tolower() in bulk at speed
dotat.at
dotat.at
Every system I'm aware of has 128-bit SIMD implemented: either SSE (x86), NEON (ARM), or AltiVec (POWERPC). As such, 128-bit SIMD is the "reliably portable" SIMD operation.
Of course, for fastest speeds, you need to go to the largest SIMD-register size for your platform: 512-bit for some Intel processors, rumored AMD Zen4 and Centaur chips. 256-bit for most Intel / AMD Zen3 chips. 128-bit for Apple ARMs, 512-bit for Fujitsu A64 ARMs, etc. etc.
> And these are the fastest kinds of instructions :-)
And it should be noted that modern SIMD instructions execute at one-per-clock-tick. So the 64-bit instructions certainly are fast, but the SIMD instructions tie if you're using simple XOR or comparison operations.
--------
This style of code might even be "reliably auto-vectorizable" on GCC and CLANG actually. I wonder if I could get portable auto-vectorizing C from these examples.
news.ycombinator.com
If you load 5 bytes into a 128-bit register (16-bytes) you'll have 5-bytes of data + 11-bytes of garbage.
Perform the calculation over all 16-bytes. Yes, this makes 11-bytes of garbage at the end. Once you're done, just write back the first 5 bytes and you're set.
The 11-bytes of garbage are "free". They didn't cost you anything.
> Getting the data from RAM is the main bottleneck for short strings
Your L1 and L2 cache-lines are 64-byte minimum read / write anyway (actually, some systems were 128-byte minimum IIRC). A 16-byte read is literally smaller than what your cache does on the path into your registers.
EDIT: More importantly, modern CPUs only have 2x or 3x load/store units. Meaning a modern CPU can only perform ~2ish load/stores per clock tick. The SSE (16-byte / 128-bit) read / write to L1 cache will perform the same speed as a 8-byte /64-bit read/write to L1 cache in practice.
I briefly considered playing games with overlong reads, but then asan gave me a slap and I reconsidered the path to wisdom.
You build that out of "load 16 bytes" and "store 16 bytes" operations.
register = [news.YCOMBINATOR]
isValid = [00000FFFFFFFFFFF]; // "F" is really 0xFF, but you get the gist
// isValid was generated because you were focusing on the YCOMBINATOR part
// of the DNS entry: news . YCOMBINATOR . com. YCOMBINATOR there to focus
// upon the current data being considered, while "news." and "com" should
// remain untouched
register = (register | ((register >= 'A' & register <='Z') << 5));
register = register & isValid;
toWrite = writeLocation & (~isValid);
toWrite = toWrite | register;
writeLocation = toWrite;
Ex: 13-size gets rounded up to 16-sized alloc. 25-size gets rounded up to a 32-sized alloc.
This, in combination of "processing of garbage" and "Valid" flags, should cover all the cases.
EDIT: At least, for the "dynamic" strings that are loaded into memory. Someone elsewhere has brought up compiler strings, which seem complicated to me (I'm not sure if we have any alignment guarantees, and therefore are forced to process strings sequentially, one byte at a time if they're from the compiler).
In Highway we also use the blending/bit-select you mention: https://github.com/google/highway/blob/master/hwy/ops/generi... If HWY_MEM_OPS_MIGHT_FAULT, which is true if using ASAN or AVX2 (because AMD reserves the right to fault even if mask=0), we actually use a scalar loop.
I'm curious if anyone has data indicating that a jump table is faster, enough to make a difference?
Anything that touches the branch predictors on these modern, crazy CPUs, is going to be too complicated to really understand from a performance perspective!
I'm pretty sure that jump tables / if statements all touch the branch predictor. Things will be high performance if the branch is consistently predicted correctly, but good luck figuring that out without just running the darn program on a CPU.
A jump table has the potential to be faster by doing SIMD, at the cost of taking more icache.
Memorize the following concept / pattern.
int select(int choice, int A, int B){
return (A & (~choice)) | (B & (choice));
}
This performs a 32-bit way parallel "choice" operation. For example, "choice = 0x00000000" will choose all bits from A. "choice = 0xFFFFFFFF" will choose all bits from B.
SIMD because "choice = 0xFFFF0000" will choose the first 16-bits from B, and the last-16 bits from A.
