Conversely when 99.9% of the software you use in your daily life is blazing fast C / C++, having to do anything in other stacks is a complete exercise in frustration, it feels like going back a few decades in time
I like writing things in python. It honestly feels like cheating at times. Being able to reduce things down to a list comprehension feels like wizardry.
I like having things written in C/C++. Because like every deep magic, there's a cost associated with it.
Lastly, people think C++ is not user friendly. No, it certainly is. It needs being careful, yes, but a lot of things can be done in less lines then people expect.
Try writing a matmul operation in C++ and profile it against the same thing done in Numpy/Pytorch/TensorFlow/Jax. You’ll be surprised.
The code I've written can complete 1.7 million evaluations per core, per second, on older hardware, which is used to evaluate things up to 1e-6 accuracy, which pretty neat for what I'm working on.
As soon as you step out of the happy path and need to do any calculation that isn't at least n^2 work for every single python call you are looking at order of magnitude speed differences.
Years ago now (so I'm a bit fuzzy on the details) a friend asked me to help optimize some python code that took a few days to do one job. I got something like a 10x speedup using numpy, I got a further 100x speedup (on the entire program) by porting one small function from optimized numpy to completely naive rust (I'm sure c or c++ would have been similar). The bottleneck was something like generating a bunch of random numbers, where the distribution for each one depended on the previous numbers - which you just couldn't represent nicely in numpy.
What took 2 days now took 2 minutes, eyeballing the profiles I remember thinking you could almost certainly get down to 20 seconds by porting the rest to rust.
There's nothing here for a DB to really help with, the data access patterns are both trivial and optimal. IIRC it was also more like a billion rows so I'd have some scaling questions (a big enough instance could certainly handle it, but the hardware actually being used was a cheap laptop).
Even if there was though - I would have been very hesitant to do so. The not-a-fulltime-programmer PhD student whose project this was really needed to be able to understand and modify the code. I was pretty hesitant to even introduce a second programming language.
Call overhead and loop overhead is pretty big in Python though. The way to work around that in Python is to use C-based "primitives", like the stuff from itertools and all the builtins for set/list/hash processing (thus avoiding the n^2 case in pure Python). And when memory is an issue (preallocating large data structures can be slow as well), iterators! (Eg. compare use of range() in newer Python with use of list(range())).
And if I recall correctly there was no allocation in the hot loop, with a single large array being initialized via numpy to store the values before hand. Certainly that's one of the first things I would think to fix.
I was strongly convinced at the time that there was no significant improvement left in python. With >99% of the time being spent in this one function, and no way to move the loop into native code given the primitives available from numpy. Admittedly I could have been wrong, and I'm not about to revisit the code now, since it has been years and it is no longer in use - so everything I'm saying is based off of years old memories.
Sounds like a Numba use case.
In essence, doing what you did is the way to get performance out of Python when nothing else works.
https://github.com/numpy/numpy/blob/main/numpy/core/src/mult...
Your assertion was that numpy etc will be faster than something else despite being python:
> Try writing a matmul operation in C++ and profile it against the same thing done in Numpy/Pytorch/TensorFlow/Jax. You’ll be surprised.
I mean TensorFlow is c++/cuda!
No. When I write Tensorflow code I write Python. I don’t care what TF does under the hood just like I don’t care that Python itself might be implemented in C. Though I got to say TF is quite ugly and not a good example of Python’s user friendliness. But that’s another topic.
In the early days, Python tutorial warned against adding to strings by doing "+" even though it works because that performed a new allocation and string copy.
What you were asked to do was use fast, optimized C-based primitives like "\n".join(list_of_strings) etc.
Basically, Python is an "ergonomic" language built in C. Saying how something is implemented in C at the lower level is pointless, because all of Python is.
Yes, doing loops over large data sets in Python is slow. Which is why it provides itertools (again, C-based functions) in stdlib.
https://www.boost.org/doc/libs/1_75_0/libs/numeric/ublas/doc...
I think BLAS (a C version, not the boost one) is also the library numpy is using, as numpy is not written in python. That's why it is fast, it is C.
I'm no hater of any programming language, but a strong proponent of using the right one for the job at hand. I write a lot of Python these days, because I neither need the speed, nor have the time to write a small utility which will help a user with C++. Similarly, I'd rather use Java if I'm going to talk with bigger DBs, do CRUD, or develop bigger software which is going to be used in an enterprise or similar setting.
However, if I'm writing high performance software, I'll reach for C++ for the sheer speed and flexibility, despite all the possible foot guns and other not-so-enjoyable parts, because I can verify the absence of most foot-guns, and more importantly, it gets the job done the way it should be done.
