

This is a nice write up, thank you. However, I'm stilling interpreting this as a "fun trick" rather than the common sense method for solving the problem. For example, looking at the source code for htonl() from glibc: https://github.com/lattera/glibc/blob/master/inet/htonl.c

Second of all: The horrifying truth is that there is no such thing as a canonical text representation for IPv4 (source) (and yes... I am indeed citing the failed attempt to standardize this as my source for it not being standardized). Authoritatively speaking, all possible (non-binary) representations are equally invalid. In fact, text address resolution is typically delegated to the OS kernel, so what constitutes a "usable" address is liable to vary depending on if you're using Linux, OSX, Windows, or Other.

Optimized code gets really weird. The creators of strlen, for example, decided that iterating over each character to find the end was far too slow. So instead, they convert the character pointer into an int pointer and do bitwise manipulation with the int on two different magic numbers so they can check four/eight characters at once.

If you're using a cross-platform C compiler like gcc or clang, you're usually expected to switch to assembly. Here's the syscall instruction in glibc

Maybe you should include the math part of a libc statically with your code. glibc is one option, or dietlibc if you want it to be as small as possible.

> Regarding JPEG XL's mobile support, it makes sense it would see limited development if the company that manages one of the biggest mobile players has been the greatest restriction on their success. The lack of support also disincentivises manufacturers to prioritise support.

There was literally no involvement from any hardware vendor in the standardization of JPEG XL. It went from a Call for Proposals in Sept 2018 to Committee Draft in Aug 2019 with very little time for industry feedback. Contrast this with AV1 which had involvement from hardware vendors Intel, NVIDIA, Arm, AMD, Broadcom, Amlogic from the beginning as well as companies who ship media on hardware at scale such as Cisco, Netflix, Samsung and yes Google. These companies reviewed and provided significant feedback on the format that made it suitable for hardware implementation.

https://news.ycombinator.com/threads?id=JyrkiAlakuijala is a lead on the project and a Google employee, and active in JPEG XL development https://github.com/libjxl/libjxl/commits?author=jyrkialakuij...

https://github.com/libjxl/libjxl/blob/main/doc/format_overvi... is a pretty detailed but good overview. The highlights are variable size DCT (up to 128x128), ANS entropy prediction, and chroma from luminance prediction. https://github.com/libjxl/libjxl/blob/main/doc/encode_effort... also gives a good breakdown of features by effort level.

For JPEG XL, refer to its format overview [1]. In short its lossless mode uses a combination of multiple techniques: the rANS coding with an alias table, LZ77, reversible color transforms, a general vector quantization that subsumes palettes, a modified Haar transform and a learnable meta-adaptive decision tree for context modelling.

One good thing about JPEG XL is that its lossy mode also largely uses the same tool, with a major addition of specialized quantization and context modelling for low- and high-frequenty components.

[1] https://github.com/libjxl/libjxl/blob/main/doc/format_overvi...

The cjxl source is here. If you spot any reason why -x strip=exif may not work, tell me.

The problem is that the instructions for actually running the WASM file are not that clear... the docs the author mentions shows how to compile to WASM, which is easy enough, but then here's the instructions to make that actually work in the browser:

https://github.com/libjxl/libjxl/blob/main/tools/wasm_demo/R...

Yeah, you need some mysterious Python script, a JS service worker at runtime, choose whether you want the WASM or WASM_SIMD target, use a browser that supports Threads and SIMD if you chose that, make sure to serve everything with the appropriate custom HTTP headers... just reading that, I can see that to get this stuff working on non-browser WASM targets would likely require expertise in WASM, which is the point of the OP. WASM's UX is just not there yet.

https://github.com/libjxl/libjxl#usage

> Specifically for JPEG files, the default cjxl behavior is to apply lossless recompression and the default djxl behavior is to reconstruct the original JPEG file (when the extension of the output file is .jpg).

I mean compiling a bleeding edge kicad, inkscape or jpeg-xl is easy. But will probably trash your system if you already have an older version installed.

But you look at your image viewer that could have the lossless indicator? (and there is an issue open to add this indicator to the jxl files)

https://github.com/libjxl/libjxl/issues/432