------
Any data-operation can be "simulated" with enough code operations, and vice versa. Obviously, use code when code is needed, and data when data is needed. But sometimes, you need to perform a "code/data" switcheroo and the above pattern helps.
EDIT: It doesn't always work. The combinational explosion of data may prevent such a technique from working in the general case. But still, its a good first step and "most if statements" are simple enough to work with the above pattern.
while (len-- > 0) { /* load */ }
switch (len) { /* 8 cases */ }
Strings in C are null terminated. You can avoiding loading exactly 5 bytes. For a string of length 4 load 4 bytes, for a string of length 5 load 6 bytes, you’ll read 5 characters of the payload + the terminating '\0'.
If you code C++, the standard library was fortunately designed with C interop in mind, strings form the standard library have c_str() method.
About how to do that efficiently, a good way is a jump table. Not all compilers are smart enough to compile switch into a table, but many of them are. Modern GCC usually does the right thing, take a look: https://godbolt.org/z/sK7jcoooa (untested)
Partial stores are pretty similar. Compare the vector of bytes for b == `\0`, use _mm_movemask_epi8(), then _tzcnt_u32() or _BitScanForward() to find the length, then in the switch use various extract instructions: _mm_cvtsi128_si32, _mm_cvtsi128_si64, _mm_extract_epi16, and/or _mm_extract_epi32.
It's a shame that there aren't guarantees on being able to overread some number of garbage bytes. (for a SIMD-heavy project, I've just went with a custom allocator that guarantees the ability to read past the end of allocations, and storing by either maskstore for 32/64-bit, and blending with what's already there for 8/16-bit)
In such instruction sets not only load and store but also most of the other instructions can operate on a subset of bytes/words/dwords/qwords of the registers, by using bit masks to specify which parts of a vector register are used in an operation.
It is possible to specify whether the unused parts should retain their previous value or they should be set to zero.
Moreover, for load and store, it is possible to load or store some number of bytes or of larger data from non-sequential locations, using the so-called gather and scatter instructions.
Or even two, or four on apple arm, plus load and store which can be done in parallel.
For x86, Intel can do 3x AVX512 instructions per clock tick as long as each instruction is simple enough (add, AND, OR, NOT, XOR, maybe even multiply if you're not counting the latency issue)
https://gcc.godbolt.org/z/bcefvznG3
Yes, there's some amount of autovectorization here. Seems like a mess of a function. Here's something more like tolower8():
https://gcc.godbolt.org/z/h8GTanodd
The generated code definitely looks funny to me. Here is a manual vectorization, which is shorter:
https://gcc.godbolt.org/z/1e4odEsKq
The issue of loading into your vector register... well, there are some dirty tricks for that. The 16-byte slice containing the first byte, and the 16-byte slice containing the last byte, can both be loaded into registers and then shifted around in order to construct the desired value. Note the careful wording here... these slices might be the same slice. Or you can iterate over the 16-byte slices containing the array, and shift as you go, if you're storing into a different location. Or you can use various masked load/store operations on various architectures.
Good discussion. I think the only method you haven't talked about is the simple "unaligned load" instructions (which might be the simplest, and most portable way, to do this). I know that ARM and x86 both can do unaligned loads no problem, but at a possible performance penalty.
(Edit: The idea is that with an aligned load, you only load 16-byte blocks that contain valid data. This can’t page fault, but you can load data you didn’t want, which is ok. Writing is trickier because the write may have to be atomic and can’t spill.)
This can happen if you're writing code for an embedded system, or even if you're calling memcpy with a too-smart-for-it's-own-good libc (such as Newlib, that ships with the aarch64-none-elf toolchain ARM provides...) implementation without first setting the MMU up.
It has to deal with an arbitrary n, so it's going to have some messy bits to deal with the "fringe": https://www.sigarch.org/simd-instructions-considered-harmful...
(I think, it’s time, to say that “‘X considered harmful’ considered harmful”.)
There's a short SIMD loop, which is simple, and fairly easy to understand. At -O2, you get a short scalar loop, which is simple, and fairly easy to understand.
If you were writing this function by hand, you might combine the SIMD loop with the scalar loop. That's not what happens, though. Instead, you get two versions of the SIMD loop which handle different alignments (unnecessary) and then, what appears to be an unrolled version of the scalar loop.
