40 ms·

Accidentally quadratic: When Python is faster than C++

• IgorPartola 6y ago
  I’ve been thinking about this lately. Can you actually be faster than C? Like, in the sense that you can transpile any bit of Python or Lisp or Haskell or Rust or JS into C but the opposite isn’t necessarily true because not all those language support all the features exposed in C (such as goto, no bounds checking, pointer arithmetic, etc.), any algorithm for say parsing JSON can be expressed equally as efficiently in C, while a clever and hacky C-specific algorithm cannot necessarily be expressed in those higher level languages. In other words, is “faster than C” even a good metric if all it means that “if you implement something inefficiently in C it will be faster than if it is implemented efficiently in not-C”?
  • xrisk 6y ago
    Rust already does better than C for some benchmarks. See: https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/rust.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
    • coliveira 6y ago
      As the paper showed, even Python can be better than C at carefully selected tasks.
      • moonchild 6y ago
        No, the paper showed that use of a particular ecosystem default in c++ led to algorithmically worse performance then use of an ecosystem default in python. You can always beat c if c happens to implement a bad enough algorithm and your n is large enough. That's no guarantee that the performance ∆ will continue to hold once the two language implement the same algorithm.
  • bqmjjx0kac 6y ago
    Sure, managed languages can beat C performance. IIRC, the JVM can reorder branches on the fly to avoid jumps.
    • pkolaczk 6y ago
      Reordering or hinting branches has virtually zero impact on branch prediction performance on modern architectures with advanced hardware branch prediction. What's much more important for good performance is memory layout and good use of CPU caches. And in this area managed languages struggle a lot. For instance, every object in Java has 16 bytes of overhead for an object header (on 64-bit openjdk jvm). Or you can't organize objects in a flat array. Or there are some guarantees about memory zeroing which often lead to needless memory writes. Or you have to live with GC, which often wastes a lot additional memory and regularly brings unused but reachable memory to caches. Project Valhalla is going to improve some of these limitations hopefully some day, but don't expect the level of C, C++ or Rust performance.
  • offtop5 6y ago
    I think about programming languages like I do cars. While rust may be a Ferrari, some kid who doesn't know how to drive a stick and has only had his license for about a month is going to have a rough time beating you driving from New York to Texas even if you're driving a Corolla. I consider myself to be an extremely good software engineer, but lower level programming languages scare me. Recently I've been riding a lot of code in C++ for my Arduino project, but aside from that I can't stand it. You have to be such a good C programmer to make things faster than you probably could knock out using Python + numpy or something like that, but to be fair under the hood numpy uses tons of optimized C code.
    • sgtnoodle 6y ago
      I mainly write embedded software in C and C++, but I tend to use python for non-embedded stuff. The funny thing is that I've attempted to speed up multiple python scripts by replacing lists of floats with numpy arrays, and each time it's ended up slightly slower. I suspect numpy only really starts to pay off when you're doing the same operations to 1000+ elements. For modest data sets, or algorithms that don't map particularly well to numpy's operations, the built in data types do better. I also recently gave numba a go, and it was significantly slower than vanilla python. I was surprised because the decorated function seemed exactly like the type of code that numba would be good at (iterating through a list of coordinates doing square roots and some quirky floating-point modulo math.)
      • ColFrancis 6y ago
        Just checking, even though you likely know. The numpy arrays really only pay off when you vectorise your calculations. Don't expect any speed up if you're still using list comprehensions. A vector operation is just where instead of having two lists, U and V which add to make W = [x+y for x,y in zip(U,V)] you directly operate on the arrays, W = U + V. This allows the inner loop to be completely in native code. Sometimes some curious things happen where it's easier to do more vector operations resulting in more looping just because each of those operations is a vector operation and so is faster than the loop. For example, increment a number every time you see a particular element (time series breaking out subsequences) might look like np.cumsum(U == value) which is two loops in practice, but much faster than the iterative approach.
  • tylerhou 6y ago
    If you use the normal toolchains for C vs another language, the other language still can be faster because it can dynamically generate bytecode based on runtime information. In the normal C toolchain, this is not possible. However, if you used an alterative toolchain that could also generate bytecode from C at runtime, then I would bet that C would stay on top or be equal.
    • thysultan 6y ago
      You can literally emit runtime assembly in C in any C toolchain, what are you on about?
      • tylerhou 6y ago
        How do you dynamically generate machine code without linking something like LLVM?
        • sgtnoodle 6y ago
          I believe that's actually what folk are doing these days, literally linking in LLVM and using it to compile C into machine code then executing it during runtime.
          • tylerhou 6y ago
            That's what I mean by "alternative toolchain," though; some of the code that you are executing at runtime isn't being generated by the normal GCC/Clang toolchain. It's being generated by your custom setup which links in LLVM. You'd still need to compile your code with a regular toolchain, but you also need additional tools to compile, optimize, and debug code at runtime. That's what I meant by toolchain, even if it is not the conventional (static) definition.
        • thysultan 6y ago
          same way you would with llvm, you can map executable memory without llvm and execute it.
  • wvenable 6y ago
    C loses some optimization potential because of aliasing; it can't be guaranteed that some pointer operation isn't going to change a variable anywhere in memory so that limits certain reordering and loop optimizations. Languages without arbitrary pointers don't have this issue and safely assume they knows all the assignments done to values in memory allowing for optimizations.
