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Mike Acton gave a great presentation on this kind of optimization at CppCon2014: https://www.youtube.com/watch?v=rX0ItVEVjHc

While this is true, and linked lists are somewhere between exotic and obsolete, the presentation by Bjarne Stroustroup had buried in it him saying 'the time to find the element takes so long a vector is always faster'. But, of course if you include a search linked lists are going to be way slower.

Also I wouldn't say that the same algorithm would be faster in C, there is no reason why C++ can't be as fast as C, even idiomatic modern C++.

Exotic? Obsolete? Really? Linked lists are used in pretty much all functional programming languages (because head/tail splitting is extremely useful)

Exotic at least for performance. Not only do you have to chase pointers to loop through them, you have to deal with the heap on every append or delete. They can be great for software that doesn't have speed as a high priority.

I'd be interested to know how you arrived at this conclusion, like what domain do you spend all your time in where this is true? I frequently see linked lists used for all manner of things, in some widely deployed performant code:

https://github.com/mackyle/sqlite/search?q=linked+list

https://github.com/freebsd/freebsd/search?l=c&q=linked+list

Stroustrup gave a talk where he demonstrated that the performance of a linked list is terrible compared to a vector. Here[0] is a summary.

[0] http://bulldozer00.com/2012/02/09/vectors-and-lists/

I'm not debating that iterating over a vector is faster than iterating over a linked list, I'm asking where this guy has seen linked lists become "exotic". I'd love to dive into that code, because I've actually spent a lot of time implementing parallel arrays vs linked lists or arrays of structs, it would be nice to see how folks have addressed the pain points like sorting in constant space...

What do you mean by sorting in constant space?

I mean sorting parallel arrays with a compile time constant amount of memory for sorting scratch space, this of course also means no unbound recursion into the stack. The most obvious solution is an additional index array, but that seems pretty wasteful. This is one area, in my experience, where linked lists beat out vectors, multidimensional sorting. Consider all that data that needs to get moved around in a parallel arrays implementation, vs simply changing the * prev and * next pointers in a linked list.

Sure, but I think you need to consider how fast moving blocks of memory can be on modern computers. memcpy feels intuitively slow, but because it's such a ubiquitous operation, it's really really fast. On the other hand, changing pointers sounds fast, until you consider that a pointer read might flush everything out of cache and result in the cpu stalling for hundreds of cycles while the RAM retrieves something.

I'm not suggesting like, never used linked lists or anything. I just think that most computer science is predicated on an abstract machine with predictable memory latency, and naturally people tend to design things with that in mind, while in reality, unless you're processing data sets so large that memory latency is insignificant, an O(1) operation might be way slower than an O(N) operation.

Well for a bunch of parallel arrays you aren't really moving big blocks. Consider this:

  struct City {
    char *name;
    int   popstrat[5];
    float bounds[2];
  };

A a memcpy on struct City would take care of eight distinct values, whereas a copy operation on a parallel arrays version would take at least three separate memcpy calls (maybe eight if naively implemented).

I certainly agree with you on the weight given to O notation though.

I've actually dug into this a tremendous amount, and sorting using an array of indices is the way to go. I tried a lot of different things and a lot of different memory layouts and I couldn't beat std::partition swapping a vector of ints.

There is no way a linked list will be the fastest way to do this on modern hardware. A big part of the reason it is so fast I think is because intel cpus are so good at prefetching, caching, and instruction reordering.

If you are talking about kd-trees specifically you might want to ask questions if you have any. I've done 4 full implementations, each one with more refinement.

I see more what you are saying below but I can't reply to it. First, you could have that a vector of that struct and memmove the elements on swap, which wouldn't be too bad performance-wise. In a struct of arrays set up it wouldn't perform as well since the swaps would be multiple cache-incoherent reads and writes (sort of as you said).

Sorting an array of ints will be very fast and likely the fastest way to do it. Then you can structure your data however you want and re-write it once you've sorted it.

