This was equally true back when C vs Fortran was the big debate, and something not easily captured in benchmarks. C, as written by an expert in high performance C, was equally fast as Fortran written by an expert in high performance Fortran. C, as written by a domain expert with limited programming skills, was often very much slower than Fortran written by a domain expert with limited programming skills.

The gist, as I recall it was: the initial, idiomatic, written-for-maintainability version of the C# program was significantly faster than the C++ equivalent. Up until the end, the C# version also generally needed to go through less heroics to keep up with the C++ version. Eventually, the final C++ version did end up being faster than the fastest C# one, but, considering what needed to be done to get there, it was a decidedly Pyrrhic victory.

One huge mitigating factor, though, is that the model program was doing something business-y. I doubt C++ would have had such a hard time of it if it had been a number crunching or systems program.

That observation was actually key to the C++ version ultimately producing the fastest version. Chen replaced malloc() with an implementation that was tailored to the problem in question.

I guess the thing that I always find lacking in these discussions is a cost/benefit analysis. Yes, C++ will let you do things like that, and they will absolutely allow you to wring every last drop of performance out of what you're doing.

But, if you aren't in a situation where optimizing to that extent is cost-effective, and you're working in a business domain where frequent heap allocation of short-lived objects is what you do, so that idiomatic, standard C++'s default way of doing things is known to generally be not significantly better, and often slower, than some of the faster GC languages, then it's just possible that that you should go for the pragmatic option.

And many that are negated due to a bad std API.

You obviously ideally want both, but that's not actually possible when you have a language this powerful. So, Rest's choice means sometimes (more rarely these days but it can happen) you will write a program that is correct, but the compiler doesn't believe you and rejects your program, you will need to alter it, perhaps after alterations it's actually nicer, but equally perhaps you feel this made it uglier or slower, nevertheless you have no choice in Rust (well, you could try waiting a few years, the compiler gets smarter)

However the C++ choice means sometimes (maybe even often) you will write a program that isn't correct and the compiler gives you no indication whatsoever that there's a problem, you get an executable or object file or whatever out, but what it does is completely arbitrary. Maybe it works how you expected... until it doesn't.

The magic phrase in the C++ standard is "Ill-formed, no diagnostic required". For example suppose you try to sort some floats in C++ 20. That's ill-formed (floats aren't in fact Totally Ordered but the function signature says you promise they are) and no diagnostic is required for... whatever it is your program now does. Maybe it crashes, maybe it works fine, not their problem, good luck with that.

Now, probably if all your floats are like boring normal finite reals like -2.5 or something this will work fine, there's no practical reason it wouldn't, but who knows, the C++ language denies all responsibility. So it gets to be very "optimal" here since it can do whatever it wants and it's your fault.

Instead, to sort a slice of floats, you have to explicitly specify what would happen for the non-ordinary cases; e.g. by using `.sort_by(f32::total_cmp)`, where f32::total_cmp()[2] is one possible interpretation of a total ordering of floats. This requires writing more code even for cases where it would be completely unnecessary.

[1]: https://doc.rust-lang.org/std/primitive.slice.html#method.so... [2]: https://doc.rust-lang.org/std/primitive.f32.html#method.tota...

How is this a negative? I'd rather program fail at compile than runtime, and rather it fail loudly than quietly.

Also Rust doesn't prevent you from making optimal ordering, just a tinge more verbose.

  let geese = something(lots_of_birds).bunch().of_chained().functions();

  let geese = geese.somehow().just().count_them(); // We don't actually need geese, just #

However, if you do run into trouble those Clippy rules can save you. You probably will find you don't want them all (or perhaps any of them) at deny, but Rust is content for you to decide you only want a warning (which you can then suppress where appropriate) and importantly these are three rules, you might well decide you only hate shadow_same or shadow_reuse or something. Here's the link specifically for shadow_reuse as an example:

https://rust-lang.github.io/rust-clippy/master/#shadow_reuse

I agree! I was just illustrating the kind of tradeoff that has to be made for that to be possible.

