Jun 252011

This question has been bugging me for the last few days. Why would anyone use a linked-list instead of arrays? I argue that linked lists have become irrelevant in the context of a modern computer system. I have asked around a few colleagues and I include their counter arguments and my rebuttal for your reference. Please point out if I am missing something.

Lists have several disadvantages that did not exist in traditional systems:

1. They reduce the benefit of out-of-order execution. Out-of-order execution gains a lot of benefit by parallelizing independent memory accesses. Accesses in  linked lists are dependent as the address of each node is only know after the current element has been loaded into the cache. This effectively eliminates the benefit of out-of-order executing when traversing the list.

2. They throw off hardware prefetching. Most processors today employ hardware prefetchers. Hardware prefetchers are good at prefetching regular data such as arrays but they are unable to prefetch linked data structures.

3. They reduce DRAM and TLB locality. Linked lists spread data elements all over the memory. Consecutive elements can be in different DRAM pages. Since modern DRAMs take 3x more cycles to fetch random memory locations, each memory access of a linked list is potentially more expensive (Explained in my post on What every programmer should know about the DRAM memory). For the same reason, a more dispersed memory footprint require more pages to be brought into the DRAM memory which increases the usage of physical memory. Lastly, if the list is dispersed across more pages, there are a larger number of TLB misses with a list.

4. They cannot leverage SIMD. In operations like lookup, multiple elements of an array can be compared to a value using a single SIMD instruction. Since elements in a linked list have to be fetched one at a time, it is unable to use the power-efficient SIMD model.

5. They are harder to send of GPUs.  Todays GPUs have a different memory address space than the CPU. Thus, when sending a data over to the GPU, all pointers have to converted from one memory space  to the other. Linked lists have lots of pointers which makes it considerable expensive to send then to the GPU. Note: This advantage will go away once GPUs share the virtual memory with the CPU.

When I gave the above reasons to my colleagues, they gave me some counter arguments about why linked lists are better. Below is their list and my rebuttal to each point.

1.  Resizable

Linked lists can be sized dynamically.
My argument: Array with amortized doubling has solved this problem.

2. Different element types

Linked lists are superior to arrays as they allow each node to be of a different type.
My argument: I agree except that this property is rarely exploited.

3. Cheaper insertions at the ends

It is cheaper to insert an element at the head or tail of a list.
My argument: Arrays can be used with a head and tail pointers and buffers at both ends for faster insertions.

4. Cheaper insertions in the middle

In ordered lists, inserts are often needed in the middle of the data structure. A link list insert is cheaper as on average it requires traversing only 1/2 of the list, finding the right spot, and inserting the node. Inserting in an array seems expensive because it requires traversing half of the array to find the insertion point and then shifting the other half of the array to make space for the incoming element.
My argument: The analysis is naive. Arrays are actually faster. The improved memory-level parallelism, locality, prefetching, and the ability to use SIMD far offsets the fact that arrays requires 2x the operations.

5. Pointers in the middle

We can have jump pointers to make list accesses faster.
My argument: This is called a hash table, not a list.

Do you have any other arguments that support the linked lists?


Do you still use linked lists?

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  96 Responses to “Quick Post: Should you ever use Linked-Lists?”

  1. I agree with the general tone of this post — programmers need to understand hardware better, and in a broad algorithmic sense, not just a narrow implementation one.

    Still, I disagree with some of your arguments. Overall, you seem to have narrowly restricted what constitutes a ‘linked list’ while allow ‘arrays’ to be rather broadly interpreted.

    Furthermore, you are performing your analysis with what seems to be a fairly narrow usage model; perhaps the data structure access/traversal cost is insignificant compared to the work actually done at each list entry. Hardware considerations are important, but the rule of optimizing bottlenecks is still essential!

    Comments on your individual points:

    1. The out-of-order execution argument is somewhat lacking; first of all, not all modern architectures use out-of-order execution; in particular, some of the lower-end ARM processors, Intel’s Atom processor, and the individual cores in the forthcoming Intel Many Integrated Core architecture.

    2. Where hardware prefetching fails, there are software prefetching instructions (and intrinsics) that can be used. This can even be necessary with an array (say, of pointers).