What’s going on here is that there are a bunch of transformations enabled at -O3 that come along with autovectorization. You don’t just get autovectorization by itself, you also get transformation passes that are designed to put your code into a state where vectorization is easier. You also sometimes get a number of transformations which are (empirically) dubious, in the sense that the resulting code is both larger and slower.
The unrolling of the scalar tail is quite unnecessary though (gcc does so even with -fno-unroll-loops). clang doesn't[0], though it does unroll the main loop (though that can be disabled with -fno-unroll-loops).
It is still messy, though. And "it has SIMD" is not really a sufficient explanation for the messiness. I think of -O3 as a kind of "throw everything at the wall and see what sticks". You're pressing a button that makes your code big, messy, and fast (although not always big, not always messy, and not always fast).
You can spend as much time as you like explaining the reasoning behind these code transformations, but in the end, there are very good reasons why people don't use -O3 as the default optimization level.
It is actually feasible to write vector-style code using SIMD instructions. Yes, the SIMD ISA is more complicated because of the various accumulated extensions, but this is what we currently have. And a bit larger code size (for one final loop iteration) doesn't seem to be a big deal.
https://github.com/google/wuffs/blob/main/doc/note/iterate-l...
WUFFS wants this because it demands all the checking at compile time (WUFFS code with a potential buffer overflow just won't compile), so if you need bounds checks you'll be writing them out by hand and the iterate loop often allows you to express a correct solution with no actual checks.
These still come in handy on embedded systems where there isn't a vector unit
BIND still supports critical internet infrastructure on less capable hardware.
Simple SIMD instructions, like integer addition, comparison, XOR, have a throughput of 2 per cycle (many ARM cores), 3 per cycle (most Intel CPUs) or even 4 per cycle (AMD Zen 3).
Similar simple 64-bit instructions have a higher throughput in instructions per cycle, from 3 per cycle up to 5 per cycle (Alder Lake), but when considering the register size the total throughput of the SIMD instructions is always higher.
It can easily make ~20 GB/sec with 48 cores: https://pastila.nl/?0f15f812/179361234a6b79d27e18c8db7050bd0...
Although it is only using SSE2. And the UTF-8 variant is using a shortcut with SSE2, another shortcut for a subset of code points and a slow path for a generic case.
Requires -fno-unroll-loops as otherwise clang gets overly unroll-y; the code is fast enough. Tail is dealt with by blending the originally read value with the new one.
You should get much more throughput if you can interleave them with other instructions...
I wonder if that's what the benchmarks did?
Bulk throughput isn’t really my aim, it’s just a convenient way to get numbers big enough to be easily measurable :-)
But, given that the task at hand usually deals with very short inputs, there's not much to interleave with anyway.
Anything else anyone cares about, that could use a speedup... ?
I have no idea about this (which is PERFECT!) so I've started looking:
> In linear algebra, the transpose of a matrix is an operator which flips a matrix over its diagonal; that is, it switches the row and column indices of the matrix A by producing another matrix, often denoted by AT (among other notations).
I ask, because I need to know if it matters how the solution works, or if all that matters is that the solution works as it should.
Thank you!
Edit: Do you have a compiled benchmark I can run, for comparison with other solutions?
There are two answers to this, one less interesting, and one more interesting.
The less interesting answer is that it's a primitive in APL, and I have a vested interest in making APL fast. Then, it needs to be a general transpose because the specific case of a 2-d transpose is already well-studied and has high-quality solutions already.
As a language feature, the transpose can be thought of as a calculus on indices. This suggests a fairly straightforward implementation strategy where no actual data is moved around; instead, a 'strided representation' is used, storing additional information about the array layout, and the indexing procedure is made aware of this. This seems to be effectively free. So why am I asking you to move data around?
This leads into the second interpretation of the transpose, which is as a permutation. Interestingly, this is almost opposite the index-calculus interpretation, because it primarily acts to change the way the data are represented; it doesn't actually need to change how the data are interpreted at all. And this is useful because, when taking into account access patterns, laying the same data out differently can significantly improve locality and therefore performance. If you're familiar with column/row stores, or 'soa'/'aos', these are applications of the 2-d transpose. The general transpose, then, performs the obvious analogue function for higher-dimensional data and is interesting for the same reasons.