[0] https://capnproto.org/news/2015-03-02-security-advisory-and-...

  struct Example<const N: usize> {
      pub e: [i32; N],
  }

Has C++ ever failed to snarf a feature?

But part of the impetus for Carbon is that WG21 (the C++ Standards Committee) rejected proposals that C++ should focus on better performance and safety. So maybe performance is no longer important either. What's left?

Where they've taken things which might appear on the surface to be modelled on a safer Rust feature, usually the committee insists they be made unsafe. For example suppose I call a Rust function which might return a char or might not, it returns Option<char> and if I'm an idiot and I try to treat that as a char, it doesn't type check because it isn't one, I need to say what I'm going to do when it isn't or else that won't compile.

You can write that in modern C++... except it can automatically try to take the char (which isn't there) out of the empty optional structure and that's Undefined Behaviour. So whereas the Rust prevents programmers from making easy mistakes, the C++ turns those into unexploded bombs throughout your code.

One obvious corner case: It is very common to allocate a buffer at startup and let the system clean it up when the program exits. (often this is embedded cases where the only way for the program to exit is power off). I don't know how you do this in rust (if you can - I'm not a rust expert)

This is possible with the lazy_static library or (not yet in stable Rust) OnceCell. It allows you to allocate & initialize any datastructure once during runtime and get global read-only access.

And C++ has the potential to be faster than C, mostly thanks to metaprogramming (templates, ...). It is horrible if you have to do it, but if you are just using the standard library, you don't have to feel the pain but still take advantage of it. That's how algorithms are implemented. Because so much is known at compile time, optimizers can do a lot.

The reason C++ is generally regarded as slower is that C++ programmers tend to create objects on the heap all the time because constructors and destructors make it easy. Modern C++ also discourages raw pointers and so you get references counters all over the place, essentially turning C++ into a garbage collected language. I am not saying it is bad, but it certainly impacts performance.

But if you manage your memory in C++ just as you do in C, keeping track of all your buffers reusing them, and not using more than necessary, I can easily see C++ beat C.

This doesn't match my experience. It's true that modern C++ discourages owning raw pointers, but the solution is usually unique_ptr, not shared_ptr. Truly shared ownership is actually pretty uncommon IME, usually you can have one entity which obviously "owns" the object and then any other reference to it can be non-owning.

It's also worth noting that with std::move, actually changing the refcount of a share_ptr can be pretty rare even if you do have shared ownership.

Tbh, Back then, I didn't see a problem with it. Once i started chasing down weird bugs where objects aren't freed properly because no one knew which objects own what, I have been very cautious.

Sometimes you C++ guys are just blinded by the tale of "zero-cost abstractions".

unique_ptr, like the idea of RAII in general, binds together what should be separate. Data schemas and physical layout on the one hand, and memory and lifetime management on the other hand. What you get as a result is what you deserve: The idea of "more safe and maintainable" where the "more" isn't added to the equivalent non-RAII program. No, it is added to the more convoluted, less understandable, and thus inherently less safe and maintainable program. Who knows what the bottom line is (in my experience often safety is a bit better but I pray for you if you need to debug a problem, and maintainability is much worse), but out of interest in my own sanity I know my preference.

How are they separate? Like, that’s what Rust does pretty explicitly, with great results.

It's like the classic argument against using exceptions: compared with the traditional C method of completely ignoring error conditions and not checking status, they're much slower.

One wart of unique_ptr (and other smart pointers) is that it cannot be passed in a register when used as a function parameter, at least with the System V ABI used on Linux.

Also, the caller is responsible for destruction and there is no way to specify that a function always "consumes" a unique_ptr so the compiler cannot eliminate the destructor code: https://godbolt.org/z/sz79GoETv

Of course if the compiler can inline the call or at least controls both and can clone the function with a custom calling convention then that doesn't have to be a problem. But it still sucks that even something as seemingly simple as a tiny wrapper around a pointer does come with a cost.