    • kevin_thibedeau 6y ago
      The restrict keyword helps with this.
      • pjmlp 6y ago
        Provided their user knows what they are doing and never do the mistake to lie to the compiler.
      • m_mueller 6y ago
        That, or switching to FORTRAN. For numeric / HPC it is much easier to get a decently fast implementation there.
    • coliveira 6y ago
      The C language has provided the restrict keyword to solve these aliasing issues since the C99 standard. That is, 20 years ago.
      • tachyonbeam 6y ago
        I think this generally doesn't have much of an impact on performance.
        • cozzyd 6y ago
          It certainly does... it's the main reason fortran was often faster than C, isn't it? Aliasing prevents automatic vectorization, among other things. Whether or not the compiler will be smart enough to autovectorize is a different question.
  • nynx 6y ago
    Be careful when you say that everything can be converted to C. While that's true in a naive fashion, it gets rid of a lot of additional information. For example, Rust can sometimes beat C because it's (1) often more friendly to auto-vectorization and (2) it has additional aliasing information.
  • TylerE 6y ago
    Sure, theoretically. For instance, C function calls always do some things (like preserving the stack) that may not always be necessary. I sure wouldn't want to to TRY to beat a C compiler, but it seems obvious to me that it is possible.
  • kevin_thibedeau 6y ago
    C code that makes heavy use of callbacks in inner loops is at a disadvantage compared to languages with more powerful inlining facilities. Compare qsort() to std::sort().
  • rz2k 6y ago
    Because C is not machine code or even assembly language it must be compiled. Not only might the compiler not be as opportunistic about improving the human written code, but it might not fully utilize the machine's full capabilities if the compiler does not understand the complete instruction set that is available or other things like memory and different levels of cache. The compiler might not even provide access to these capabilities to a programmer who knows about them and wants to explicitly use them. In such cases, the programmer might have to use assembly, or Fortran, C++ or even something much higher level than C with a compiler that provides access or knows how to "intuit" when those capabilities are useful.
    • tachyonbeam 6y ago
      Compiler engineer here. In practice, compilers for higher-level languages often have a lot of difficulty getting anywhere close to the efficiency of comparable C code. If you take Python, for example, you have to do a lot of inlining to eliminate various abstractions. Inlining is actually non-trivial. Yes, inlining, by itself, is an easy program transformation, but knowing where to inline to get the best performance is very hard. If you inline too much, you increase code size, and you lose performance. You have to know precisely which inlining decisions will pay for themselves, and your large codebase might have tens of thousands of call sites and a call hierarchy 40 functions deep. Python also adds the fun little problem that you can redefine any function at run time, which makes it hard for the compiler to know which function you're actually going to be calling. To complicate things further, inlining decisions affect other optimizations. For example, if you inline foo into bar, do you then also inline into bar the functions that foo is calling? Do you unroll the loops from foo into bar? Etc. Also, there's an aspect that I feel is constantly overlooked, and this is the way that objects are laid out in memory. In Python, JavaScript, Ruby, you have a lot of pointers to objects. You get a lot of pointer-chasing as a result. This is BAD for caches. Each object you touch, each pointer you dereference means pulling in a new cache line. In C, you can design very compact and flat data structures. You can use 32-bit floats, 8-bit, or even one-bit integers if you want. You can have a struct within a struct, with an array inside of it, all without any pointer-chasing. Modern CPUs are constrained by memory bandwidth, and it's very hard for any programming language to beat C on achievable memory efficiency. What's worse is that we have little to no academic compiler literature on automatically optimizing for memory efficient data layouts. It's an overlooked and understudied problem. You would have to prove that integer values lie within certain ranges (hard) and also do object inlining (collapsing objects into parents) which AFAIK is also hard and not done by any mainstream compiler. So, yeah, keep thinking that a sufficiently-smart compiler will do everything for you. You will assuredly be very disappointed. Until we have strong AI, the sufficiently smart compiler is basically unobtainium. If you want efficiency, the best route is generally to have less layers of abstraction, or to only rely on compiler optimizations you know for certain will happen.
  • mhh__ 6y ago
    C - and especially idiomatic C actually withholds a __lot__ of information from the compiler. People like to think C is just a portable assembler, but by god it ain't. The reason why C programs often are fast or run faster is because the language forces you to approach almost all abstraction head on - this goes both ways however, in that linked lists are much faster to write in C than a safe array type, so programs can end up with bad data structures for too long. Since the lingua franca of the operating system is still C or C-like, there are actual optimization opportunities that go missing because of the C interface: It's hard to devise many alternatives, but if you are calling into libc for mathematics in a hot loop it may be worth avoiding it and rolling your own since the compiler can't really inline libc calls.
    • coldtea 6y ago
      >C - and especially idiomatic C actually withholds a __lot__ of information from the compiler. People like to think C is just a portable assembler, but by god it ain't. Well it might not be, but the fact that it "withholds a lot of information from the compiler" is an argument in favor of it being (a portable assembler), not the opposite.
