are-we-fast-yet VS .NET Runtime

> Sure there's a small overhead to smart pointers

Not so small, and it has the potential to significantly speed down an application when not used wisely. Here are e.g. some measurements where the programmer used C++11 and did everything with smart pointers: https://github.com/smarr/are-we-fast-yet/issues/80#issuecomm.... There was a speed down between factor 2 and 10 compared with the C++98 implementation. Also remember that smart pointers create memory leaks when used with circular references, and there is an additional memory allocation involved with each smart pointer.

> Garbage collection has an overhead too of course

The Boehm GC is surprisingly efficient. See e.g. these measurements: https://github.com/rochus-keller/Oberon/blob/master/testcase.... The same benchmark suite as above is compared with different versions of Mono (using the generational GC) and the C code (using Boehm GC) generated with my Oberon compiler. The latter only is 20% slower than the native C++98 version, and still twice as fast as Mono 5.

See https://github.com/smarr/are-we-fast-yet/blob/master/docs/guidelines.md.

That's a very interesting article, thanks. Interesting to note that Cython is only about twice as fast as Python 3.10 and only about 40% faster than Python 3.11.

The official Python site advertises a speedup of 25% from 3.10 to 3.11; in the article a speedup of 60% was measured. It therefore usually makes sense to measure different algorithms. Unfortunately there is no Python or C++ implementation yet for https://github.com/smarr/are-we-fast-yet.

> Software benchmarks are super subjective.

No, they are not, but they are just a measurement tool, not a source of absolute thruth. When I studied engineering at ETH we learned "Who measures measures rubbish!" ("Wer misst misst Mist!" in German). Every measurement has errors and being aware of these errors and coping with it is part of the engineering profession. The problem with programming language benchmarks is often that the goal is to win by all means; to compare as fairly and objectively as possible instead, there must be a set of suitable rules adhered to by all benchmark implementations. Such a set of rules is e.g. given for the Are-we-fast-yet suite (https://github.com/smarr/are-we-fast-yet).

I am aware of the various published test results where CoreCLR shows fantastic speed-ups compared to Mono, e.g. when calculating MD5 or SHA hash sums.

But my measurements based on the Are-we-fast-yet benchmark suite (see https://github.com/smarr/are-we-fast-yet and https://github.com/rochus-keller/Oberon/tree/master/testcases/Are-we-fast-yet) show a completely different picture. Here the difference between Mono and CoreCLR (both versions 3 and 5) is within +/- 10%, so nothing earth shattering.

Here are my measurement results:

https://github.com/rochus-keller/Oberon/blob/master/testcases/Are-we-fast-yet/Are-we-fast-yet_results_linux.pdf comparing the same benchmark on the same machine run under LuaJIT, Mono, Node.js and Crystal.

https://github.com/rochus-keller/Oberon/blob/master/testcases/Are-we-fast-yet/Are-we-fast-yet_results_windows.pdf comparing Mono, .Net 4 and CoreCLR 3 and 5 on the same machine.

Here are the assemblies of the Are-we-fast-yet benchmark suite used for the measurements, in case you want to reproduce my results: http://software.rochus-keller.ch/Are-we-fast-yet_CLI_2021-08-28.zip.

I was very surprised by the results. Perhaps it has to do with the fact that I measured on x86, or that the benchmark suite used includes somewhat larger (i.e. more representative) applications than just micro benchmarks.

What are your opinions? Do others have similar results?

There is a good reason for this; have a look at e.g. https://github.com/smarr/are-we-fast-yet/blob/master/docs/guidelines.md.

Then apparently the SOM nbody program is taken as the basis of a new Java nbody program.

It's an interesting "time is a circle" problem given that a century only has 100 years and then we loop around again. 2-digit years is convenient for people in many situations but they are very lossy, and horrible for machines.

It reminds me of this breaking change to .Net from last year.[1][2] Maybe AA just needs to update .Net which would pad them out until the 2050's when someone born in the 1950s would be having...exactly the same problem in the article. (It is configurable now so you could just keep pushing it each decade, until it wraps again).

Or they could use 4-digit years.

[1] https://github.com/dotnet/runtime/issues/75148

These can also be passed as arguments to `dotnet publish` if necessary.

Reference:

- https://learn.microsoft.com/en-us/dotnet/core/deploying/nati...

- https://github.com/dotnet/runtime/blob/main/src/coreclr/nati...

- https://github.com/dotnet/runtime/blob/5b4e770daa190ce69f402... (full list of recognized keys for IlcInstructionSet)

Yes, that is true. I'm not sure about JVM implementation details but the reason the comment says "virtual and interface" calls is to outline the difference. Virtual calls in .NET are sufficiently close[0] to virtual calls in C++. Interface calls, however, are coded differently[1].

Also you are correct - virtual calls are not terribly expensive, but they encroach on ever limited* CPU resources like indirect jump and load predictors and, as noted in parent comments, block inlining, which is highly undesirable for small and frequently called methods, particularly when they are in a loop.

* through great effort of our industry to take back whatever performance wins each generation brings with even more abstractions that fail to improve our productivity

[0] https://github.com/dotnet/coreclr/blob/4895a06c/src/vm/amd64...

[1] https://github.com/dotnet/runtime/blob/main/docs/design/core... (mind you, the text was initially written 18 ago, wow)

If you care about portable SIMD and performance, you may want to save yourself trouble and skip to C# instead, it also has an extensive guide to using it: https://github.com/dotnet/runtime/blob/69110bfdcf5590db1d32c...

CoreLib and many new libraries are using it heavily to match performance of manually intensified C++ code.

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https://github.com/dotnet/runtime/issues/59591

Support zstd Content-Encoding:

We might not be that far away already. There is this issue[1] on Github, where Microsoft and the community discuss some significant changes.

There is still a lot of questions unanswered, but initial tests look promising.

Ref: https://github.com/dotnet/runtime/issues/94620

https://github.com/dotnet/dotnet exists for source build that stitches together SDK, Roslyn, runtime and other dependencies. A lot of them can be built and used individually, which is what contributors usually do. For example, you can clone and build https://github.com/dotnet/runtime and use the produced artifacts to execute .NET assemblies or build .NET binaries.

Yeah, it kind of is. There are quite a few of experiments that are conducted to see if they show promise in the prototype form and then are taken further for proper integration if they do.

Unfortunately, object stack allocation was not one of them even though DOTNET_JitObjectStackAllocation configuration knob exists today, enabling it makes zero impact as it almost never kicks in. By the end of the experiment[0], it was concluded that before investing effort in this kind of feature becomes profitable given how a lot of C# code is written, there are many other lower hanging fruits.

To contrast this, in continuation to green threads experiment, a runtime handled tasks experiment[1] which moves async state machine handling from IL emitted by Roslyn to special-cased methods and then handling purely in runtime code has been a massive success and is now being worked on to be integrated in one of the future version of .NET (hopefully 10?)

[0] https://github.com/dotnet/runtime/issues/11192

[1] https://github.com/dotnet/runtimelab/blob/feature/async2-exp...