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C vs. C#: racing VLC's Gaussian blurmartinfinkel.com
134pts/52 comments/5d/─
by martz / hn> / « feed
▲▼ maxlin03:53 hn>

Very interesting readup! I have some C# projects I've had in mind to try to compile to AOT. Quite different for complex programs of course, but the perf being very close to C is a great motivator.

▲▼ mihular05:57 hn>

Why would you want them in AOT, though? It's great for startup time and native interoperability, but it might be slower in general due to machine specific optimizations.

▲▼ mcraiha06:28 hn>

Because of Docker. You want the images to be as small as possible.

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▲▼ Brian_K_White05:00 hn>

Most industries consider 8% a mountain. More than their entire profit margin.

▲▼ Dylan1680705:37 hn>

And 8% milk would taste bad and ruin most recipes.

I don't know why we're comparing completely different categories of number though. In a business context getting a project done a few percent faster, or more accurately one step done a few percent faster, barely affects revenue at all. And saving CPU time matters even less.

▲▼ teo_zero05:59 hn>

Nice article!

What surprises me is that the original VLC filter does not use SIMD. Sometimes I wonder why I spent my money on a "modern" CPU with a lot of new features that stay completely ignored by most software I run every day.

▲▼ fenwick6706:21 hn>

It surprises me that it runs on the CPU at all, honestly.

▲▼ ygra06:23 hn>

Probably a reflection of the age of that code along with the usefulness of the feature. If no one actually needs the video blurry, then it doesn't matter how efficient the filter is.

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▲▼ ack_complete09:08 hn>

It's also a basic implementation, doing a divide per sample to renormalize and not taking advantage of the symmetry of the kernel.

Even on a GPU, you wouldn't use an algorithm this simple in production. For small radii, leveraging bilinear filtering halves the number of samples. For larger radii, hierarchical pyramid approximations are popular.

▲▼ martz06:48 hn>

The blur is an outlier: a 2007 filter almost nobody uses, so it never got optimized.

The hot paths in VLC (decoding via dav1d/FFmpeg, chroma conversion, surface copies, deinterlacing) are hand-tuned SIMD with runtime CPU dispatch, and most playback runs on the GPU anyway.

▲▼ rerdavies18:00 hn>

Are large gaussian blurs not used for modern glass effects? Worth pointing out that doing so really requires use of FFT convolutions -- an optimization that has not been implemented in the benchmarks.

Not really the question that this study is addressing though.

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▲▼ byhemechi03:16 hn>

Personally, I’m surprised it doesn’t get auto-vectorised

▲▼ HackerThemAll08:53 hn>

It's possible it's a deliberate project decision to support very old CPUs. But it could be multi-targetting different CPUs and using SIMD whenever available.

▲▼ brookman64k06:42 hn>

I would have expected the filters to run as a shader on the GPU which would make them much more efficient. I remember implementing two-pass gaussian blur in GLSL almost 20 years ago.

▲▼ bschwindHN06:51 hn>

Yep if you want it done quickly, the GPU is the way to go. Especially once you get to 4K resolution and beyond.

See here:

https://aras-p.info/blog/2026/10/01/Fast-blur-with-animated-...

▲▼ anderskaseorg06:47 hn>

There are much faster algorithms available for Gaussian blur though, running in O(n) rather than O(nσ) for an n pixel image and radius σ. See https://www.ipol.im/pub/art/2013/87/article.pdf for a survey.

▲▼ schobi07:08 hn>

If we look at the example of the FFT blur filter, sure O(n) looks easier and independent of the blur size, but you have an overhead in needing FFT and iFFT. It can be faster for some sigmas, but not for all. So you need different variants depending on sigma and a smooth transition between them.

Then you can start approximating - for large sigmas just using every 10th pixel would work. Looks like MS Teams background blur is doing this, you get a blurry background with aliasing - yay!

Once you add SIMD variants and look at CPU caching behaviour, you build versions for different CPUs. Instead of filtering the whole image for one dimension you might finish blocks locally.

You've now got a dozen variants that need testing and maintenance on different architectures.

I'm reminded of my colleague preaching requirements engineering - what was the goal gain?

▲▼ anderskaseorg08:19 hn>

FFT is O(n log n), not O(n). The better O(n) algorithms are the likes of Deriche, Vliet–Young–Verbeek, or Alvarez–Mazorra.

▲▼ hn_submit07:32 hn>

The C# version uses unsafe raw pointers which is practically the same as writing it in C. No surprise then that the outcomes are similar.