As a C programmer, I try to avoid tracking ownership in separate struct member fields. I try to make central data structures that keep care of the tracking. Cleaning up shouldn't happen pointer-by-pointer. Usually a much bigger context has a shared lifetime, so there is no point in splitting stuff up in individually tracked "objects". Instead you just track a bigger block of memory.

As an added bonus, you don't actually have to do a separate heap allocation.

Since 1993, I never saw any need to keep bothering with C other than having it imposed on me, C++ had enough C89 subset on it, if I ever miss coding like C and its warts.

Nowadays that compatibility is up to C11 subset.

Not true unfortunately, the "C subset" is still stuck at something that can at best be called a fork of "C95" which was then developed into a "bastard language" that resembled C on the surface, but isn't actually C (e.g. the incomplete designated init support in C++20 is the best example of this half-assed "looks like C, but isn't actually C" philosophy).

On the other hand, empirically, it is not unusual to see straightforward C programs being dramatically faster than comparable C++ programs written in enterprise style, and to also build much faster.

> Last but not least, you would have to be a masochist to write heavily multi-threaded code in C

You have to be a masochist to write heavily multi-threaded code that uses a lot of ad-hoc synchronization with mutexes and atomics. As it turns out, for many many tasks, it's also a spectacular bad way to go about parallelization, because mutexes are the _opposite_ of parallelization.

As a rule of thumb, do coarse-grained concurrency. Install a few queues, come up with a job system, and it won't be hard to get parallization right in plain C at all. Writing in C is often a good idea because what's a bad idea to do on hardware coincedes pretty well with what is painful to write.

Your only comparison cases are cases where the code in question was re-written in C. This most likely means that everyone already knew it was slow and so the re-write also fixed the fundamental problems. If the code had been rewritten in C++ it would also be faster - and since C++ allows some optimizations C doesn't it would be even faster. (it is known that if you switch from gcc to g++ your code often will run faster if it compiles)

There is a reason for enterprise style C++. Most of the time it is still fast enough, and it is a lot more maintainable.

I've never heard such a claim, can you back it up? And what does it say about the language?

> and it is a lot more maintainable

If you equate "maintainable" = readable, I've never once seen maintainable enterprise code. Everything is a convoluted mess that never gets anything done. Probably I haven't worked at the best shops, but then again, where are those? And why doesn't the language help mediocre programmers to write maintainable code?

I suspect that maintainability is almost exclusively a function of experience, not the programming language used. Experienced programmers do seem to agree that C-style C++ or even plain C is the way to go.

> And what does it say about the language?

C++ has a stronger type system. This is already known. You avoid a few bugs in C++ because of this. The type system isn't nearly as strong as Haskell.

> I've never once seen maintainable enterprise code. Everything is a convoluted mess that never gets anything done

Two sides of the same coin. While the code is convoluted, it often is doing a lot of things in a generic way. More straightforward code is possible, but only by created a lot more code, and quantity is itself convolution.

> And why doesn't the language help mediocre programmers to write maintainable code?

It does. However you have to be careful here. C++ is often used for very large problems that are also complex. I would never use python for something that is over 100,000 lines of code as you can't change anything anymore for fear that some case isn't covered in the tests and so you won't see that syntax error until months later. I maintain 15 million lines of C++ (and this isn't the largest C++ codebase I know of).

Not, I'm not arguing that C++ is a great language. It has a lot of inconsistencies, and foot guns. However it is still the best language I know for very large, very complex programs. (Note that I do not know ADA or Rust, two that often come up in context of very large, very complex programs. I would not be surprised if they are better. That C++ is better known that others is itself an advantage to C++)

> I suspect that maintainability is almost exclusively a function of experience, not the programming language used.

Sort of. As I said before languages like Python are out of the running for very large programs because they are not compiled and so you can get runtime errors. There are also intentionally impossible to write languages that we can throw out even sooner. However there are for sure other languages that can play in the very large program space. So long as we limit ourself to languages that play in the very large program space, experience is the largest factor.

