dsharlet

I can agree with the skeptics that LLM generated code is usually crap. I rarely accept its output without significant edits unless it's truly boilerplate, and I want to avoid the need for that kind of code in the first place.

For me, the killer use case is debugging. I hate wasting time debugging something that should work except for mistakes, and now I do that probably 75% less than I used to because AI does it for me.

I don't know if it makes me that much more productive, but I certainly enjoy my work more not having to do as much tedious debugging, and it feels like I waste a lot less time doing it.

Yes I am, you can reach me at dsharlet@gmail.com

BLIS doesn't appear to support SME: https://github.com/search?q=repo%3Aflame%2Fblis+mopa&type=co...

Maybe you want a comparison anyways, but it won't be competitive. On Apple CPUs, SME is ~8x faster than a single regular CPU core with a good BLAS library.

The problem I've seen is this: in order to get good performance, no matter what language you use, you need to understand the hardware and how to use the instructions you want to use. It's not enough to know that you want to use tensor cores or whatever, you also need to understand the myriad low level requirements they have.

Most people that know this kind of thing don't get much value out of using a high level language to do it, and it's a huge risk because if the language fails to generate something that you want, you're stuck until a compiler team fixes and ships a patch which could take weeks or months. Even extremely fast bug fixes are still extremely slow on the timescales people want to work on.

I've spent a lot of my career trying to make high level languages for performance work well, and I've basically decided that the sweet spot for me is C++ templates: I can get the compiler to generate a lot of good code concisely, and when it fails the escape hatch of just writing some architecture specific intrinsics is right there whenever it is needed.

I wrote a library in C++ (I know, probably a non-starter for most reading this) that I think does most of what you want, as well as some other requests in this thread (generalized to more than just multiply-add): https://github.com/dsharlet/array?tab=readme-ov-file#einstei....

A matrix multiply written with this looks like this:

    enum { i = 2, j = 0, k = 1 };
    auto C = make_ein_sum<float, i, j>(ein<i, k>(A) * ein<k, j>(B));

This library is a lot less automated than most other einsum implementations. You have to explicitly control the loop ordering (in the example above, the `j` loop is innermost because it is loop 0). If you build a good loop order for your shapes, the compiler will probably autovectorize your inner loop, and you'll get pretty good performance. Control over the loop ordering is in general a useful tool, but it's probably a lot lower level than most users want.

I dunno man. My claim was that for specific cases with unique properties, it's not hard to beat BLAS, without getting too exotic with your code. BLAS doesn't have routines for multiplies with non-contiguous data, various patterns of sparsity, mixed precision inputs/outputs, etc. The example I gave is for a specific case close-ish to the case I cared about.

You're changing it to a very different case, presumably one that you cared about, although 4096x4096 is oddly square and a very clean power of 2... I said right at the beginning of this long digression that what is hard about reproducing BLAS is its generality.

BLAS is getting almost exactly 100% of the theoretical peak performance of my machine (CPU frequncy * 2 fmadd/cycle * 8 lanes * 2 ops/lane), it's not slow. I mean, just look at the profiler output...

You're probably now comparing parallel code to single threaded code.

The makefile asks for -O2 with clang. I find that -O3 almost never helps in clang. (In gcc it does.)

Here's what I see:

   $ clang++ --version
   clang version 18.0.0

   $ time make bin/matrix
   mkdir -p bin
   clang++ -I../../include -I../ -o bin/matrix matrix.cpp  -O2 -march=native -ffast-math -fstrict-aliasing -fno-exceptions -DNDEBUG -DBLAS  -std=c++14 -Wall -lstdc++ -lm -lblas
   1.25user 0.29system 0:02.74elapsed 56%CPU (0avgtext+0avgdata 126996maxresident)k
   159608inputs+120outputs (961major+25661minor)pagefaults 0swaps

   $ bin/matrix
   ...
   reduce_tiles_z_order time: 3.86099 ms, 117.323 GFLOP/s
   blas time: 0.533486 ms, 849.103 GFLOP/s

   $ OMP_NUM_THREADS=1 bin/matrix
   ...
   reduce_tiles_z_order time: 3.89488 ms, 116.303 GFLOP/s
   blas time: 3.49714 ms, 129.53 GFLOP/s

Despite being on a current-ish version of clang, I've been getting similar results from clang for years now.