  • WalterBright 6y ago
    > Can you actually be faster than C? Sure, in any language that provides more semantic information than C does. For example, D enables a "pointer to immutable data" type, while C does not. This can improve optimization. On a pragmatic note, C makes it easy to use 0-terminated strings, and clumsy to use length-terminated strings. The natural result is people use 0-terminated strings. 0-terminated strings are inefficient because of the constant need to strlen them. When I look to optimize C, I find plenty of paydirt in all the strlen instances. D makes it easy to use length-terminated strings, and so people naturally prefer them.
    • teddyh 6y ago
      > For example, D enables a "pointer to immutable data" type, while C does not. Wait, isn’t that what const int *x; does? I.e. a pointer to a constant.
      • WalterBright 6y ago
        In C, the value pointed to by `x` cannot be changed via a write through `x`, but if there is another pointer to that value, it can be changed through that. In D, `immutable(int)* x` cannot be changed by any reference to the value.
        • teddyh 6y ago
          You’d think that C could do any appropriate optimizations as well as D, since the C compiler (in theory) should know whether there can exist (in any given program) any writeable pointers to the same value or if there only can exist pointers to const.
          • innocenat 6y ago
            Pointer arithmetic would surely make all value mutable. Or even simple array access.
            • 6y ago
  • lmm 6y ago
    > Like, in the sense that you can transpile any bit of Python or Lisp or Haskell or Rust or JS into C but the opposite isn’t necessarily true because not all those language support all the features exposed in C (such as goto, no bounds checking, pointer arithmetic, etc.), any algorithm for say parsing JSON can be expressed equally as efficiently in C, while a clever and hacky C-specific algorithm cannot necessarily be expressed in those higher level languages. This isn't true though. Just off the top of my head: C doesn't expose the carry flag directly, C doesn't let you write signed arithmetic using twos-complement overflow, C doesn't let you share a value between separate compilation units without allowing it to be mutated... "faster" or "slower" is a pretty terrible metric anyway, given that most projects do not have infinite developer time available. But it's very hard to benchmark "equivalent developer effort" between two different languages.
  • btilly 6y ago
    FORTRAN is faster for many tasks, and is probably more popular in high performance computing. Also tasks that can be moved to the GPU go a lot faster. You can interact with those programs in C, but not natively. But some languages, like Julia, can easily move calculations to/from the GPU. And also can transparently take advantage of parallelism. Julia is in the process of growing rapidly for high performance computing. I don't know if it has officially passed C there. But if not yet, it will.
    • Leo_Verto 6y ago
      To add onto your point: from what I've heard the split at HPC conferences is about 40% C to 60% Fortran.
      • btilly 6y ago
        That sounds right. I wonder what the current split is. I'm not in HPC computing, but I see a lot of posts about how quickly Julia is being adopted.
      • SubjectToChange 6y ago
        In my limited experience, almost every language specific presentation at an HPC conference is about C++. New projects and initiatives like DPC++, SYCL/oneAPI, Ginkgo, etc are C++ based. And if you look at the open source libraries of the national laboratories, e.g. LLNL, C++ libraries are more common than C libraries, which in turn are more common than the Fortran libraries. If anything I'd say that C++ is the default for HPC conferences.
    • not2b 6y ago
      Python users who use numpy and scipy are actually using a lot of Fortran code.
  • flohofwoe 6y ago
    IME "faster than C" without going into a lot of details what manual optimizations have gone into a specific piece of the C code doesn't mean much. Writing C code doesn't automatically give you high performance, but it gives you quite a bit of wiggle room to manually optimize the code. In my mind, different languages have different "optimization sound barriers", where further manual optimization becomes impossible, or at least runs into diminishing returns. For instance Javascript can be made nearly as fast as "vanilla" natively compiled C code, but that means writing asm.js by hand, which is entirely different from "idiomatic Javascript". It's not worth it writing asm.js by hand, when C compiled to asm.js (or WASM nowadays) is much more maintainable. Same with garbage-collected languages in general. You can write fast code with very little overhead from the garbage collector, but this means you need to know exactly how the garbage collector of that language works, and the resulting code will usually look very different and will be harder to maintain than the idiomatic style of that language. In standard C the optimization sound barrier is a bit further away than in many other high-level languages but it's not all that special, on the other hand C with compiler- and CPU-architecture-specific extensions is pretty good (e.g. extensions can push the sound barrier quite a bit further away).
  • Const-me 6y ago
    > Can you actually be faster than C? Sometimes. I know 2 reasons. 1. In some higher-level languages, some problems can be efficiently solved with code generation. For instance, you can take some of the input data, generate code from that, then run the generated code processing the rest of the input data. Examples of the mechanism include Lisp macros, or .NET shenanigans like System.Reflection.Emit or System.Linq.Expressions. It’s hard to generate good machine code. Possible to do in C, but you gonna need to embed C compiler for that. They are extremely complex, and were not designed for the use case e.g. relatively slow (designed to run offline on fast developer’s computers, or on even faster build servers). If your problem is a good fit for that approach, C# can actually be faster than C. Parsing JSON is a good example. When your goal is not just parsing the syntax, but also populating structures with the data from the JSON, good C# JSON parsers gonna runtime-generate serialization code by reflecting the structure types being populated. For an isolated program, technically you can generate equivalent C code offline. But if you’re making a general-purpose JSON serialization library that’s borderline impossible to achieve in C, need reflection and code generation. 2. Modern computers are heterogenous, they have 2 chips computing stuff, CPU and GPU. Even when these pieces are on the same chip and using the same RAM like Intel’s UHD graphics or AMD’s APUs, GPU can be still faster for some problems. Not only because more GFlops (that’s not necessarily true for integrated graphics), also because GPUs have better strategy for dealing with RAM latency. They switch threads instead of waiting for the data to arrive. CPU cores only run 1-2 hardware threads each i.e. limited in that regard. That’s how for some problems HLSL or OpenCL can actually be faster than C.