IME that's mostly a myth though. A C compiler will stamp out a specialized version just as well if it can see all the relevant function bodies (either via inlining or LTO).

"Zero cost abstraction" isn't just a C++ thing, it happens mostly in the language agnostic optimizer passes. For instance the reason why std::sort() shows up faster in benchmarks than C's qsort() is simply because std::sort() implementation is all inline template code, not because of some magic performance-enhancing qualities of the C++ template system.

AFAIK out of the major compilers, gcc has the most aggressive cloning, but it's still nowhere near to const propagate the comparator from qsort. With std::sort with a stateless comparator function object (such as std::less, which is the default), you get this for free*.

* of course this is not entirely free, as this is more prone to code bloat. But, you can always type-erase the comparator, and use a function pointer, or std::function, if this ever becomes a problem. But you can't convince a C compiler to const propagate the comparator in qsort all the way through, if the optimizer chooses that it doesn't worth it.

It's also an apples-to-oranges comparison, since std::sort and qsort implement different algorithms.

A lot of std::sort's performance is actually from using the version without any callbacks. If you pass a comparator function which just compares two integers the obvious way, it gets much slower. So one of std::sort's biggest advantages is actually not that it uses templates, but that it's specialized for the common case of not needing a custom callback. Theoretically the compiler should make the two cases the same, but apparently GCC is too dumb (that's not a slight on GCC; I think people expect too much from compilers):

  ------------------------------------------------------------------------
  Benchmark                              Time             CPU   Iterations
  ------------------------------------------------------------------------
  std_sort_random                 52881299 ns     52873089 ns           14
  std_sort_with_callback_random   63319633 ns     63307876 ns           11
  qsort_random                   106803314 ns    106784567 ns            7
  external_sort_random            97642851 ns     97640888 ns            7
  std_sort_sorted                  8433311 ns      8432564 ns           82
  std_sort_with_callback_sorted   13868016 ns     13865170 ns           50
  qsort_sorted                    28098439 ns     28093720 ns           26
  external_sort_sorted            33629020 ns     33628108 ns           21

When I drill further down into everything I want to drill into, maybe I'll publish the source for the benchmarks somewhere.

This is not true. `std::sort`'s default comparator is a `std::less` object. The advantage comes from using a stateless callback functor object. If you pass a capture-less lambda instead of a function pointer, you can reap the same benefits as using the default comparator. Even if that capture-less lambda just forwards to a specific free function anyway.

In short, `std::sort(beg, end, [](auto x, auto y) { return foo(x,y); })` can be faster than `std::sort(beg, end, &foo)`.

On a sidenote, it has weird claims:

> obviating the need for ownership type systems or other compiler approaches to fixing the type-safety of use-after-frees. This means that we need one heap per type, and be 100% strict about it.

---

C lets you do most of what C++ can if you rely on always_inline. This didn't used to be the case, but modern C compilers will meat-grind the code with repeated application of the following things:

- Inlining any always_inline call except if it's recursive or the function uses some very weird features that libpas doesn't use (like goto pointer).

- Copy-propagating the values from the callsite into the function that uses the value.

Consequently, passing a function pointer (or struct of function pointers), where the pointer points to an always_inline function and the callee is always_inline results in specialization akin to template monomorphization.

This works to any depth; the compiler won't be satisfied until there are no more always_inline function calls. This fortuitous development in compilers allowed me to write very nice template code in C. Libpas achieves templates in C using config structs that contain function pointers -- sometimes to always_inline functions (when we want specialization and inlining) and sometimes to out-of-line functions (when we want specialization but not inlining). Additionally, the C template style allows us to have true polymorphic functions. Lots of libpas slow paths are huge and not at all hot. We don't want that code specialized for every config. Luckily, this works just fine in C templates -- those polymorphic functions just pass around a pointer to the config they are using, and dynamically load and call things in that config, almost exactly the same way that the specialized code would do. This saves a lot of code size versus C++ templates.

PS.: I'm just annoyed that my generic C hashtable that is written in a qsort style doesn't get function copied/inlined when it's used for more than one type.