Anyways, I'm not going to debate any further. It works for me :) If you want to keep writing code the way you have, go for it.

I should have mentioned somewhere, I disabled threading for OpenBLAS, so it is comparing one thread to one thread. Parallelism would be easy to add, but I tend to want the thread parallelism outside code like this anyways.

As for the inner loop not being well optimized... the disassembly looks like the same basic thing as OpenBLAS. There's disassembly in the comments of that file to show what code it generates, I'd love to know what you think is lacking! The only difference between the one I linked and this is prefetching and outer loop ordering: https://github.com/dsharlet/array/blob/master/examples/linea...

This gets to 90% of BLAS: https://github.com/dsharlet/array/blob/38f8ce332fc4e26af0832...

The less involved versions still get ~70%.

But this is also quite general. I’m claiming you can beat BLAS if you have some unique knowledge of the problem that you can exploit. For example, some kinds of sparsity can be implemented within the above example code yet still far outperform the more general sparsity supported by MKL and similar.

This is really overstating how hard it is to compete with matrix multiply libraries. The main reason those libraries are so big and have had so much work invested in them is their generality: they're reasonably fast for almost any kind of inputs.

If you have a specific problem with constraints you can exploit (e.g. known fixed dimensions, sparsity patterns, data layouts, type conversions, etc.), it's not hard at all to beat MKL, etc... if you are using a language like C++. If you are using python, you have no chance.

It isn't even necessarily that different from a few nested loops. Clang is pretty damn good at autovectorizing, you just have to be a little careful about how you write the code.

I think for a compiler it makes sense to focus on small matrix multiplies, which are a building block of larger matrix multiplies anyways. Small matrix multiplies emphasize the compiler/code generation quality. Even vanilla python overhead might be insignificant when gluing small-ish matrix multiplies together to do a big multiply.

I tried it out and compared it to C++ at the last release. Here was what I found: https://github.com/dsharlet/mojo_comments

Some of the issues I pointed out there are pretty low hanging fruit for LLVM, so they may have improved a lot in more current releases.

I don't think it's true at all, you can just write C/C++ with SIMD intrinsics, just like you can on ARM or x86, and the instruction set is mostly awesome. OpenCL would just be an extra layer to get in the way. If you want to just run some existing OpenCL code, I guess that would be nice, but I doubt any OpenCL code written for a GPU would actually run well on Hexagon anyways.

The article also complains about VLIW in the same paragraph, but I don't think VLIW makes things harder, it just makes problems more obvious. If you write ARM or x86 code that has dependencies between every instruction, that's going to suck too, you just won't know it until you run it, but VLIW will make it obvious if you just look at the generated code. For the kinds of programs that make sense to run on a processor like Hexagon, VLIW is fine.

The whole Hexagon environment is just so much better than any of the other similar DSPs I'm aware of: you can use open source LLVM to compile code for it (so you aren't stuck with an old version of GCC), and the OS is much closer to standard (e.g. thread synchronization is just pthreads).

I did a bunch of work on Hexagon and I like it a lot. It is my favorite in its class.

The only case I can currently fore see where using LMUL=1 and manually unrolling instead will likely be always beneficial is vrgather operations that don't need to cross between registers in a register group (e.g. byte swapping).

What about algorithms where register pressure is an issue?

I think the problem with LMUL is it assumes that you always want to unroll the innermost dimension (where the vector loads are stride 1). That's usually, the last dimension I try to unroll, if there are any registers left over. If there is any sharing of data across any other dimension in the algorithm, it's better to tile/unroll those first.

Of course, for a simple algorithm, there will be registers left over. But I think more interesting algorithms will struggle on RVV if you must use LMUL > 1 for performance.

If you are looking for something like this in C++, here's my attempt at implementing it: https://github.com/dsharlet/array#einstein-reductions

It doesn't do any automatic optimization of the loops like some of the projects linked in this thread, but, it provides all the tools needed for humans to express the code in a way that a good compiler can turn it into really good code.