    3. The TLB/DRAM locality argument assumes that you have no indirection in your array (no arrays of pointers) and your that linked list cannot take advantage of pooled allocations. It depends greatly the usage model, but the cost of a TLB miss for a single element being added to a list vs. 100s of misses as you copy a gigantic array to insert into the middle is worth considering.

    4. Again, this depends on the usage. Most people probably don’t use SIMD for their vector traversal anyway. Notwithstanding, it should be possible to traverse multiple linked lists at once in SIMD given gather support (announced in the AVX2 instructions to appear in the upcoming Haswell microarchtecture)

    You argument against ‘cheaper insertions in the middle’ is missing a lot of details. Clearly inserting into the middle of a linked list (given the position) is O(1) while inserting into the middle of a vector is O(n). Searching is a different matter; at least separate the two notions in your analysis. Where does the ’2x the operations’ argument come from?

    The argument about ‘Pointers in the middle’ is nonsensical to me. What you’ve described _isn’t_ a hash table, it’s more like Pugh’s ‘Skip lists’.

    You’ve also omitted all of the interesting things you can do with lists, like have them be circular, or element be members of multiple lists. I agree that these are unusual methods of use, but they are still interesting and potentially useful.

    I think a more interesting angle is ‘how to do you implement linked lists (or linked list behavior) in a way that is architecture-friendly?’. Things like B-Trees can be viewed as sort of hybrid arrays/lists.

    Anyway, thanks for the interesting post; just thought I’d weigh in.


    • Hi Jason,

      Thanks for an excellent comment This is exactly what I was hoping that someone will add some sense to my arguments as I was sure I was missing some details. I agree with most of what you have said. Individual responses follow:

      I do not think I have restricted the list as such. I just added amortized doubling and a head/tail pointer to the array which are both present in the linked list already (it does mallocs are has head/tail pointers). It just makes the comparison a bit fair to arrays. What do you think?

      I agree with your comment about the traversal being a minor cost in some. It may not matter at all in many cases. However, if an optimization comes for free, then I would argue that it should be done even if it does not always benefit and does not increase code’s complexity.

      Now the points:

      1. I agree that out-of-order is not in every core but the list does restrict opportunities to increase memory-level parallelism (MLP). Some in-order cores like Sun Rock employ techniques to get MLP and lists are still bad for those.

      2. In my experience, software prefetching a linked list is actually a lot harder than an array. Letting software prefetches get ahead of the actual stream requires a lot more ninja. Also, the null pointer at the end makes prefetching a bit tricky as well.

      By the way, I think we should not not compare an array of pointers to a linked list unless the link list is also a list of pointers, in which case they will be equally bad.

      3. Totally agreed with the pooled allocation argument. However, as I said, pointers of arrays should be kept out of the picture (as it is unfair to compare it with a list of data values). See my response below to the insertion being O(1).

      4. I agree SIMD is not used commonly but that trends is changing. Intel C compiler 10 is pretty good at auto vectorizing which makes it pretty automatic. I think gcc is getting better as well. ARM’s compiler is already good at it. Thus, I think arrays do win this argument. While it is possible to do multiple lists as SIMD, it requires more programmer effort. Also, I want to clarify that a gather still pretty expensive compared to a vector load. nVdia has the best implementation of gather out there and yet they discourage its use.

      Inserting in the middle of a list is O(1) in theory but only if you already know where to insert. Most likely I am missing something but I am thinking that it is almost always preceded by a a traversal of some sort. I think I need a correction here so please let me know what I am missing.

      Lastly, skip lists vs. Hash tables: Nah! it is more like a hash table and not a skip list. Perhaps we disagree on the definition of jump pointers. To me, it is a _direct_ shortcut pointer to the middle of a data structure in order to reduce traversal. Although short, a skip list still requires traversal to get to the part of the list where you think your data is. I see your point about a skip list and again an array can also have jump pointers and skip list like optimizations.

      I have indeed omitted the whacky lists. I was just thinking about a traditional list for the most part.

      I agree that we should think more about how a list should be build rather than whats wrong with it. Based on our discussion, I am thinking that a list of small array chunks can be pretty good. What say?

      Thanks for your excellent insights and correcting me. I enjoyed reading your comment and have now enjoyed writing this response. I really hope to hear from you again on this topic as I am sure I am again biased and need to be corrected:-).