That’s PCs that have steam installed at all.

Intel’s bare minimum current-gen i3 processor has 12 threads. That’s the absolute cheapest desktop-level processor you can get.

Your phone probably has 6 cores (though not 12 threads).

So yes, if you’re writing code for desktop hardware, it’s safe to assume you have at least 8 threads. Maybe you don’t want to consume all of them, but it’s better to let the OS handle scheduling.

https://www.techspot.com/article/2363-multi-core-cpu/

If I look around me, for instance in my whole family we're two with Steam installed but ever household has a desktop or a laptop (and generally a 7-8 years old cheap entry-level 350€ one, you'd be hard-pressed to find even a quad-core in there)

My $400 laptop from 2014 has a quad core processor.

I want to write software that people, most people, not only those with SV salaries able to buy a computer every year, can use.

Here's the current best seller laptop in Amazon in my country: https://www.amazon.fr/Dell-Inspiron-i5-1135G7-Ordinateur-por...

It's half past 2022 and the most sold laptop here in France, 7th-ranked in GDP, has 8 gigabytes of RAM and 4 cores. This is what the real world looks like. (and just a year ago it was still 4GB of RAM iirc)

The second best sale is a gamer PC which is a bit better. https://www.amazon.fr/MILUI-Modulaire-Colosseum-Livraison-30...

The third https://www.amazon.fr/Ordinateur-Portable-Celeron-Resolution... comes with a CPU that does not support hyperthreading (so 4C/4T) and does not even support friggin AVX and goes to SSE4.2 at most.

That does not mean not making use of multiple cores of course, but a software should still be able to work on a single-core. Right now we only have certifications such as https://www.blauer-engel.de/en/productworld/resources-and-en... (see https://www.umwelt-campus.de/en/research/projekte/green-soft... for the methodology) but hopefully in a few years we can start making it first heavily discouraged and over time less and less viable to create resource-wasting software - in any case this is a thing I am asking of the people whom I vote for :-)

Edit to add:

Related to your links to best-selling computers, I've been thinking about downgrading to a low-spec PC as my daily driver, and using a remote machine for the times that I truly need something powerful for a big compile or the like. That would force me to feel the users' pain. But how far should I go? Taken to the extreme, I could use a machine with a spinning rust hard drive (not SSD) and the bare minimum system requirements for Windows 10 or 11, and keep all the crapware on it to more accurately reflect the typical user's environment. But then, maybe I'd just be hurting myself for no benefit, since the pressure to value developer productivity over runtime efficiency would not actually go away in the absence of regulations.

But, in many modern languages (including c++) multi threading 1. Doesn’t significantly detract from the performance of single core systems 2. Can massively improve the performance of multi core systems, even with 2 cores or more.

For appropriate applications, the memory overhead and the cost of the bootstrapping code for instantiating a worker thread should be dwarfed by the time of actually computing the task (we’re talking about actions 100ms or longer). Not using multiple threads when you could reasonably half or quarter that time (without needing to drop support for single-core systems) is just foolish. If you’re that worried about single core performance then maintain two code paths, but at least recognize that the majority of commodity systems sold today, including the ones you listed, have multiple threads available to them to do the work that have the most painful wait times.

my rule of thumb for the software I develop is - on my desktop computer (2016 intel 6900k, still plenty powerful) - there mustn't be any slowness / lag in any user interaction when built at -O0 with -fsanitize=address. This has ensured so far that said software had correct performance on optimized builds on a Raspberry Pi 3 in ARMv7 mode.

People are apparently surprised at how easy it is to ignore the GDPR:

https://web.archive.org/web/20200813235643/http://slawsonand...

> Article 3(2), a new feature of the GDPR, creates extraterritorial jurisdiction over companies that have nothing but an internet presence in the EU and offer goods or services to EU residents[1]. While the GDPR requires these companies[2] to follow its data processing rules, it leaves the question of enforcement unanswered. Regulations that cannot be enforced do little to protect the personal data of EU citizens.

