upb VS compiler-explorer

> There are undeniably power users for whom every last bit of performance translates to very large sums of money, and I don’t claim to know how to satisfy them otherwise.

That is the key, right there.

In 1970, C may have been considered a general-purpose programming langauge. Today, given the landscape of languages currently available, C and C++ have a much more niche role. They are appropriate for the "power users" described above, who need every last bit of performance, at the cost of more development effort.

When I'm working in C, I'm frequently watching the assembly language output closely, making sure that I'm getting the optimizations I expect. I frequently find missed optimization bugs in compilers. In these scenarios, undefined behavior is a tool that can actually help achieve my goal. The question I'm always asking myself is: what do I have to write in C to get the assembly language output I expect? Here is an example of such a journey: https://blog.reverberate.org/2021/04/21/musttail-efficient-i...

I created the https://github.com/protocolbuffers/upb project a long time ago. It's written in C, and over the years I have gotten it to a state where the speed and code size are pretty compelling. Both speed and code size are very important to the use cases where it is being used. It's a relatively small code base also. I think focused, performance-oriented kernels are the area where C makes the most sense.

More and more languages are being built on top of the "upb" C library for protobuf (https://github.com/protocolbuffers/upb) which is designed around arenas to avoid this very problem.

Currently Ruby, PHP, and Python are backed by upb, but this list may expand in the future.

Lua uses "chained scatter" (linked list, but links point to other entries in the same table, to maintain locality): https://github.com/lua/lua/blob/master/ltable.c

This is a good visual depiction of chained scatter: https://book.huihoo.com/data-structures-and-algorithms-with-...

Inspired by Lua, I did the same for upb (https://github.com/protocolbuffers/upb). I recently benchmarked upb's table vs SwissTable for a string-keyed table and found I was beating it in both insert and lookup (in insert upb is beating SwissTable by 2x).

> But yes, the serialized dict-of-arrays-of-dicts type stuff can be approached in a few ways, none of which are particularly beautiful.

For what it's worth, this sounds somewhat similar to protobuf (which also supports dicts, arrays, etc).

After spending many years trying to figure out the smallest, fastest, and simplest way to implement protobuf in https://github.com/protocolbuffers/upb, the single best improvement I found was to make the entire memory management model arena-based.

When you parse an incoming request, all the little objects (messages, arrays, maps, etc) are allocated on the arena. When you are done with it, you just free the arena.

In my experience this results in code that is both simpler and faster than trying to memory-manage all of the sub-objects independently. It also integrates nicely with existing memory-management schemes: I've been able to adapt the arena model to both Ruby (tracing GC) and PHP (refcounting) runtimes. You just have to make sure that the arena itself outlives any reference to any of the objects within.

> Google's implementations, at least C++ and Java, are a bunch of bloated crap (or maybe they're very good, but for a use case that I haven't yet encountered).

As someone who has been working on protobuf-related things for >10 years, including creating a size-focused implementation (https://github.com/protocolbuffers/upb), and has been working on the protobuf team for >5 years, I have a few thoughts on this.

I think it is true that protobuf C++ could be a lot more lean than it currently is. That's why I created upb (above) to begin with. But there's also a bit more to this story.

The protobuf core runtime is split into two parts, "lite" and "full". Basically the full runtime contains reflection support, while the lite runtime omits it. The full runtime is much larger than the lite runtime. If you don't need runtime reflection for your protos, it's better to use "lite" by using "option optimize_for = LITE_RUNTIME" in your .proto file (https://developers.google.com/protocol-buffers/docs/proto#op...). That will cut out a huge amount of overhead in your binary. On the downside, you won't get functionality that requires reflection, including text format, JSON, or DebugString().

In addition to this, even the lite runtime can get "lighter" if you compile your binary to statically link the runtime and strip unused symbols with -ffunction-sections/-fdata-sections and gc-sections in the linker. Some parts of the lite runtime are only used in unusual situations, like ExtensionSet which is only used if your protos use proto2 extensions (https://developers.google.com/protocol-buffers/docs/proto#ex...). If you avoid this stuff, the lite runtime is quite light.

However, there is also the issue of the generated code size. The size of the generated code is generally quite large, even for lite. You are getting a generated parser, serializer, CopyFrom(), MergeFrom(), etc for every message you define. If your schema is of any size, this quickly adds up and can dwarf the size of the actual runtime. For this reason, C++ also supports "option optimize_for = CODE_SIZE" which does everything reflectively instead of generating code. This means you pay the fixed size hit from the full runtime, but the generated code size is much smaller. On the downside, "optimize_for = CODE_SIZE" has a severe speed penalty.