(The duplicating-axes part is just a novelty, then.)
As a starting point, I suggest looking at libfft; as I recall, it has some good 2-d transpose implementations, and links to relevant papers. (Fft is, not-so-coincidentally, an application that can benefit from transposing.) Also interesting may be this paper, and any relevant links that result therefrom: https://www.researchgate.net/publication/273912700_In-Place_...
> I need to know if it matters how the solution works, or if all that matters is that the solution works as it should
The interface is: I give you a buffer, an element size (probably 1/2/4/8 bytes), a shape projecting a multidimensional interpretation onto that buffer, and a permutation of that shape; you move around the elements of the buffer according to the permutation. How you implement that is up to you (though I will also say that I am also more curious about the general approach you come up with than with the specifics of the implementation).
> Do you have a compiled benchmark I can run, for comparison with other solutions?
The only place I know of to find implementations is inside of apls; the fast apl implementations are dyalog apl[0] and j[1]. I don't know how optimised their transposes are, though; they might or might not be a good baseline. Here's a simple example to get you started benchmarking inside j:
a=. 27 1000 40 77?@$0 NB.declare a to be an array of shape 27 1000 40 77
NB. contents are randomised; this is a memory-heavy algorithm, and apparently writing lots of zeroes can have weird cache effects
NB. elements are double-precision floats; so, 8 bytes
timex'2 1 0 3|:a' NB. see how long it takes to apply the permutatio 2 1 0 3 to the shape of a
0.34551
10 timex'2 1 0 3|:a' NB. do 10 runs, and take their average
0.301104
You give me a problem, tell me the desired solution and I figure it out on my own.
What I can provide you with is an optimized, compiled block of assembly code, with source, which solves your problem fast.
What I require is a problem (you've stated it) and how the solution is supposed to look like/how the desired output is supposed to be presented/etc.
I feel like you didn't actually do that.
I guess the software you've linked is supposed to help me; I'll have a look at that. Usually, though, the problem itself is enough, though. Looking at other peoples work messes with creativity.
Anyhow, can you execute a block of compiled assembly code? Then all I need is access to the data, how it's laid out and what it is you actually want the code to do. In return you'll get a wacky, working solution you can just plug in.
If you can't call compiled binary directly, there's still a way around that using shared memory and having my code run in a separate process.
Anyway, I've thought about this. You definitely want the data to be moved? You don't want just more efficient access to it? That'd been trivial to do. If you don't want that, because of cache reasons, then there's a trivial way of not destroying the cache, as long as you know how to access registers directly. Or I can still drop it in-place, I guess.
I'm sure we'll get there. I guess next time I need to lay out what's actually "in store".
> The interface is: I give you a buffer, an element size (probably 1/2/4/8 bytes), a shape projecting a multidimensional interpretation onto that buffer, and a permutation of that shape; you move around the elements of the buffer according to the permutation
but if not, please tell me!
The remainder is just providing context for the problem, but you don't have to look at it if you don't care to.
> there's a trivial way of not destroying the cache, as long as you know how to access registers directly
What do you mean by this? Nt accesses or similar? Those can be helpful in some specific cases, but are not really a general solution. Esp. if there is spatiotemporal correlation between accesses (as is often the case), and you want to be able to take advantage of the caches.
I wasn't. I was too intimidated, because i'm not a "professional" working in the industry, thus I have little to no knowledge about what others are doing and often terms that others are using. Like how I had no idea what "general transpose" means ... but now I know.
> The interface is: I give you a buffer, an element size (probably 1/2/4/8 bytes), a shape projecting a multidimensional interpretation onto that buffer, and a permutation of that shape; you move around the elements of the buffer according to the permutation
I can work with that! Do you have an "in" and "out" example as a reference,
or should I just make my own?
What CPU are we talking about?
How does the shape look like?
Do you have example data I can start working with?
I've never worked with other people!
Thanks!
PS: Does it really have to be in-place replacement?
> I had no idea what "general transpose" means ... but now I know
FWIW pretty much no one else in industry knows what a transpose it, let alone a general one. It's rather obscure :)
> What CPU are we talking about?
amd64 with avx2 is probably the most important target.
> How does the shape look like?