Compilers can be pretty good if you help them out a bit. Here's my implementation of Einstein reductions (including summations) in C++, which generate pretty close to ideal code until you start getting into processor architecture specific optimizations: https://github.com/dsharlet/array#einstein-reductions

Funny you mention SPICE to VST compilation... It was on my list for this (my) side project but I never got around to it: http://livespice.org/

edit: And a Tubescreamer is one of the examples!

which introduces some frequency/phase distortion

I agree with everything you said here, except this. Can you explain this? Why does oversampling introduce distortion?

The rest of the post after the question mark you stopped reading at:

Filesystem corruption is frequently silent, and every-time it happens customers don't get on the phone and send the disks to apple so that they can root cause the problem. Its quite possible this bug has happened an untold number of times before it happened to someone who went through the effort to reproduce and isolate it.

In my experience, GPS doesn't work well when the device is in a pack or even a thick jacket. Also, batteries don't work well in the cold. Combine those two things, it's hard to get continuous GPS tracking in cold environments.

That hundreds of millions of users took years to notice should be an indication this whole thing worked pretty well IMO.

People absolutely have been noticing, this has been a very popular and long running issue. It took years for Apple to admit it, not for people to notice.

I think this could have been better handled by throttling according to the actual condition of the battery, rather than controlling it via software updates. The former seems reasonable, the latter is pretty shady. (Maybe this is actually what is happening and the press has confused what is going on.)

TP-link is clearly ignoring many of the instructions from NTP: http://www.pool.ntp.org/vendors.html

Notably, they are ignoring the ones that make it possible for NTP to be aware of the problem in the first place. Right now, TP-link's traffic probably just looks like millions of unrelated devices misconfigured. NTP wants vendors like this to make requests to a particular subdomain so they can identify problematic vendors in the first place.

Of all of the companies you listed, only Comcast needed to obtain the right to dig a trench through much of the private and public property in America prior to doing business. In exchange for that right, they accepted many regulatory burdens (often very explicitly in contracted negotiations with various levels of government), and should be accepting of future regulatory burdens as well.

"The government" in this case is responsive to the 99% of people that do not care one bit about ISP monopolies, but do care greatly about road construction in their neighborhoods. This regulation exists for a reason that isn't just pure mustache twirling corruption.

On one hand, I agree with this sentiment, you should be free to do your own work on your own property.

On the other hand... my landlord in college. He did a lot of work on his house himself, and did it really badly. One example: he installed a cheap indoor light fixture outside where rain would get into it. It also happened to be on the same circuit as our refrigerator. So, sometimes when it rained, the circuit with our refrigerator on it would trip its circuit breaker.

You might be perfectly competent to do your own work on your house. The problem is, everyone that works on their own house thinks they are competent, and many of them are not.

I feel like this is one of those issues that is doomed to oscillate. A few generations will exist with these rules complaining of not being allowed to work on their own houses, and maybe these rules will change. Some injuries/deaths/just plain tiring of janky poorly done house modifications by prior owners will see a return of these rules. Repeat...

This is probably why Broadcom is pulling it's HQ move from Singapore to the US stunt right now. It buys favor with Trump, who appears to operate entirely based on favor and image rather than the rule of law.

This reddit post is the very top of /r/all right now: https://www.reddit.com/r/videos/comments/79i4cj/youtube_user...

And the general sentiment appears to be very far from what you claim. I think it's possible that this issue is going to come to a head this time around. I don't know what the outcome will be. Just saying it's confirmation bias (the likely real explanation?) may not be enough to satisfy people.

Maybe I'm misinterpreting your point, but: doing small team group work with supervision that leads to being effectively able to solve problems sounds like a thing that makes someone actually a good candidate, not just good at interviewing.

There really are good reasons for such a choice. The schools that are so famous for their top-end research are achieving that research success by having those elite professors doing research, and that comes at the expense of teaching. Going to a merely "most selective" school rather than "Ivy Plus" is an decent solution to that problem.

One of the points of going to good research universities is to immerse yourself in exactly that environment: research. Good students probably don't need good teachers to learn. The reason to go to a top research university is to get involved in exactly that research. Especially in computer science and engineering, this is part of the pipeline to the really interesting jobs in industry.

I completely missed this when I was in school, and I regret it.