> This article discusses how U.S. law affects the enforcement of Article 3(2). In reality, enforcing the GDPR on U.S. companies may be almost impossible. First, the U.S. prohibits enforcing of foreign-country fines. Thus, the EU enforcement power of fines for noncompliance is negligible. Second, enforcing the GDPR through the designated representative can be easily circumvented. Finally, a private lawsuit brought by in the EU may be impossible to enforce under U.S. law.

[snip]

> Currently, there is a hole in the GDPR wall that protects European Union personal data. Even with extraterritorial jurisdiction over U.S. companies with only an internet presence in the EU, the GDPR gives little in the way of tools to enforce it. Fines from supervisory authorities would be stopped by the prohibition on enforcing foreign fines. The company can evade enforcement through a representative simply by not designating one. Finally, private actions may be stalled on issues of personal jurisdiction. If a U.S. company completely disregards the GDPR while targeting customers in the EU, it can use the personal data of EU citizens without much fear of the consequences. While the extraterritorial jurisdiction created by Article 3(2) may have seemed like a good way to solve the problem of foreign companies who do not have a physical presence in the EU, it turns out to be practically useless.

"Patching" that hole seems to require either action on the American side or, perhaps, a return to old-fashioned impressment or similar projection of Majestic European Power to Benighted Lands Beyond the Ocean Sea. /s

Still big multinational companies will have international branches (Google, Amazon, Microsoft, ...) that can easily be fined in their host countries.

For example, a company like Digital Ocean might have no assets of any kind in the EU (assuming that they don't own their European datacenters), so the EU cannot force them to pay a fine nor seize their assets; the EU could technically sanction them by stopping EU datacenter providers (like AWS-Germany) from renting compute to Digital Ocean, but maybe not for something like a GDPR violation.

Writing multithreaded applications increases the performance ceiling. If an application can’t take use of multiple threads, but is written in a multi-threaded way, there’s no harm done. It simply runs the multi threaded code in a single threaded way (think of ParArray) with a bit of overhead incurred for “becoming multithreaded”.

Reasoning out of adding multithreaded support for long running actions because “most systems can’t take use of the extra threads” is just irrational, especially since most modern commodity systems could have a linear improvement with the additional threads.

The single core systems are barely hurt by the memory overhead involved with provisioning CORE_NUM of worker threads. But the multi core systems can take massive advantages from it.

I wish more developers would work on RPIs for the same reason.

Steam doesn't even measure publicize information about threads on the survey, which makes it near impossible to check because not that long ago Intel locked out hyperthreading/SMT on their low/mid-grade CPUs.

Additionally, and more importantly: the Steam hardware survey _obviously_ doesn't represent the average consumer PC.

Low end systems (4 threads or less) have less potential, but they also have the most need for speed, making multi-threading quite important. And high-end systems have more threads, so going multi-thread makes a bigger difference.

I'm about to buy a PC with 16 cores and 32 threads for "normal" money.

The AMD EPYC server CPUs scale to dual sockets with 64-cores each, for a whopping 256 hardware threads in a single box. That's not some sort of esoteric hyper-scale configuration, but completely ordinary off-the-shelf stuff you can get in large quantities from mainstream vendors.

A single-threaded application on a server like that will use between 0.5% to about 50% of the total available performance, depending on where its bottleneck is. It will never reach 100%!

This matters to things like CLI tools, batch jobs, and the like, many of which are written in C, especially in the Linux world. A case-in-point that demonstrates how much performance has been left on the table is ripgrep, which is a multi-threaded Rust replacement for grep.

I think you are mixing hyperthreading, or SMT with regular "software" threading

Parallelism aka CPU-bound tasks are limited by the number of cores you have. Concurrency aka IO-bound tasks are not, because they're usually not all runnable at once. It can be faster to go concurrent even on a single core because you can overlap IOs, but it'll use more memory and other resources.