    • jcelerier 6y ago
      > They are extremely complex, and were not designed for the use case e.g. relatively slow (designed to run offline on fast developer’s computers, or on even faster build servers). in 2021 bundling clang along with your program is actually reasonable - if you are compiling small functions without two tons of headers it's measured in milliseconds.
      • Const-me 6y ago
        I never tried to, but I think integration of runtime-generated native code gonna cause overhead. Where do you place these functions once compiled? Into a separate DLL/each? In .NET it’s quite easy to generate code that calls manually written functions, or access data provided by manually-written stuff. JIT runtime doesn’t treat generated code as something special, e.g. may inline calls across runtime-generated and manually written pieces of the program. With clang you gonna need a layer of indirection to integrate, with function pointers and such.
  • thaumasiotes 6y ago
    > you can transpile any bit of Python or Lisp or Haskell or Rust or JS into C but the opposite isn’t necessarily true because not all those language support all the features exposed in C (such as goto Turing-completeness tells you that it is in fact necessarily true that you can convert any bit of C into a corresponding bit of Python, Lisp, or Haskell. One obvious approach would be to emit code that implements a C runtime. For goto in specific, you don't even need to do that. You don't need a goto keyword to implement goto functionality.
    • jcelerier 6y ago
      > One obvious approach would be to emit code that implements a C runtime. how could you negate the python interpreter startup time though ?
      • thaumasiotes 6y ago
        Are we talking about compiling or performance? You negate the Python interpreter startup time by changing your implementation of the Python binary, but that isn't relevant to the problem of producing compiler output in the Python language.
    • IgorPartola 6y ago
      Let’s be careful here. All these languages are Turing complete. Heck, isn’t CSS Turing complete now? But insofar as goto in C produces a certain small amount of CPU instructions (1? 2?), can all those other higher level languages do the same? Or will something like JavaScript need a callback-based solution that will do several pointer lookups, memory allocation, etc?
  • limaoscarjuliet 6y ago
    Per Jim Keller, current source of speed in CPU is better branch prediction and data fetching prediction. It is possible C++ (or some other lang) may have more met information to drive these better, in which case it will be faster than C/assembler. https://www.youtube.com/watch?v=G4hL5Om4IJ4 https://www.youtube.com/watch?v=G4hL5Om4IJ4
  • junon 6y ago
    I've thought about this very question for years. My answer has been, usually, "no" for all cases that do not allow the developer to write straight assembly. That is, until things like C++'s stackless coroutines came about, which are a construct intrinsic to the compiler and not functionality directly exposed by C. Further, any machine code language is going to allow you specific instruction access that a compiler might not otherwise utilize (rare, but it happens). In such cases you can gain 'manual' speedups over what C could allow you to do. I would hope that is the obvious exception, however. But you are asking a very good question not a lot of developers are willing to think much about.
  • kllrnohj 6y ago
    Easily. C++ has ways of being faster that C can't really match - namely, templates. They can be hellish to write & debug, but generating type-specific functions is fantastic for optimization & performance. It's also a lot easier to be faster in C++ on key things than it is in C, specifically small-size optimizations. Yes you can do an SSO string or function pointer in C, but it's hard & painful to do so, so it's rarely if ever done. But it's trivial to do in C++, and since the standard library does it for both strings & functions, it's also commonly done. Similarly in languages like Java or C#, having first-class exceptions means fewer branches & error checking on the hot path over something like C. They are on the whole slower than C for other reasons, but it's not because C is "the best" or "the fastest" at everything. And of course you can't really do de-virtualization optimizations in C.