▲▼ pjmlp09:12 hn>

Yeah, the point being that you can write a much safer application, and in a very small section reach out to unsafe code, instead of having 100% unsafe code application, or having to do Python style, "Python libraries" that are actually C libraries.

▲▼ Rapzid12:02 hn>

You can also do Memory<T> and Span<T> over unmanaged memory to make things.. Safer.

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▲▼ theanonymousone08:18 hn>
>Two small edits close most of it: compute the loop bounds into locals before the loop,

I always assumed any decent runtime/compiler takes care of such optimisations, and usually leave such things to JDK in my Java code. Is that really not the case?

▲▼ RandomOnyx08:37 hn>

Decent ones do. .NET's JIT has historically (before ryujit especially and tbh I haven't looked at .NET much in the past decade) not been decent, general statements about decent compilers never applied to it. It has been playing catch-up lately but apparently it still has a ways to go.

▲▼ zerr09:02 hn>

It never was a priority I believe. As opposed to C, the main target audience of C#/.NET has always been LOB CRUD app shops.

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▲▼ pjmlp09:07 hn>

They have been improving, this is one area however where those C# vs Java flamewars kind of miss out.

While C# the language has much more goodies than Java the language, Java the platform has had much better options than the CLR, because it has been mostly Microsoft and Mono (nowadays almost gone after the acquisition), whereas the Java ecosystem has a plethora of implementations, each one wiht their JIT and GC implementations, even AOT caches since almost three decades now.

Thankfully the outcome of Project Midori, and the whole Secure Future Initiative , has had the side effect of Microsoft becoming more serious about .NET performance, instead of always outsourcing performance workloads to C++ and COM.

▲▼ torginus12:56 hn>

That is absolutely not true. Not historically, and not now. .NET's JIT has always been very good, bar a few qualifying statements.

It used to be based on MSVC, with the most expensive optimizations trimmed, things like loop hoisting, devirtualization, generally ones with long runtimes, or ones that required whole program visibility.

But otherwise it was competent, if you wrote code aware of these limitations, it could get very close to even C++, and even on naive code, it was always very competitive (with strange wildcards, that if you used raw pointers, which are an obscure C# feature, you could get code to be even faster, due to the compiler's heritage).

It also helped, that due to MS's nature; C# was meant to be used with MS frameworks, written by people who were aware of the compiler's quirks, which you could learn if you looked at these codebases.

Sure if yo measure against LLVM-compiled static languages, it falls short in certain scenarios, but it's been soldily in the pack of other GC languages like Java or Go, and always has been.

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▲▼ kvemkon10:51 hn>

In C++ we have such troubles either. If something looking as constant cannot be 100% proved to be a constant, it cannot be optimized.

>Incrementing vectors (26.08.2019)

https://news.ycombinator.com/item?id=20800076

▲▼ Rapzid13:54 hn>

Its possible the JIT would have optimized this but that optimization isn't making its way into the native AOT build.

Also possible if it were more idiomatic C# it would have optimized it better. RyuJIT does in fact do BCE and other loop optimizations. It's improving a lot every year too.

The optimization paths from C# though Roslyn and RyuJIT probably don't start with C# written like it were C. Maybe iterating over Spans and slices would have optimized to fewer instructions than raw dogging nested for loops.

▲▼ ozgrakkurt15:44 hn>

You would be astounded how more efficient your program can be if you are doing decent amount of computation or IO.

You can try to understand some of it by doing some measurements and doing some napkin math using the hardware limits like internet bandwidth or IO read/write etc.

▲▼ tinykerneldev11:56 hn>

Interesting. Even more interesting would have been seeing if/how C# SIMD intrinsics would have improved performance.

▲▼ moron4hire13:26 hn>

They did do that.

▲▼ sebazzz14:40 hn>

This is all and nice, but I can debug a native C plugin. Is this possible for an AOT compiled C# plugin? Or can perhaps the C# plugin be also run with the normale JITting runtime being loaded?

▲▼ platinum9515:22 hn>

Yes, the AOT compiler can emit debug symbols which point to the C# source files, so depending on your debugger you can get it to work. I previously had to ship a C#/dotnet library in a native C iOS/Android app. It was a PITA but it worked in the end, and I was even able to debug the C# code in XCode (albeit in a limited fashion).

▲▼ esafak15:13 hn>

Lens blur is a lot more fun, since each lens is different, and there is an art to it.