Also, "going faster" isn't always a good thing. If you're a low priority system task, you don't want to consume all the system resources because the user's apps might need them. Or the the user doesn't want the fans to turn on, or it's a passive cooled system that shouldn't get too hot, etc.

And for both of them, it not only makes it easier to write bugs in unsafe languages, but in safe languages you can easily accidentally make things slower instead of faster just because it's complicated.

Using his distinction, concurrency isn't about IO-boundedness (though that's a common use-case for it), but instead is about composing multiple processes (generic sense). They may or may not be running in parallel (truly running at the same time).

On a unix shell this would be an example of concurrency, which may or may not be parallel:

  $ cat a-file | sort | uniq | wc

This hasn't fully caught on, though.

> Concurrency is concerned about correctness, parallelism concerned about performance.

Concurrency is concerned about keeping things correct[1] when multiple things are happening at once and sharing resources. The reason why those problems arise might be for performance reasons, e.g. multiplexing IO over different threads. As such, performance is still a concern. But, your solution space still involves the thread and IO resources, and how they interleave.

Parallelism is in a different solution space: you are looking at the work space (e.g. iteration space) of the problem domain and designing your algorithm to be logically sub-dividable to get the maximum parallel speedup (T_1 / T_inf). Now, a runtime or scheduler will have to do the dirty work of mapping the logical subdivisions to hardware execution units, and that scheduler program is of course full of concurrency concerns.

[1] For the sake of pedantry: yes, parallelism is sometimes also used to deal with correctness concerns: e.g. do the calculation on three systems and see if the results agree.

I'm not sure it's fair to say C developers avoid hash tables - I've worked on several projects with hash-table implementations in them.

The 'problem' if there is one, is that such things are rarely picked up from any sort of standard library, and are instead implemented in each project.

I'm also not really sure what the problem is with 'resorting' to void*, it's part of the language. It's not 'safe' in that the compiler won't catch your errors if you stuff any old thing in there, but that's C.

> you would have to be a masochist to write heavily multi-threaded code in C

pthreads makes it relatively straightforward. I've seen (and written) fairly sophisticated thread-pool implementations around them.

Yeah C doesn't really go in for that sort of thing. The standard library tends to be much more about some minimal support for strings and interfaces to OS features like files, signals, memory allocation etc. It doesn't really provide much in the way of building blocks to be reused by application developers.

The recommendation out there on the net seems to be to look at Glib, which is used by gtk, for that sort of thing.

Another good alternative might be the NSPR - https://firefox-source-docs.mozilla.org/nspr/reference/index...

I used this way back in 2001-3 for a multi-platform project because it provides some good platform abstractions, and it looks like it has a hash-table implementation in amongst its other features.

Development in C is slow, fraught with unexpected pitfalls and you end up writing an awful lot of stuff from scratch. While this is satisfying in some ways, I find the modern paradigms of the languages I work in now to be more fulfilling - yes you throw a lot of existing components together, but you also get to deliver functionality much more frequently.

There are also a lot of very old-school C shops out there, that don't believe in modern things like CI/CD, git, even automated testing. I'm sure there are good ones too, but there are a lot of dinosaurs in that arena. One of the last ones I contracted for (for about three weeks until I quit) responded to my usual first-day question of "OK, how do we build this product?" with "Oh don't worry about that, we can sort that out later" and later never came.

That all said - I really enjoyed working on a couple of embedded devices. Seeing what you can achieve with 128kB of SRAM and 256kB of flash is challenging, and since I was a kid I've enjoyed making the computer do stuff. With embedded devices that have buzzers, leds, little displays etc, you get immediate feedback. And having to think more about things like memory allocation strategies does influence your thinking in (I think) a good way. You can definitely gain some deep knowledge!

Anything you can do in C++, you can do in C. But C++ compilers will generally optimize semantically equivalent code better than C compilers will, because C++ gives the compiler more freedom.

I.e. your data structure is like this:

  generic_hashtable Hash; // (hash->int)
  Foo *values; // where my typed data is.

And yes, C developers also tend to avoid ordered containers.