    • cb321 6y ago
      This seems oriented around just code bases you have seen personally rather than fundamental C limitations and so misleads. To clarify: C can do template-like macros for type specialization (and this is common in some code bases) and easier to debug ways [1] { I've personally used a variant of [1] techniques since the mid 90s }. C can do setjmp & longjmp exceptions and you can even do a TRY/CATCH like exception macros. Function pointers are in stdlib interfaces (e.g. qsort) and generally pretty common in C code. I believe gcc has been able to inline through C function pointers ("de-virtualize", though you qualify with "really" there..) in the same translation unit for like 2 decades now with the right compiler flags/annotations. It is true that C cannot do full template meta-programming or certain state save/restore optimizations faster than setjmp/longjmp. Hard/painful/trivial are all a bit more subjective and relative to exposure/practice/training/etc. Personally, I think Nim [2] is a much friendlier way to go than either C++ or Python without the pitfalls of either, but the ecosystem is admittedly much smaller. Also, I've yet to hit something where re-writing the C++/Rust in Nim did not speed things up from "at least a little" to "quite a bit". { This is more an artifact of "performance incautious programming" in the C++/Rust. Too many people fall into the trap that, since a language has a reputation for speed, they don't need to think. This is probably largely why @mehrdadn's original article had the title it did. ;-) } [1] https://github.com/glouw/ctl/ https://github.com/glouw/ctl/ [2] https://nim-lang.org https://nim-lang.org
  • Measter 6y ago
    > [..] Like, in the sense that you can transpile any bit of Python or Lisp or Haskell or Rust or JS into C but the opposite isn’t necessarily true because not all those language support all the features exposed in C [..] How would you write this (admittedly contrived) Rust function in C without invoking UB: pub fn foo(a: &mut i32, b: &mut i32) { let (new_a, new_b) = a.checked_mul(*b) .map(|new_a| (new_a, b.saturating_sub(*a))) .unwrap_or((10, 20)); *a = new_a; *b = new_b; } For those unfamiliar with Rust, it multiplies a by b, and if it didn't overflow: * a = a * b * b = b - a, saturating at the minimum value (as in, it won't wrap it just stops there) If it did overflow: * a = 10 * b = 20 And finally, does it compiler better: https://godbolt.org/z/66n8W9 https://godbolt.org/z/66n8W9
    • lorenzhs 6y ago
      Something like this? https://godbolt.org/z/6nod5e https://godbolt.org/z/6nod5e. It even produces almost identical assembly. The equivalent to checked_mul is __builtin_mul_overflow, which is a compiler builtin: https://gcc.gnu.org/onlinedocs/gcc/Integer-Overflow-Builtins.html https://gcc.gnu.org/onlinedocs/gcc/Integer-Overflow-Builtins.... Similarly, saturating_sub seems like it can be implemented with __builtin_sub_overflow.
      • Measter 6y ago
        Would those work on any compiler, or are they compiler-specific? If those are non-standard compiler-specific extensions, not even a library, is it truly a part of C++? While I'll grant that Rust only has one fully functional compiler at this time, those functions have been part of Rust's corelib since 1.0. Any Rust compiler would have to support them.
        • lorenzhs 6y ago
          They're not part of the standard, but gcc, clang, and icc (Intel) support them. Alternatively, you could emit the correct instructions in an asm block on supported architectures (x86: 'jo'/'jno' for jumps, 'cmovo'/'cmovno' for conditional moves [1]) or reimplement the operations in software [2] [1] https://www.felixcloutier.com/x86/jcc https://www.felixcloutier.com/x86/jcc and https://www.felixcloutier.com/x86/cmovcc https://www.felixcloutier.com/x86/cmovcc [2] https://wiki.sei.cmu.edu/confluence/display/c/INT32-C.+Ensure+that+operations+on+signed+integers+do+not+result+in+overflow https://wiki.sei.cmu.edu/confluence/display/c/INT32-C.+Ensur....
  • majjgepolja 6y ago
    C also does lack few features which can provide better performance or provide hints to the compiler. computed goto, non-aliasing guarantees, actual "const" - just a few on top of my mind. Generic code is generally faster in C++ unless you manually reimplement it in C. Rust can be faster than C in few cases because stronger aliasing guarantees, but few compiler bugs prevent it. Doing some good-performance stuff is also more difficult in C. Strings are null terminated etc..
  • Symmetry 6y ago
    Well, generally optimized numerical libraries like BLAS are faster than what you write yourself. But a prolbem with calling them from C is that you don't get loop fusion, each call has to go through the data before the next one starts. That isn't true with Haskell's lazy evaluation so it can be faster in some cases. Of course, C++'s eigen does loop fusion for you with template magic so to go really fast you probably want that.
  • Blikkentrekker 6y ago
    > Can you actually be faster than C? Like, in the sense that you can transpile any bit of Python or Lisp or Haskell or Rust or JS into C but the opposite isn’t necessarily true because not all those language support all the features exposed in C (such as goto, no bounds checking, pointer arithmetic, etc.), I'm not sure why you think that, especially with Rust one of the reasons it can be theoretically faster is because it allows for even more undefined behavior and has a variety of datatypes that simply exist to inform the compiler of potential optimizations.
• david2ndaccount 6y ago
  Have academic CS articles always had click-bait titles?
  • adgjlsfhk1 6y ago
    Goto considered harmful is from 1968, so yes.
    • DarkWiiPlayer 6y ago
      "goto considered harmful" was at least not wrong. It didn't say who considered it harmful, but at least it was still presented as an opinion. This title states "Python is Faster Than C++", which neither implies that this is just an opinion, nor that it isn't an absolute statement. You have to figure out yourself that it's probably hyperbolic and just referring to special cases.
      • redeyed 6y ago
        Not "Python is Faster Than C++" but "When Python is Faster Than C++"
    • schot 6y ago
      A minor historical note on this: the original title of Dijkstra's text was "A Case Against the Goto Statement" and it was the Communications of the ACM editor (Niklaus Wirth) that changed it[0]. [0]: https://en.wikipedia.org/wiki/Considered_harmful https://en.wikipedia.org/wiki/Considered_harmful
      • bregma 6y ago
        In 2021 the note would probably have been headlined "Academics hate programmers who use this one cool trick!!"