Writing good C++ is hard. People who think they can write good C++ are surprised to learn about certain footguns (static initialization before main, exception handling during destructors, etc).
I found this reference which I thought was a pretty good take on the C++ learning curve.
https://www.reddit.com/r/ProgrammerHumor/comments/7iokz5/c_l...
Ah, don't remind me Java people write C++ like they write Java, I've seen my fair share, thank you.
> Writing good C++ is hard.
I concur, however writing good Java is also hard. e.g. Swing has a fixed and correct initialization/build sequence, and Java self-corrects if you diverge, but you get a noticeable performance hit. Most developers miss the signs and don't fix these innocent looking mistakes.
I've learnt C++ first and Java later. I also tend to hit myself pretty hard during testing (incl. Valgrind memory sanity and Cachegrind hotpath checks), so I don't claim I write impeccable C++. Instead I assume I'm worse than average and try to find what's wrong vigorously and fix them ruthlessly.
I always find this remark amusing, given that Java adopted the common patterns in C++ toolkits that precedded Java.
If anything they are writting C++ like it used to be on Turbo Vision, Object Windows Library, MPW, PowerPlant, MFC, wxWindows,....
The code was math-heavy, and became completely unreadable and un-followable. He remarked "I'm a java developer, I do what I do, and as long as it works, I don't care".
That was really bad. It was a serious piece of code, in production.
If your C++ program exhibit undefined behaviour, the compiler is allowed to format your entire hard drive. Or encrypt it and display a "plz pay BTC" message. That's called a vulnerability. Real and meaningful security checks have been removed as "dead code" because of signed integer overflow (which is undefined behaviour by default).
If anything, I would guess the gross misunderstanding sprouted somewhere between the specs and the compiler writers. Originally, UB was mostly about bailing out when the underlying platform couldn't handle this particular case, or explicitly ignoring edge cases to simplify implementations. Now however it's also a performance thing, and if anything is marked as UB then it's fair game for the optimiser — even if it could easily be well defined, like signed integer overflow on 2's complement platforms.
No, it isn't. That's a completely made up fabrication. And if you had a compiler that was going to do that, then what the standard says or if there's undefined behavior is obviously not relevant or significant in the slightest.
The majority of the UB optimization complaints are because the compiler couldn't tell that UB was happening. It didn't detect UB and then make an evil laugh and go insane. That's not how this works.
Compilers cannot detect UB and then do things in response within the rules of the standard. Rather, they are allowed to assume UB doesn't happen. That's it, that's all they do. They just behave as though your source has no UB at all. As far as the compiler is concerned, UB doesn't exist and can't happen.
When a compiler can detect that UB is happening it'll issue a warning. It never silently exploits it.
> Real and meaningful security checks have been removed as "dead code" because of signed integer overflow (which is undefined behaviour by default).
Real and meaningful security checks have been removed because the security check happened after the values were already used in specific ways, not because of UB. The values were already specified in the source code to be a particular thing via earlier usage. UB is just the shield for developers who wrote a bug to hide behind to avoid admitting they had a bug.
Use UBSAN next time.
> even if it could easily be well defined, like signed integer overflow on 2's complement platforms.
Signed integer overflow is defined behavior, that's not UB. Also platform specific behavior is something the standard doesn't define - that's why it was UB in the first place.
It is kinda ridiculous it took until C++20 for this change, though
> No, it isn't. That's a completely made up fabrication.
Ever heard of viruses exploiting buffer overflows to make arbitrary code execution? One cause of that can be a clever optimisation that noticed that the only way the check fails is when some UB is happening. Since UB "never happens", the check is dead code and can be removed. And if the compiler noticed after it got past error reporting, you may not even get a warning.
You still get the vulnerability, though.
> UB is just the shield for developers who wrote a bug to hide behind to avoid admitting they had a bug.
C is what it is, and we live with it. Still, it would be unreasonable to say that the amount of UB it harbours isn't absolutely ludicrous. It's like asking children to cross a poorly mapped minefield and blame them when they don't notice a subtle cue and blow themselves up.
Also, UBSan is not enough. I ran some of my code unde ASan, MSan, and UBSan, and the TIS interpreter still found a couple things. And I'm talking about pathologically straight-line code where once you test for all input sizes you have 100% code path coverage.
> Signed integer overflow is defined behavior, that's not UB.
The C99 standard explicitly states that left shift is undefined on negative integers, as well as signed integers when the result overflows. I had to get around that one personally by replacing x<<n by x(1<<n) on carry propagation code.
Strangely enough I cannot find explicit mentions of signed integer overflow for regular arithmetic operators, but apparently the C++ standard has an explicit mention: https://stackoverflow.com/questions/16188263/is-signed-integ...