I have long had the goal of making https://github.com/protocolbuffers/upb competitive with protobuf C++ in speed while achieving much smaller code size. With the benefit of 10 years of hindsight and many wrong turns, upb is meeting and even surpassing these goals. It is an order of magnitude smaller, both in the core runtime and the generated code, and after some recent experiments it is beginning to significantly surpass it in speed also (I want to publish these results soon, but the code is on this branch: https://github.com/protocolbuffers/upb/pull/310).

upb has downsides that prevent it from being fully "user ready" yet: the API is still not 100% stable, there is no C++ API for the generated code yet (and C APIs for protobuf are relatively verbose and painful), it has a bunch of legacy APIs sitting around that I am just on the verge of being able to finally delete, and it doesn't support proto2 extensions yet. On the upside, it is 100% conformant on every other protobuf feature, it has full binary and JSON support, it supports reflection if you want it but also lets you omit it for code size savings.

I hope 2021 is a year when I'll be able to publish more about these results, and when upb will be a more viable choice for users who want a smaller protobuf implementation.

At least on android arm64, looks like a `dmb ishst` is emitted after the constructor, which allows future loads to not need an explicit barrier. Removing `final` from the field causes that barrier to not be emitted.

https://godbolt.org/#g:!((g:!((g:!((h:codeEditor,i:(filename...

You said You won't get "extreme performance" from C++ because it is buried under the weight of decades of compatibility hacks.

Now your whole comment is about vector behavior. You haven't talked about what 'decades of compatibility hacks' are holding back performance. Whatever behavior you want from a vector is not a language limitation.

You could write your own vector and be done with it, although I'm still not sure what you mean, since once you reserve capacity a vector still doubles capacity when you overrun it. The reason this is never a performance obstacle is that if you're going to use more memory anyway, you reserve more up front. This is what any normal programmer does and they move on.

Show what you mean here:

https://godbolt.org/

I've never used ISPC. It's somewhat interesting although since it's Intel focused of course it's not actually portable.

I guess now the goal posts are shifting. First it was that "C++ as a language has performance limitations" now it's "rust has a vector that has a function I want and also I want SIMD stuff that doesn't exist. It does exist? not like that!"

Try to stay on track. You said there were "decades of compatibility hacks" holding back C++ performance then you went down a rabbit hole that has nothing to do with supporting that.

C++ Insights is available online at https://cppinsights.io/

It is also available at a touch of a button within the most excellent https://godbolt.org/

along side the button that takes your code sample to https://quick-bench.com/

Those sites and https://cppreference.com/ are what I'm using constantly while coding.

I recently discovered https://whitebox.systems/ It's a local app with a $69 one-time charge. And, it only really works with "C With Classes" style functions. But, it looks promising as another productivity boost.

[P&H RISC] https://www.google.com/books/edition/_/e8DvDwAAQBAJ

Compiler Explorer by Matt Godbolt [Godbolt] can help better understand what code a compiler generates under different circumstances.

[Godbolt] https://godbolt.org

The official CPU architecture manuals from CPU vendors are surprisingly readable and information-rich. I only read the fragments that I need or that I am interested in and move on. Here is the Intel’s one [Intel]. I use the Combined Volume Set, which is a huge PDF comprising all the ten volumes. It is easier to search in when it’s all in one file. I can open several copies on different pages to make navigation easier.

Intel also has a whole optimization reference manual [Intel] (scroll down, it’s all on the same page). The manual helps understand what exactly the CPU is doing.

[Intel] https://www.intel.com/content/www/us/en/developer/articles/t...

Personally, I believe in automated benchmarks that measure end-to-end what is actually important and notify you when a change impacts performance for the worse.

Let's compile it with https://godbolt.org/, turn on some optimisations and inspect the IR (-O2 -emit-llvm). Copying out the part that corresponds to the while loop:

  4:

resources were extra useful when building deeper intuitions about GPU performance for ML models at work and in graduate school.

- CMU's "Deep Learning Systems" Course is hosted online and has YouTube lectures online. While not generally relevant to software performance, it is especially useful for engineers interested in building strong fundamentals that will serve them well when taking ML models into production environments: https://dlsyscourse.org/

- Compiler Explorer is a tool that allows you easily input some code in and check how the assembly output maps to the source. I think this is exceptionally useful for beginner/intermediate programmers who are familiar with one compiled high-level language and have not been exposed to reading lots of assembly. It is also great for testing how different compiler flags affect assembly output. Many people used to coding in C and C++ probably know about this, but I still run into people who haven't so I share it whenever performance comes up: https://godbolt.org/

To really understand what's going on here we can look at the compiled assembly code. I'm working on a Mac and can do this using the objdump tool. Compiler Explorer is also a handy tool but doesn't seem to support Arm assembly which is what Rust will use when compiling on Apple Silicon.

Play around with it in godbolt if you're really curious: https://godbolt.org/