  • unishark 6y ago
    I have noticed this a lot too. I'd guess is has to do with the importance of conferences over journals in CS (as opposed to nearby fields where journals dominate). Conferences tend to be more informal and forgiving of titles like that. Of course this is just a preprint. If they ultimately publish it somewhere the editors/reviewers may make them give a more conservative title.
• mehrdadn 6y ago
  If you're wondering whether this is a theoretical or practical problem: I actually observed some of this effect in practice first, and only after thinking about it for a while did the larger issue (and the complexity implications) dawn on me. I had something like a set<string> or a set<set<string>> (or map... I can't remember which) somewhere in my program a few years ago, and I was trying to improve the program's performance. I tried breaking into it several times and found it quite perplexing that the bottleneck appeared to be the set<> container. I mean, I get that cache locality and all has an effect, but it seemed to be having a little too much of an effect. Why did I find this odd? Because other languages (like C#) have tree-based sets and maps too, but I'd never felt they were quite as slow as I was seeing them in C++. So I felt something weird must be going on. I tried to step through the program for a while and observe what's going on, and at some point, I realized (perhaps I hit the same breakpoint twice? I don't recall) that these functions were being called more often than I intuitively thought would be necessary. Which was obvious in hindsight, as less() needs to be called multiple times on the same objects (4 times at level 2). Now, this hadn't resulted in quadratic behavior, but that was only because my data structures weren't arbitrarily deep—the slowdown was nevertheless a significant constant factor at the core of my program, only linear because the data structure's depth was bounded. So once I had realized the implications of this, including that constant-factor differences can actually turn into polynomial ones, I eventually decided to post an article about it on arXiv... hence this article. Aside from the complexity issue illustrated in the article, one of my (unexpected) higher-level takeaways was that you can't just take any utility function in a program and blindly put it into a library: even if it's correct, you probably have hidden assumptions about its use cases that won't hold true in general. You really need to think about how it could be used in ways you didn't initially expect, and this may well require a different kind of code review with an entirely different mindset than before. It really drove home the point for me that writing a library is quite a bit different (and in some ways more difficult) than writing an application. It's possible there is more theory lying underneath this that I haven't touched on—it would be interesting if someone can take this and find more interesting consequences of it. For example, maybe when analyzing algorithms we need to consider something akin to a "recursive time complexity", too? Is there another useful notion of complexity that we can generalize here? Anyway, hope people enjoy reading it and take something useful away from it. :)
• halayli 6y ago
  How can this paper be taken seriously when the paper doesn't even show the compilation flags?
  • coldtea 6y ago
    Perhaps because the paper is not about the specifics of the incident (which is menial work to figure out, left as an exercize to the header) but the fact that it can happen and a general explanation of the circumstances?
  • flohofwoe 6y ago
    Once you go accidentally quadratic, any clever combination of optimization flags or compiler magic becomes quite irrelevant though.
    • josalhor 6y ago
      To be fair, certain compilation flags can change the time complexity of some algorithms if the mistake is trivial enough to figure out.
      • Lvl999Noob 6y ago
        Really? Can you give some examples? I know compilers are amazing but this seems too much.
        • mehrdadn 6y ago
          At least GCC was (and by now Clang and probably others are) known for occasionally optimizing out certain uses of functions like strlen(), which can change the time complexity in some trivial cases. For instance, if you consider strcmp(x, y, strlen(y)) where x is of fixed length, then the whole function would be constant-time if strlen(y) is optimized out, whereas it would take linear time (linear in strlen(y)) otherwise. GCC et al. can optimize out strlen() in some such cases. In practice you wouldn't want to rely on this, both because compilers aren't anywhere near smart enough to figure out complicated cases and also because these wouldn't happen in debug mode either, but it's not impossible for time complexity to change through optimization.
• deleted 6y ago
  [deleted]
• globular-toast 6y ago
  Why are we back to learning basic computer science? This isn't news to anyone here is it?
  • zo1 6y ago
    We lost that battle many years ago. Is that news to you?
  • 082349872349872 6y ago
    I once implemented a regex matcher in a SQL dialect. I forget exactly how large the "pathological" expression was that it could beat perl's C implementation for matching, but I'm pretty sure n was less than 30. See also https://swtch.com/~rsc/regexp/regexp1.html https://swtch.com/~rsc/regexp/regexp1.html
  • bregma 6y ago
    Most people never learned computer science. Few programmers I've met have.
    • acdha 6y ago
      It's also not like having a CS degree makes you immune to this — people are busy, real software has indirection which can make the implications non-obvious, and for many working programmers an awful lot of what they learned was a long time in the past and not something they use on a daily basis. The C++ code shown is a great example: when you see very simple code in the middle of a paper talking about how a particular patterns fails badly, yes, you're primed to look for a problem but if that showed up for you in code review are you really confident that you'd say something other than “followed standard practice, maybe add braces around the if statement”? The real lesson here is that nothing beats actually measuring your code to make sure you didn't miss something like this.