Yeah I'm not very happy about needing to allocate an n sized array for sort operations, but it doesn't look like there is any way around it. To be clear, the struct is really just a opaque data type for what is essentially a dynamically defined struct of arrays. Most of the work has gone into compressing the array contents, as memory is the primary point of concern. I look forward to finishing it so that I can rewrite it a few more times :)

You can always sort portions of it and then merge those portions. In that case it might actually make sense to use packets on dense memory in a linked list. You could put the packet with the highest high value ahead of the other and merge middle out.

Yeah, I'd considered something like that to avoid large reallocs - I hadn't considered independent sort state for each chunk. That would allow for a single compile time constant sort index. Sounds like a good idea, thanks for that.

No problem, let me know how it turns out, it would be very satisfying to me if it ended up working well.

He's referring to spatial complexity of sorting algorithms. For example, mergesort can be implemented in parallel and in place (O(1) spatial) easily using linked lists.

Introsort and quicksort can be done in place using an array, but constant spatial complexity isn't always priority one. Some of the more recent algorithms aren't constant (timsort, spreadsort).

Linked lists (doubly linked lists) still have valid uses, but it's generally better to use an array (or vector) until you can justify otherwise.

Those are just search results for linked lists. It isn't as if sqlite using a linked list somewhere slows the entire thing down.

I can promise you that if there is a linked list in a performance crucial place that it could be sped up a non trivial amount.

Linked lists can be convenient and they can be consistent, but they aren't fast. The pointer chasing hurts performance. The extra heap allocations may or may not be able to be avoided. The alternative is something like a vector of pointers (although I would in practicality not being using a raw pointer, it would be some sort of larger data structure). That will be faster with the exception of resizes, which should be fast in general, since what they hold should be small, but they won't be consistent.

Beyond that there is the idea that concurrency could mean that needing to lock a data structure makes a linked list less of an outlier because the time that it would need to remain locked would be smaller. In that case I think a vector would likely still be much better in general, since you could keep the current index to write to in an atomic, which wouldn't require a lock.

The point of the search results is that I'm not seeing the use of linked lists, in code bases that I could consider fairly performant, at a level that I would describe as "exotic". I'm not trying to be argumentative, I'd really love for you to point me to a code base that has replaced linked lists with arrays. I've spent the last week writing a library to do just that, but I'd happily abandon it in a heartbeat if I could (the novelty of binary packing parallel arrays wore off after day two). You're preaching to the choir on deference overhead, that I why I prefer c over c++ :)

Linked lists aren't fast in general but there are workloads on which they outperform vectors -- which is why it is important to understand the advantages and disadvantages of each data structure.

Most lower-level memory allocators e.g. tend to use linked-lists afaik because you end up removing and inserting slabs a lot, the Linux kernel also makes wide use of linked lists as well. If there were substantial performance gains to be made without other large tradeoffs it would have been done.

EDIT: Some additional info: http://www.jikos.cz/jikos/Kmalloc_Internals.html

Context matters a lot. Linked lists are great for small lists, where you want very little memory overhead and you care more about each operation taking a pretty small, consistent amount of time.

Vectors/Arrays are great when you don't care as much about the total memory consumption, but you want to reduce the amount of heap fragmentation.

A downside to using arrays like this, and likely a leading reason you don't see them used that much in the linux kernel is that every once and a while an insert is going to force a realloc which takes a relatively long time. std::vector and the like mitigate this by reserving more space than they'll need and so they minimize the # of reallocs required.

There is also performance implications to doing std::vector<myStruct> vs std::vector<myStruct*>. The later can very easily mimic the same cache coherence issues that happen with a linked list of myStructs.

So when we are talking about speed in terms of millions of items in some data crunching program, vector is almost always going to win out. But if we are talking about minimal memory footprint where we want consistent timings , linked lists are a contender.

