simdjson
ClangBuildAnalyzer
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simdjson | ClangBuildAnalyzer | |
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63 | 4 | |
18,275 | 904 | |
1.9% | - | |
9.2 | 5.7 | |
8 days ago | about 1 month ago | |
C++ | C++ | |
Apache License 2.0 | The Unlicense |
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simdjson
- 1BRC Merykitty's Magic SWAR: 8 Lines of Code Explained in 3k Words
- Training great LLMs from ground zero in the wilderness as a startup
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Use any web browser as GUI, with Zig in the back end and HTML5 in the front end
String parsing is negligible compared to the speed of the DOM which is glacially slow: https://news.ycombinator.com/item?id=38835920
Come on, people, make an effort to learn how insanely fast computers are, and how insanely inefficient our software is.
String parsing can be done at gigabytes per second: https://github.com/simdjson/simdjson If you think that is the slowest operation in the browser, please find some resources that talk about what is actually happening in the browser?
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Cray-1 performance vs. modern CPUs
Thanks for all the detailed information! That answers a bunch of my questions and the implementation of strlen is nice.
The instruction I was thinking of is pshufb. An example ‘weird’ use can be found for detecting white space in simdjson: https://github.com/simdjson/simdjson/blob/24b44309fb52c3e2c5...
This works as follows:
1. Observe that each ascii whitespace character ends with a different nibble.
2. Make some vector of 16 bytes which has the white space character whose final nibble is the index of the byte, or some other character with a different final nibble from the byte (eg first element is space =0x20, next could be eg 0xff but not 0xf1 as that ends in the same nibble as index)
3. For each block where you want to find white space, compute pcmpeqb(pshufb(whitespace, input), input). The rules of pshufb mean (a) non-ascii (ie bit 7 set) characters go to 0 so will compare false, (b) other characters are replaced with an element of whitespace according to their last nibble so will compare equal only if they are that whitespace character.
I’m not sure how easy it would be to do such tricks with vgather.vv. In particular, the length of the input doesn’t matter (could be longer) but the length of white space must be 16 bytes. I’m not sure how the whole vlen stuff interacts with tricks like this where you (a) require certain fixed lengths and (b) may have different lengths for tables and input vectors. (and indeed there might just be better ways, eg you could imagine an operation with a 256-bit register where you permute some vector of bytes by sign-extending the nth bit of the 256-bit register into the result where the input byte is n).
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Codebases to read
Additionally, if you like low level stuff, check out libfmt (https://github.com/fmtlib/fmt) - not a big project, not difficult to understand. Or something like simdjson (https://github.com/simdjson/simdjson).
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Building a high performance JSON parser
Everything you said is totally reasonable. I'm a big fan of napkin math and theoretical upper bounds on performance.
simdjson (https://github.com/simdjson/simdjson) claims to fully parse JSON on the order of 3 GB/sec. Which is faster than OP's Go whitespace parsing! These tests are running on different hardware so it's not apples-to-apples.
The phrase "cannot go faster than this" is just begging for a "well ackshully". Which I hate to do. But the fact that there is an existence proof of Problem A running faster in C++ SIMD than OP's Probably B scalar Go is quite interesting and worth calling out imho. But I admit it doesn't change the rest of the post.
The walkthrough is very nice, how to do this if you're going to do it.
If you're going for pure performance in a production environment you might take a look at Daniel Lemire's work: https://github.com/simdjson/simdjson. Or the MinIO port of it to Go: https://github.com/minio/simdjson-go.
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New package : lspce - a simple LSP Client for Emacs
I have same question as /u/JDRiverRun : how do you deal with JSON, do you parse json on Rust side or on Emacs side. I see that you are requiring json.el in your lspce.el, but I haven't looked through entire file carefully. If you parse on Rust side, do you use simdjson (there are at least two Rust bindings to it)? If yes, what are your impressions, experiences compared to more "standard" json library?
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Any fresh jvm21 benchmarks ?
I expect a lot of transcoders will be rewritten when the Vector instructions land. You can see speedups when used in other languages, such as simdjson. Please try to be more thoughtful and not disregard other people's hard work so easily.
ClangBuildAnalyzer
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IncludeGuardian - improve build times by removing expensive includes
ClangBuildAnalyzer reports on parsing, build, and link time, whereas IncludeGuardian only reports on parsing time.
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"Fast Kernel Headers" Tree -v1: Eliminate the Linux kernel's "Dependency Hell"
https://github.com/aras-p/ClangBuildAnalyzer is a very useful tool to quantify the cost of different headers (and other costly parts of the compile such as template instantiations). It doesn’t help with actually fixing such problems, but it’s a pretty good ruler to measure where the time is spent.
What are some alternatives?
RapidJSON - A fast JSON parser/generator for C++ with both SAX/DOM style API
jsoniter - jsoniter (json-iterator) is fast and flexible JSON parser available in Java and Go
json - JSON for Modern C++
json-schema-validator - JSON schema validator for JSON for Modern C++
JsonCpp - A C++ library for interacting with JSON.
json - A C++11 library for parsing and serializing JSON to and from a DOM container in memory.
sonic - A blazingly fast JSON serializing & deserializing library
json_struct - json_struct is a single header only C++ library for parsing JSON directly to C++ structs and vice versa
json-c - https://github.com/json-c/json-c is the official code repository for json-c. See the wiki for release tarballs for download. API docs at http://json-c.github.io/json-c/
jsoncons - A C++, header-only library for constructing JSON and JSON-like data formats, with JSON Pointer, JSON Patch, JSON Schema, JSONPath, JMESPath, CSV, MessagePack, CBOR, BSON, UBJSON
zed - A novel data lake based on super-structured data
json11