> Also platform specific behavior is something the standard doesn't define - that's why it was UB in the first place.*
One point I was making is, compiler writers didn't get that memo. They treat any UB as fair game for their optimisers. It doesn't matter that signed integer overflow was UB because of portability, it still "never happens".
There's a lot of ludicrous stuff about C and I wouldn't recommend anyone use it for anything. Not when Rust and C++ exist.
But UB really isn't the scary boogie man. There could probably stand to be a `as-is {}` block extension for security checks, but that's really about it.
Granted, C is underpowered and I would like namespaces and generics. But from a safety standpoint nowadays, C++ is just as bad. Not only is is monstrously complex, it still has all the pitfalls of C. C++ may have been "more strongly typed" back in the day, but now compiler warnings made up for that small difference.
Granted, C++ can be noticeably safer if you go RAII pointer fest, but then you're essentially programming in Java with better code generation and a worse garbage collector.
---
There's also a reason to still write C today: its ubiquity. Makes it easier to deploy everywhere and to talk to other languages. It's mostly a library thing though, and the price in testing effort and bugs is steep.
C++ had a defined multithreaded memory model and 2's compliment behavior before C did. Since you're all about UB, that kinda matters. A lot.
Point taken about multi threading.
This is why valgrind, asan and friends exist. They move the error diagnostic to the place where error actually happened.
The “forget to check an error” one is valid, but rare (usually a function will return data and an error, and you can’t touch the data without handling the error)—moreover, once you use Go for a bit, you sort of expect errors by default (most things error). But yeah, a compilation failure would be better. Personally, the things that really chafe me are remembering to initialize maps, which is a rarer problem in Python because there’s no distinction between allocation and instantiating (at least not in practice). I do wish Go would ditch zero types and adopt sum types (use Option[T] where you need a nil-like type), but that ship has sailed.
I’ve operated services in both languages, and Python services would have tons of errors that Go wouldn’t have, including typos in identifiers, missing “await”s, “NoneType has no attribute ‘foo’”, etc but also considerably more serious issues like an async function accidentally making a sync call under the covers, blocking the event loop, causing health checks to fail, and ultimately bringing down the entire service (same deal with CPU intensive endpoints).
In Go, we would see the occasional nil pointer error, but again, Python has those too.
It is memory safe but otherwise I think it was an imitation, not a reaction, to ObjC features.
Typescript (by way of comparison with other non-low-level languages) just feels far more solid wrt type system, type safety, tooling etc. C# (which I've used for years) is faster by orders of magnitude and IMO safer/easier to maintain.
> or try to get debug information for a segfault
what's the problem with opening the core dump with gdb and looking at the backtrace?
It uses debuginfod for fetching symbols - here it "just works".
No, it's even worse -- there are even MORE ways of doing the same thing now.
But I do have to say that I never managed to really get into python, it always just felt like to much of a hassle, thus I always avoided it if possible.
https://www.synopsys.com/blogs/software-security/understandi...
I think Go gets this right: it consistently uses braces for structure, but has an idiomatic reformatting tool that is applied automatically by most IDEs. This ensures that the format and indentation always perfectly matches the code structure, without needing to use invisible characters.
Have you ever tried Rust? Compared to C++, it's like lawyer speech vs poetry
I’m gonna need an example because I do not believe this whatsoever.
Let's say I'm making a lot of numerical calculations which are fed from a lockless queue with atomic operations to any number of cores you want, where your performance is limited by the CPU cores' FPU performance and the memory bandwidth (in terms of both transfer speed and queries that bus can handle per second).
As I noted below, that code can complete 1.7 million complete evaluations per core, per second on older (2014 level) hardware, until your memory controller congests with all the requests. I need to run benchmarks on a newer set of hardware to get new numbers, however I seriously lack the time today to do so and provide you new numbers.
That much is obvious from Python providing a bunch of C-based primitives in stdlib (otherwise they'd just be written in pure Python).
In many cases, you can make use of the existing primitives to get huge improvements even with pure Python, but you are not beating optimized C++ code (which almost has direct access to CPU vector operations as well).
Python's advantage is in speed of development, not in speed of execution. And I say that as a firm believer that majority of the Python code in existence today could be much faster only if written with the understanding of Python's internal structures.
Have you ever tried Rust? Compared to C++, it's like heaven
because what you wrote could be said about using C++ in the context of dev experience
10 compilers, IDEs, debuggers, package managers
and at the end of the day LLVM compiles 30min and uses tens of GBs of RAM on average hardware
I don't believe that this is the best we can get.