The only thing that I really agree with in that is that context matters :) My view on the matter is pretty simple, if you know how much data you are going to handle, before your first malloc, and the dimensionality is low, arrays win from the perspective of both processing speed and low memory usage. Otherwise (a lot of cases fall into otherwise) use linked lists - for your listing needs. BTW, a linked list of arrays is a pretty nice way of getting around the realloc problem. Sorting arrays across the linked list is not very nice though...

If it weren't for pointer overhead I'd just throw everything into a graph, but 64-bit addressing makes that expensive.

Well, I think you'd agree to that if random access is a requirement, arrays are nearly always going to work better.

Even then, that might be a good strategy if inserting into a vector type data structure required a realloc everytime. However, most implementations use an exponential strategy, so for a million item vector grown one at a time, you'd see only 10-20 reallocs. Which is to say, the realloc problem is only a problem in memory constrained -- think embedded or kernel -- systems where a memory overhead of 1.5-2x isn't viable.

You can do the same thing -- and some linked list implementations do -- with linked lists, where you grab a block of nodes at a time. If you don't though, just in inserts, the vector will be faster. Mallocs are non-trivial in most applications and can easily become the bottleneck.

I think the thing you might be underestimating is how much faster it is to iterate over a vector than a linked list, nevermind random access. Cache issues really, really do matter. If you want to see this in action and happen to have a SoC dev board around, disable the data cache and run some benchmarks with vectors. If you re-enable it, you will easily see a 10x-20x improvement.

If you watch https://www.youtube.com/watch?v=YQs6IC-vgmo, he goes into some of the use cases and some benchmarks that might be compelling to you.

> Which is to say, the realloc problem is only a problem in memory constrained -- think embedded or kernel...

Or userland, when your software is processing hundreds of millions of datapoints - each with several additional independent properties.

> I think the thing you might be underestimating is how much faster it is to iterate over a vector than a linked list...

No, I'm well aware of the effects of locality - the linked list of arrays is an easily tuned compromise between speed and memory consumption (leaning much more towards memory considerations).

The point I was trying to make though is that even with 100mil datapoints you are looking at probably less than 50 reallocs. In userland, the story is even nicer because that actual reserve space in the vector is mostly going to be deferred until you actually write to it.

I don't get the point about additional independent properties. Size of the struct changes the memory you need to grab, but not the number of reallocs you have to do.

See: http://baptiste-wicht.com/posts/2012/12/cpp-benchmark-vector... for actual benchmarks on this. If you disagree with these benchmarks, and find your own results to be different, I'd love to see the write up on that, and I'm sure I'm not the only one.

Yeah the disconnect seems to me to be that I'm talking about performance from the perspective of a presupposition that I haven't adequately explained.

You've got hundreds of millions of data points to slurp into ram, each data point may have additional properties from a fixed set, data points are independent of one another - so not a good fit for a graph, adjacency matrix, etc. Disk access outside of the initial slurp is a no go, so we have to make this fit in ram.

Throwing the data point into a struct is nice and easy, plus it eases the system call overhead for m/re/callocs. But you've got a bunch of wasted space in padding, locality is gone, compressibility is much more limited.

Throwing the data into a bunch of parallel arrays is about the most memory efficient way of doing it, especially considering the ease with which you can use delta encoding and bin packing. But now every memory/sort operation is magnified by the number of arrays you've got.

As far as a writeup, I'm working on a succinct atomic map for strings - I look forward to releasing the code once I finish... and rewrite it in such a way that my employer doesn't sue me (it isn't organic to the business, but better safe than sorry).

I'm afraid I don't quite get the data problem. Is there a toy example of the kind of data you are talking about?

Not entirely clear on how the indexing would work to your benefit with parallel arrays. If there is a 1 to 1 mapping between datapoints to items in each || array, the memory consumption is the same as a struct, so I assume thats not it.

It almost sounds like you are describing a (possibly non-numeric? non-homogenous?) sparse matrix, which are usually worked with in vastly different formats for construction vs manipulation vs iterating over.

> Is there a toy example of the kind of data you are talking about?

Not that I know of, sorry.