• derriz 6y ago
  Am I missing something? In the paper the definition of cmp3 on page 2 seems to have a bug - as defined, wouldn't cmp3([1,2,3], [1]) return 0? # Uses 3-way cmp() for primitives def cmp3(a, b): if not isinstance(a, list): global c; c += 1 return cmp(a, b) for x, y in zip(a, b): r = cmp3(x, y) if r != 0: return r return 0 This isn't generally the expected behaviour for comparing lists, surely?
  • mehrdadn 6y ago
    Oh yeah I think you did find a bug, thanks for pointing it out! I need to check the lengths as well. It shouldn't affect the conclusion (in fact I think I made all comparisons equal-length in the paper?) but I should revise it when I get the chance.
  • rav 6y ago
    Seems like you can fix this by changing return 0 to return cmp(len(a), len(b)).
• a-dub 6y ago
  cpython is faster than c++ under the following circumstances: 1) when you're using native libraries via python that were written by better c/c++ programmers than you are and you're spending most of your time within them 2) when you're using native libraries in python that are better implementations than the c/c++ libraries you're comparing against 3) when you don't know the libraries you're using in c/c++ (what they're talking about here) ...otherwise, if you're just using doing basic control flow optimizing compiler c/c++ will almost always be faster. (unless you're using numba or pypy or something). point stands about the constants though. yes, asymptotic analysis will tell you about how an algorithm's performance scales, but if you're just looking for raw performance, especially if you have "little data", you really have to measure as implementation details of the software and hardware start to have a greater effect. (often times the growth of execution time isn't even smooth with the growth of n for small n)
  • PixyMisa 6y ago
    4) string concatenation
    • piker 6y ago
      Probably (1) above, no?
      • pjmlp 6y ago
        NULL terminated strings are quite bad for performance, because you need to transverse them to find the terminator. Now try to concatenate a bunch of them in C.
        • harveywi 6y ago
          >you need to traverse them to find the terminator. Why not just do a binary search through the entire 256TB virtual memory space, finding the largest memory address that doesn't segfault? :-)
        • varajelle 6y ago
• ac130kz 6y ago
  The code doesn't compile, i.e. is in the broken state. The author is missing the comparison operators. https://godbolt.org/z/5EWhec https://godbolt.org/z/5EWhec
  • lorenzhs 6y ago
    Why do you assume -std=c++20? The code is written for C++17, and compiles just fine for that.
• froh 6y ago
  If I read the paper correctly, then it compares three-way comparison with two two-way comparisons, for a recursively defined (tree) data type. The paper points out that the convenience of just defining "less than", and heaving "equals" derived from that can be costly. Specifically, for the recursively defined data structure (tree), a three-way comparison which is derived canonically from the two way compare seems to entail not a linear but an quadratic number of comparisons. What I don't understand is what is happening in 'lt2'. this is what I'd expect for __lt__ lt(a, b) also known as (a __lt__ b) is returning True, iff a < b, elementwise for lists False otherwise for same length lists. I also do understand cmp2. (a __eq__ b) iff not (a __lt__ b) and not (b __lt__ a) so looking at cmp(a, b) = lt(a, b) - lt(b, a) I get a < b: 1 - 0 ==> 1 b < a: 0 - 1 ==> -1 a == b: 0 - 0 ==> 0 which makes sense. Now two questions arise with respect to the presented hypothesis and the paper: 1. why does the paper call lt2 twice, recursively? 2. why does the paper compare the performance of their lt2 and lt3 instead of the performance of cmp2 and cmp3? I intuit, when taking the double recursion out of lt2, which imnsho is erroneous, and when comparing cmp2 and cmp3, we'll see a performance penalty of a factor 2, between cmp2 and cmp3, and identical run times for lt2 and lt3, as it should be. What am I missing? edit: updated for clarity
  • attractivechaos 6y ago
    Initially I thought this preprint is a click bait (sorry, author), but when I read into details, I realize it is an interesting one. The key observation is the code under section 2.4.2. There, the author triggers the worse case (everything being equal in two trees) and shows that C++'s lt2 behavior leads to its horrible performance: 4.1s in C++ vs 0.018s in Python. Note that the difference is much more than two folds as lt2 and lt3 have different time complexity. PS: after a quick thought, I am not sure if we can avoid the quadratic behavior of lt2 in this particular example.
    • deleted 6y ago
      [deleted]
    • mehrdadn 6y ago
      Haha, thank you! It was pretty demotivating to see so many people immediately dismiss it as clickbait without any attempt to discuss the topic at all, so it actually means a lot to hear that you think differently now. I hope it was fun & worth the read. I know I had a lot of fun writing it. :)
• frankus 6y ago
  Here’s an article from 2001 discussing a very similar issue: https://www.joelonsoftware.com/2001/12/11/back-to-basics/ https://www.joelonsoftware.com/2001/12/11/back-to-basics/
• leecarraher 6y ago
  This is a pretty clickbaity title in my opinion. Bubble sort in lower level language X is going to be slower than quick sort in high level language Y. And bubblesort in high level language is going to be faster than merge sort for small data sets on low level language X. If you aren't comparing the same underlying routine, or data application, I don't think any conclusion should be made. Comparisons between languages is exactly why asymptomatic analysis was devised. Extract the process from the low level and hardware characteristics, and get the overall complexity growth. But this doesn't work the other way around. You can't compare different routines in different languages and expect big oh to be comparable.