      • Don’t forget that allocating arrays with extra space (eg, doubling the size when you run out of space) can sometimes give you pooled memory for free – insertions into the array already have their memory allocated.

        The only time I’d normally (abnormal situations would be where the data is distributed across machines, for example, or where fine-grained low level performance isn’t as important as the time complexity) consider using a linked lists is when I want to avoid false sharing between nodes in multithreaded code and cache-line aligning each array element would be too memory wasteful. Of course, this will only work if there is other data that the linked list nodes can share cache lines with and the memory is carefully managed to allow this… and of course, this only really makes sense if accessing individual nodes is more common than traversing or manipulating the list itself.

        • Dan,

          Excellent point about pool memory and also about distributed machines. I do not write distributed code generally which is why it did not occur to me. Thanks for teaching me something new.


          • No problem. This is something I’ve been thinking about recently, so I really enjoyed your post.

            I’ve been doing a bunch of multithreaded programming with Intel’s Threading Building Blocks and for that, linked list based structures are pretty inefficient, because processing linked lists generally requires sequential traversal, while array based structures can be more easily be split into chunks to process in parallel. (Sure, you can walk a linked list once and split it into chunks and then walk the chunks in parallel as they are processed, but that just creates more complexity). From a multithreaded point of view (which is my main interest in this discussion), linked lists and other inherently sequential data structures, as you know, are often a bad idea. I guess for the same reasons you gave regarding GPU’s.

            But I don’t think that linked lists are useless either – they do have their uses, its just that there are fewer of them than a lot of people think (and, sadly, are taught in computer science courses…)

          • “But I don’t think that linked lists are useless either – they do have their uses, its just that there are fewer of them than a lot of people think (and, sadly, are taught in computer science courses…)”

            I think you hit the nail here. It is less useful than it seems like. By the way, if you talking about the queue structures in TBB, I know that they are slow. I wrote my own for that reason. I can share some code if it will help.

          • “I wrote my own for that reason. I can share some code if it will help.”

            If you feel like making them the topic of a future blog posts, I would be very interested to hear about it! I would be interested in taking a look at code too :)

        • Nice discussion – just a quick reply;

          0) I think Aater mentioned how arrays-of-pointers aren’t ‘fair’ for comparison, but what happens when you have huge records to handle? With arrays that directly store them, you pay a lot to copy those big entries around, while lists can stay ‘where they are’. This is a big deal when inserting into the middle of an array. Furthermore, the persistence of pointers is pretty handy. If I give a pointer to someone, it’s going to remain valid for a linked list. If you have to move data around, as in an array, you’ll have to find some other way to make id that data.

          For that matter, it’s pretty handy to be able to have a pointer – just one – to be able to traverse a list. You can start right in the middle and traverse until you hit the sentinel/tail value. You need a sizes and a base pointer in addition to an index for arrays.

          1) Much talk has been made of exponential array allocation, which is also taught in CS courses. It’s a very useful technique, but it can also waste memory. There are some situations where you need to be very careful about space, and having 2x the space allocated than you need can be a problem – for example, on huge datasets on workstations, or even modest-sized problems on embedded devices.

          2) Linked lists can be modified (inserts, deletds, etc) by multiple threads asynchronously (with some pain, locks, etc), without much contention, while arrays generally cannot. Sometimes quite useful!

          3) Much of what we are taught in CS is based on theory of computation, and asympototic analysis. This blog, and our discussion, is very motivated by modern hardware considerations. I think that is a good thing, and that CS classes should do more to inform students about how hardware informs algorithm design/implementation, but I don’t think that should be added to the curriculum to /replace/ the theoretical model. That stuff is useful. Even with hardware considerations, there is a point at which the asymptotic complexity of some linked list operations outperform arrays.

          I’m really just playing devil’s advocate here — I honestly haven’t had to use linked lists for a while. I have done some stuff with trees, which can sometimes be interpreted as hybrid array/lists. For short to medium sequences of scalar data where performance is key, arrays are absolutely the way to go. I’m just calling you out on the ‘never’ generalization in this post :)

          • Jason,

            I agree with most of what you say. Specific answers below:

            1) True, I have always wondered if there is a better middle ground, something that isn’t exponential. Just for completion, I want to add two facts: (a) lists also waste space by saving points, (2) memory wastage for “unused” memory is can actually be zero overhead because it is in virtual memory which may never be pages if its never used. This is even more true for 64-bit addressing.