No. I feel there is great developer experience in many high performance languages: Java, C#, Rust, Go, etc.
In fact, for my personal tastes, I find these languages more ergonomic than many popular dynamic languages. Though I will admit that one thing that I find ergonomic is a language that lifts the performance headroom above my head so that I'm not constantly bumping my head on the ceiling.
"10 compilers, IDEs, debuggers, package managers" what are you talking about? (Virtually) No one uses ten different tools to build one application. I don't even know of any C++-specific package managers, although I do know of language-specific package managers for... oh, right, most scripting languages. And an IDE includes a compiler and a debugger, that's what makes it an IDE instead of a text editor.
"and at the end of the day LLVM compiles 30min and uses tens of GBs of RAM on average hardware" sure, if you're compiling something enormous and bloated... I'm not sure why you think that's an argument against debloating?
I meant you have a lot of choices to make
Instead of having one strong standard which everyone uses, you have X of them which makes changing projects/companies harder, but for solid reason? I don't know.
>"and at the end of the day LLVM compiles 30min and uses tens of GBs of RAM on average hardware" sure, if you're compiling something enormous and bloated... I'm not sure why you think that's an argument against debloating?
I know that lines in repo aren't great way to compare those things, but
.NET Compiler Infrastructure:
20 587 028 lines of code in 17 440 files
LLVM:
45 673 398 lines of code in 116 784 files
The first one I built (restore+build) in 6mins and it used around 6-7GB of RAM
The second I'm not even trying because the last time I tried doing it on Windows it BSODed after using _whole_ ram (16GBs)
Source for claim: That's a problem we actually faced in the Windows CI at my old job. Our test suite invoked about 100k to 150k programs (our program plus a few 3rd party verification programs). In the Linux CI the whole thing ran reasonably fast, but the Windows CI took double as long. I don't recall the exact numbers, but if Windows incurs a 50ms overhead per program call you're looking at 1:20 (one hour twenty minutes) more runtime at 100k invocations.
Also I'm pretty sure I've built LLVM on 16GB memory. Took less than 10 minutes on a i7-2600. The number of files is a trade off: You can combine a bunch of small files into a large file to reduce the build time. You can even write a tool that does that automatically on every compile (and keeps sane debug info). But now incremental builds take longer, because even if you change only one small file, the combined file needs to be rebuild. That's a problem for virtually all compiled languages.
is it some C++ header file overhead? or they do something specific?
1. Compile times: If you have one file with 7000 LOC that and change one function in that file, the rebuild is slower than if you had 7 files with 1000 LOC instead.
2. Maintainability: Instead of putting a lot of code into one file, you put the code in multiple files for better maintainability. IIRC LLVM was FOSS from the beginning, so making it easy for lots of people to make many small contributions is important. I guess .NET was conceived as being internal to MS, so less people overall, but newcomers probably were assigned to a team for onboarding and then contributing to the project as part of that team. With other words: At MS you can call up the person or team responsible for that 10000 LOC monstrosity; but if all you got is a bunch of names with e-mail addresses pulled from the commit log, you might be in for a bad time.
3. Generated code: I don't know if either commit generated code into the repository. That can skew these numbers as well.
4. Header files can be a wild card, as it depends on how their written. Some people/projects just put the signatures in there and not too much details, others put the whole essays as docs for each {class, method, function, global} in there, making them huge.
For the record, by your stats .NET has 1180 LOC per file and LLVM 391 on average. That doesn't say a lot, the median would probably be better, or even a percentile graph. Broken down by type (header/definition vs. implementation). You might find that the distribution is similar and a few large outliers skew it (especially generated code). Or when looking at more, big projects you might find that these two are outliers. I can't say anything definite, and from an engineering perspective I think neither is "suspicious" or even bad.
My gut feeling says 700 would be a number I'd expect for a large project.
aha, I remember when I was in class, the absolute rule our teachers gave us was no more than 200 lines per file
I mean, that's the initial build.
Here's my compile-edit-run cycle in https://ossia.io which is nearing 400kloc, with a free example of performance profiling, I haven't found anything like this whenever I had to profile python. It's not LLVM-sized of course, but it's not a small project either, maybe in the medium-low C++ project size: https://streamable.com/o8p22f ; pretty much a couple seconds at most from keystroke to result, for a complete DAW which links against Qt, FFMPEG, LLVM, Boost and a few others. Notice also how my IDE kindly informs me of memory leaks and other funsies.
C/C++ Header 2212 29523 17227 200382
C++ 1381 34060 13503 199259
I mean what if there are features that take significant % of whole time
What if getting rid of them could decrease perf by e.g 4%, but also decrease comp. time by 30%
would it be worth?