  • brundolf 6y ago
    The problem is that a language isn't just its speed, a language is a UX. If that UX makes it easier to accidentally make performance mistakes, then practically speaking, things written in that language are slower than they would be otherwise. Edit: The original title "Why Python is faster than C++" is much more clickbaity than the editorialized ("When Python is faster than C++")
• brundolf 6y ago
  Reminds me of the sscanf thing that popped up a few days ago (in fact I assumed this was about that at first): https://news.ycombinator.com/item?id=26302744 https://news.ycombinator.com/item?id=26302744 I wonder (genuinely asking, not being snarky) what it is about C/C++ that seems to make these issues more common? It's also possible my perception of "more common" has just been inflated by seeing multiple examples in a single week
  • bobbylarrybobby 6y ago
    See this comment and children: https://news.ycombinator.com/item?id=26340233 https://news.ycombinator.com/item?id=26340233
  • mehrdadn 6y ago
    > I wonder (genuinely asking, not being snarky) what it is about C/C++ that seems to make these issues more common? It's also possible my perception of "more common" has just been inflated by seeing multiple examples in a single week It's an interesting question. There isn't a whole lot in common between the std::set and scanf issues except that they're both multi-pass algorithms (which I posted a comment about there), so I guess as far as the language is concerned, the question might reduce to "(Why) are multi-pass algorithms more common in C++?" I suppose one possible response, to the extent that this might be the case, might be that in C and C++ operations are so cheap (due to inlining and fewer indirections and such) that people don't blink twice before performing them multiple times, without thinking about the implications. Whereas in Python, everything is already so slow that you would try to factor out common operations if you were optimizing code. However, I'm not sure this is a broad pattern in general; e.g., the hashing example is just as bad in Python. I think the bigger explanation might be more mundane as far as the language is concerned. Some of it is likely just accidental (there's no particular reason Python couldn't have made the same decision as C++ to implement == in terms of <, for example), and some of it is just a consequence of C and C++ programmers being more likely to look into performance issues, since that's probably why they chose said languages to begin with. Even the C++ example only dawned on me after years of experience optimizing things for performance, so given that Python is already incredibly slow to begin with, if I did see this in Python, chances are pretty good I would just assume it's just the interpreted nature of Python (or a poor set implementation) that's slow and not look into it further.
• nneonneo 6y ago
  Hang on, this isn't accidentally quadratic, this is accidentally exponential in the worst case. If you set the tree branching factor to one, then the running time of C++‘s std::less becomes 2^h, but there’s only h nodes. Demo: https://ideone.com/CBIEAE https://ideone.com/CBIEAE This creates ~60 objects, but takes something like 2^30 operations to resolve (it times out on this online runner, and takes around 5s on my laptop with -O3). That's much worse than claimed in this paper! An accidentally-exponential algorithm is the kind of thing that makes DoS attacks trivial...
  • mehrdadn 6y ago
    You're right!! Thanks for pointing this out! I indeed tried to hint at the DAG case in the footnote, but didn't try to explore what happens when the DAG degenerates to a linked list. The biggest obstacle here is, honestly, motivating that an exponential slowdown is a real-world issue that concerns the average programmer, because I know for sure that many would immediately blame the data structure and tell you it's your fault for designing it that way. :-) Whereas if I can illustrate a problem that's fundamentally ingrained into language's built-in data structures by design, and that people would actually encounter in the real world, that's far more compelling. I know this because I already received such criticism for my examples, with people telling me that it's unclear how wide-reaching the ramifications are in real-world applications (which I suppose is fair enough). People really want to see examples of real-world software being improved with such small tweaks in the algorithms, whereas I didn't have the bandwidth to try to investigate that, and just tried to settle for plausible examples. That criticism would be magnified many times further for DAGs and (especially) degenerate linked lists. (I'm not claiming these are the only relevant scenarios by the way—just saying this is how it is likely to be seen by many readers.) I moved on and didn't spend more time on this (it was kind of a random paper idea I had and not related to anything else), but I think it would be awesome to flesh this out further into something more interesting and compelling and properly publish it. If you find this interesting and have the time to help joint-author & actually publish a paper on this, please grab my email from arXiv and email me! It would be great to flesh out more consequences and find more interesting examples together. I feel like there's a lot more underneath the surface that I might not have touched (both theoretically and practically), but I hadn't managed to gather enough interest from others in the topic (let alone the examples) to motivate me to look further until now!
• brobdingnagians 6y ago
  Very interesting. I think it makes C++ come across looking quite good. The committee has considered a case where C++ comparisons are lacking, and C++20 rectifies the situation by having more expressive comparisons with partial and total orderings. C++ may be slower than other languages in doing innovations, but they move relentlessly forward with trying to adopt a good way of accomplishing new features & rectifying mistakes from the past.
  • mehrdadn 6y ago
    C++20 doesn't quite rectify this unfortunately! The data structures still use std::less even in C++20, meaning the 2-way comparisons would happen twice. Except now each 2-way comparison is potentially implemented on top of 3-way comparisons. Perhaps I or someone else should get in touch with the committee to try to change that, otherwise things are going to be slower rather than faster if we still use 2-way comparisons but now they have to do 3-way comparisons underneath!