> example
The shape is a list of natural numbers. Its length is the number of dimensions in the array, and each element is the length of the corresponding axis. For instance, suppose the contents of the array are the numbers 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23. Then, if the shape is 2 3 4, that is a 2x3x4 brick. The length of the array is 24 (being the product 2*3*4), and the structure may be elucidated by the following tabular display:
0 1 2 3
4 5 6 7
8 9 10 11
12 13 14 15
16 17 18 19
20 21 22 23
0 12
1 13
2 14
3 15
4 16
5 17
6 18
7 19
8 20
9 21
10 22
11 23
Well, that's the trick, isn't it? :P
Wait ... you want to be able to pick the permutation? I thought it's about a fixed transposition, but you want to choose? lol
What a complicated mess! 234 ... two blocks of data, containing three lines of data, containing four entries of data.
I'm not sure the example is sufficient for me to understand. I hope I can figure this out for other cases than 1 at the start, which seems to be a bad example for understanding this. if 1 was 2, do I instead, then, pick every second number?
I'm trying to wrap my head around this, starting with wikipedia.
Why don't you use a better data structure? Seems rather wasteful, CPU-wise, not coming up with some more generic structure that makes it easier to permute?
But ... I guess that's not allowed, otherwise you'd not need in-place replacement.
On the other hand, as long as I store everything in-place it shouldn't matter, as long as the result is correct.
But are you sure you need in-place replacement as long as you can get the correct, permutated result? Do you request the same results more than once?
Yes, I keep asking again looking for a way to avoid it. I have to make sure I cover everything and know the desired outcome exactly. Like, if you just care about the result itself, then that's different to having to store it in memory.
Hm. Given that there's some sort of memory limitation ... How much space do I have, in bytes/kbytes, for code? Can I allocate my own memory?
Questions, questions, questions. Details, details, details.
https://mlochbaum.github.io/BQN/implementation/primitive/tra...
(EDIT: missed a 0 in the first version and got the wrong timing for Dyalog)
For the particular case you give, the axis permutation swaps two axes, so it's invertible and the same left argument applies in both J and APL (may require adjustment for ⎕IO of course). I measure Dyalog the same as J, 0.3s. This array has a large fixed axis at the end, meaning that chunks of 77 floats (over half a kB) stay together, and the best way is just to move each of these with memcpy. Seems both languages miss this optimization? 0.3s is only 2.2GB/s. The array's definitely too large to fit in even L3 cache but this seems too slow for an in-memory copy.
While 512 bytes seems like a lot, the core loop is likely 4x unrolled avx; that is 128 bytes per iteration, so only 4 iterations. And it probably tries to align the dst, so annoying dispatch overhead (which you could avoid by working in batches of k at a time, eg k=4 for double floats and avx in the general case).
But 5GB/s for ⊖ isn't great either. In an application that uses these huge arrays and needs the best performance (most don't!), it needs to be split up, ideally into sections that fit in L1, so that multiple array operations can be applied to those chunks and stay CPU-bound (at least for transpose, maybe not for arithmetic). That's why I wouldn't be too interested in optimizing this case.
⎕IO←0 ⋄ ar←,[0 1 2]a←?27 1000 40 77⍴0
ci←(⊢⍴∘⍳×/)3↑⍴a ⍝ cell indices
i←⍉ci ⋄ cmpx '2 1 0 3⍉a' 'ar[i;]'
2 1 0 3⍉a → 2.8E¯1 | 0% ⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕
ar[i;] → 2.8E¯1 | 0% ⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕
i←5⊖ci ⋄ cmpx '5⊖a' 'ar[i;]'
5⊖a → 1.2E¯1 | 0% ⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕
ar[i;] → 1.7E¯1 | +46% ⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕> cache associativity
This one is annoying. Nt accesses might help, though probably not for the in-place case. You mention padding; the paper I linked uses padding as well, to shorten cycle length for inplace+parallel. Although, I would guess that dimensions which are nice to the cache will be not-nice to the blocking, and vice versa
> Of course larger loops are possible as well. But most of the time it's really the base case that's important. The base case also handles two axes most of the time, but can incorporate all the 2D optimizations like blocking.