> Not entirely clear on how the indexing would work to your benefit with parallel arrays.

Lets say that we have data that would be represented in an array of structs like so:

  typedef struct
  {
      char* name;
      int   age;
  } prsnT1;
  prsnT1* peopleAOS = calloc(100, sizeof(*peopleAOS));

You could also represent it as a struct of arrays:

  typedef struct
  {
      char** name;
      int*   age;
  } prsnT2;
  prsnT2* peopleSOA = malloc(sizeof(*peopleSOA));
  peopleSOA->name   = calloc(100, sizeof(*peopleSOA->name));
  peopleSOA->age    = calloc(100, sizeof(*peopleSOA->age));

Now why would you want to use SOA over AOS (disregarding the AOS alignment padding overhead)? Locality and compression. When you want to search on age, you don't need to waste cache space stepping over name. Age can also be bin packed pretty tightly, so in reality it wouldn't be an int* - but a variably sized ADT. Ideally name wouldn't be char either, but another ADT. Transparent zlib compression isn't unheard of.

> ... which are usually worked with in vastly different formats for construction vs manipulation vs iterating over.

Yup, classic abstract data type - doing OO in C the hard way :)

I should preface this by saying I love functional programming, I think it's a great way to write stable and testable code, it's likely the future, but...

Functional programming is much more popular in academic circles than in industry. I think academics tend to hand wave away practical concerns like cache coherency as "an implementation detail" or as an "exercise for the reader." Big O times are often considered, but rarely do researchers make a career off of "how well does this fit into an X86 L3 cache"

So yes, functional programming tends to depend on linked lists, and that's probably why you don't see it in things like the game industry. This might change in the future, for instance, I could see it being the case where having provably immutable memory could allow processors to manage more memory efficiently, but I don't think you can have a fast language that considers the CPU as an abstract entity; you have to design these things in.

Erlang is used quite a bit in the gaming industry (backend stuff for online games) and depends quite strongly on the linked list concept.

He is obviously talking about engine programming. For tools and back ends game studios will use whatever.

C is absolutely not faster than C++. Generic types in C are typically done through type erasure or an intrusive type. C++ templates explictly allow different instances of code to be generated for each of the types. So, the code can be specialized for each type, which most of the time is faster. In general, template metaprogramming allows for drastically more complex optimization at compile time.

The biggest reason to write in C over C++ is simplicity and higher level of control over algorithms and data structures. Portability is another reason though.

Very much right, unless CPU icache limits start to bite. C++ templates can generate a lot of code.

For simple functions, templates are usually always a pure win. For example, C qsort is horrible. Function pointer to a otherwise so inlinable small sort function is always going to get beaten by C++ template based sort.

But if each iteration of loop needs to fetch 10 kB of instructions because of specialized separately compiled templated forms, suddenly pointers win again. It can also invalidate TLB, branch predictor, etc. caches.

But most cases templates are a win. Just remember microbenchmarking can really mislead here, if the program as whole has an icache bottleneck as a consequence.

Yeah, but nothing says you can't specialize your C code.

And C compilers can generate specializations if everything is in the same translation unit. What I was trying to point out is that less abstract doesn't imply better performance. It isn't C > C++ > Python. What the machine ultimately has to do is what matters.

I think programs written in dynamic languages are generally harder to optimize because they're more likely to take advantage of complex features. Since these languages often compile to C, you can obviously write the same program in C, but you aren't as likely to.

I tend to use C and C++ interchangeably (and yeah, I know there are vast differences), I didn't mean to imply that you can't write code in C++ that is as fast as C, I just get bored writing "C/C++"

I wonder why we don't yet have CPUs and memory access that are more friendly to higher level structures. The Burroughs 5000 tried to do that and it worked. http://www.smecc.org/The%20Architecture%20%20of%20the%20Burr...

This is not always true but depends on the operation. Cython/Numpy take advantage of Python's extensibility at the operating system level. See: http://stackoverflow.com/questions/2119761/simple-python-cha...