As it happens, splitting input text into words fast is one of the things I most want to do this week! But maybe that's because it's a distraction from benchmarking hash tables.

It's a wonderful problem for optimizing code. Michael Abrash hosted a performance contest for word counting back in... 1991? (If my memory serves.) The article and code can be found here:

There Ain’t No Such Thing as the Fastest Code: Lessons Learned in the Pursuit of the Ultimate Word Counter

Article: https://www.phatcode.net/res/224/files/html/ch16/16-01.html

Code: https://www.phatcode.net/res/224/files/html/ch16/16-05.html

I was able to get a little bit faster than the multithreaded version with a single thread using page-aligned reads and Grand Central Dispatch for asynchronous read operations: https://github.com/healeycodes/counting-words-at-simd-speed/...

This was a nice opportunity to learn about Grand Central Dispatch through AI for me as well. I knew about the page alignment and async read techniques but not on OSX.

Edit: actually you can get even faster with mmap https://github.com/healeycodes/counting-words-at-simd-speed/...

I think you can just xor the whitespace mask with the shifted one.

Also, when counting 0xFF bytes from a boolean etc., sub the mask; 0xFF == -1.

Would be interesting to see how far this is with exaloop's codon compiler.

On semi-related note, it's worth noting that if you're trying to make a Python script run faster and don't have the know-how to re-write your program in C or how to write SIMD (if applicable), you can always try to run the script through pypy, merely replacing python3 with pypy3 in bench.sh, with no other changes, brings the runtime of the first program down from 104s to 9s on my machine:

    Benchmark 1: python3 0_mvp.py bench.txt
      Time (mean ± σ):     104.739 s ±  3.982 s    [User: 104.213 s, System: 0.158 s]
      Range (min … max):   100.303 s … 108.005 s    3 runs
    Benchmark 2: python3 1_c_regex.py bench.txt
      Time (mean ± σ):     14.777 s ±  0.017 s    [User: 14.563 s, System: 0.158 s]
      Range (min … max):   14.759 s … 14.791 s    3 runs
    Benchmark 1: pypy3 0_mvp.py bench.txt
      Time (mean ± σ):      9.381 s ±  0.204 s    [User: 9.110 s, System: 0.234 s]
      Range (min … max):    9.245 s …  9.616 s    3 runs
    Benchmark 2: pypy3 1_c_regex.py bench.txt
      Time (mean ± σ):      4.296 s ±  0.031 s    [User: 4.038 s, System: 0.236 s]
      Range (min … max):    4.262 s …  4.324 s    3 runs

Author here, one of the fastest improvements I've seen is @cloud11665's idea here: https://x.com/cloud11665/status/1955958965046595699

I don't know ARM, but an alternate approach, if it's available, is to store the query constants as bitmasks in SIMD registers; and use the input bytes as indices into those constants, using a shuffle instruction. Two levels, to pull out a bit from a 256-bit mask: part of an input byte is used to index a byte (SIMD shuffle), and another part indices a bit within the byte (bit shifts).

Idea being, this is constant in the size of the query set.

You can avoid hard-coding the whitespace symbols and have a generic byte-set search kernel via `vpshufb` AVX512BW-capable CPUs [1] or via `tbl` instructions on NEON-capable CPUs [2].

[1]: https://github.com/ashvardanian/StringZilla/blob/2f4b1386ca2...

[2]: https://github.com/ashvardanian/StringZilla/blob/2f4b1386ca2...

> Perform a bitwise AND with 0xFB and check if the result equals 0x09. Both 0x0D & 0xFB = 0x09 and 0x09 & 0xFB = 0x09.

This explanation was a bit unsatisfying. This works because 0x09 and 0x0D differ by a single bit, and 0xFB masks that bit (and only that bit) out.

If they differed by more than one bit, the fact that they & the same would be necessary but not sufficient.

I'm wondering what the gain would be if we considered "white space" every char <= 0x20. I know it's changing the rules of the game, but who would want to count the words in a text full of control characters?