Unless I am misunderstanding--reversing the axes of an array of shape 1e6 1e3 2 2 will have an inner loop dealing with the two innermost axes, and some extra dispatch outside of that. Which is not ideal, obviously--the inner loop only deals with 4 elements.
The example given is insufficient. I've tried figuring out how this whole thing works, but I didn't find any actually good explanation that's not written for mathematicians.
I've read through the other comments and it even looks like people are approaching this problem mathematically, which makes no sense to me.
There's someone mentioning that his code apparently uses memcopy and I'm wondering if he actually really knows what he's doing, because that's just massive overheard for elements which - as I believe - fit into registers.
Compilers don't write the best code out there, period. After several generations of this attitude, how would people even know, if all they ever do is having blind faith that the compiler will do their job?
Anyhow, I digress into ranting ...
I have several solutions in mind, but apparently I require actual data and at least a few examples that aren't so simple that they don't actually reflect the desired outcome.
I'm sorry about that. It's just how I work. I need something to work with and the scope of this is way beyond reasonability, because all the learning involved does not appear to be at all required for solving the problem.
So, instead of doing it myself (I'd still love to), I'll just tell you the solution I came up with.
To me, this problem seems solvable just by writing code dynamically. Based on how you want the result to be laid out, you just drop blocks of assembly code meant to perform the tasks required to solve the problem.
Tasks, like, "read the element from memory" and "write it somewhere else".
There's room for exploration, by trying different methods of reading and writing the elements. There's more ways than mov to do the job.
How all of this would work:
You know your memory access patterns, both for reading and for writing.
You can sort the access patterns based on linear locality. If it boils down to one "cacheline per read" and you're not allowed to change the structure of the data to make things easier on the cache, then that's just how it is and there's no way around it.
With your sorted access patterns, you start writing the blocks of adjusted (memory addresses) compiled code into your executable memory.
Given that there's different ways of doing the reads/writes, you could do what I'd do and have several different blocks of code to experiment with. Like, one can abuse push/pop for memory transfers, eight byte per.
If you want to be really fancy you'd make a benchmark shuffling around the access patterns (the blocks of code and its respective variations) until you find the quickest, assuming there's any practical value for you in doing so.
I know I would, because this problem can be brute-forced.
Now you execute your created block of code, which then rewrites it all in place, hopefully making proper use of pipelining.
And if you want to go really, really fancy ... you do it multithreaded.
I can see that working ... and I can see room for tinkering. Further exploration follows after a working prototype.
Well ... so far that's all I have. From my perspective this problem is solvable, because it's just memory accesses and, including exploration of possible optimizations, clever use of registers.
Looking at it from a mathematical perspective and trusting the compiler to figure out how to do it quickly doesn't at all appear to me like it's going to cut it.
Thank you for coming to my TED talk.
To work around this, the international domain name standard defines an encoding called Punycode which maps Unicode to the limited character set DNS supports. The server is unaware of this, and so this optimised tolower() implementation works without any Unicode considerations.
And within unicode, doing it in a "dumb ascii" way probably needs some normalization of diacritics. Eg. 'é' should be U+0065 U+0301 ("e\xcc\x81"), not U+00E9 ("\xc3\x89").
Not sure how punycode handles this, I did once look deeply into it but that was years ago.
[1] https://datatracker.ietf.org/doc/html/rfc7564
[2] https://www.icann.org/resources/pages/root-zone-lgr-2015-06-...
This is the classical label form used, albeit with some additional
restrictions, in hostnames [RFC0952]. Its syntax is identical to
that described as the "preferred name syntax" in Section 3.5 of RFC
1034 [RFC1034] as modified by RFC 1123 [RFC1123]. Briefly, it is a
string consisting of ASCII letters, digits, and the hyphen with the
further restriction that the hyphen cannot appear at the beginning or
end of the string. Like all DNS labels, its total length must not
exceed 63 octets.
[1] https://datatracker.ietf.org/doc/html/rfc5890#section-2.2
Also, use SIMD - I think even AVX can do byte level paralellism for 256 and 512 bits at a time?
Although I guess since it's often a dynamically linked library, said optimizations would have to happen at load time, and the dynamic loader doesn't do such things... :-(
Such functions would be called only from within vectorised loops (or other SIMD versions of functions).
Most systems default to using Unicode these days, for which the problem is much more complex even when the language is set to English.