            2) Hmm.. I could argue that you can also optimize arrays but from a practical standpoint, I totally agree.

            3) I could not agree more. It shouldn’t replace the theory (my blog also has many analytic models:-) ) but it should make theory more realism aware. In fact, CS theory is actually tied to hardware IMO even today, but its tied to old hardware. The whole computes and memory concept is from hardware but when they were first generated, hardware used to be a lot simpler.

            Thanks for the great comment. I really enjoy learning new insights.

          • If doubling an array takes too much RAM or address space, you can also scale it by a smaller factor. You will get proportionally more allocations, but big O is unaffected. Factors > 2 work just as well. Doubling is still a good trade-off that works for most purposes, almost certainly better than wasting time bothering. But if it doesn’t work for you, you can still reap the benefits of exponential scaling with a different factor.

            And yeah, linked lists have their uses. Although I rarely use them, indeed.

  2. A correction in 5: this is called a “skip list” and they are useful sometimes.

  3. For hardware prefetching I think a counter example would be if you have a garbage collector that iterates through used memory. It will probably compact the linked list and put all the nodes sequentially, so the prefetchers would catch it just fine. It’s still an inefficient use of memory though, and if the data is primitives it will force a traversal for the garbage collector when it wouldn’t for the array (I guess if you care about garbage collector traversals you should have stopped caring about the performance of the data structure a long time ago).

    Disagree with the different data type argument. You can just have an array of pointers to handle different data types, so linked lists don’t make something possible that would otherwise have been impossible.

    One use I can think of for using linked lists is in a text editor. Each node handles a page or so of text. Adding in a character only requires playing with the current page. You don’t have to touch the next million characters. Using a skip list or some other data structure would be helpful but in general it’s a form of linked list. Another use I can think of is that it keeps you from using huge chunks of memory. That may or may not be acceptable, but from what I understand most file systems use linked lists because it’s harder to provide contiguous memory at all times. A linked list may also be considered for having better real-time properties than amortized doubling.

    Another use I was thinking was something like a game, in which you have to iterate over a list of missiles in flight anyway and update their state, but some missiles in the middle may explode and are removed from the list, but for all we know, it may be faster to mark for removal in the first iteration, and copy the remaining missiles in the next iteration, or when the number of active missiles to tracked missiles drops below a certain ratio garbage collect.

    Agree on the general point behind the post, for the most part just use an array and thank you to you, and both Dan and Jason for the valuable insights.

    I’d guess there would be more use cases for programs getting user input, because it really is useful over an array only when you know a position that you want to insert or remove from out of order, while preserving the order (if order does not matter then use an array and swap with the last element). In HPC too many things are just a stream in which you just iterate from front to back and definitely in those cases a linked list will be expensive.

  4. I think you’re coming at it from one very specific angle, e.g., thinking about GPUs. Those of us doing symbolic computation use linked lists all the time. I’ve got entire programs that basically do nothing but manipulate heterogeneous linked lists — not a rare case at all. This really is the killer feature: exploratory symbolic programming. I don’t care about CPU performance 99.9% of the time.

    A natural extension of heterogeneity is that I can also make it out of more lists — thus promoting an algorithm that works on a list to one that works on a tree, with almost no work. Promoting an array algorithm to work on trees, either by storing the tree in the array, or by writing a new class, seems like a lot more work to me.

    Insertion in the middle of an array isn’t 2x more operations. It’s O(N) times more operations. I don’t know where you got 2x.

    Another advantage: they have a simpler mutability model. I can declare that my lists are immutable (or use a language which enforces this), and when I want to prepend something and share the tail, I can do it safely, even with threads. I’m sure this is possible with arrays and there’s got to be some libraries out there to help with this, but I’m not familiar with any.

    • I agree with you regarding this article, and it very much reflects my usage of lists, at least when I’m programming in Scheme/Erlang/Lisp/Haskell/Prolog/etc… Heck, even when I program in python I do a ton of symbolic computation.

      I almost always use homogeneous lists though, and use vectors or tuples for heterogeneous values.

    • I’d also add that the performance of lists is not that bad if you know how to use them properly. For the stuff that I do, that I assume you do as well, the performance is actually better, for the reasons that you noted as well as reasons that I noted.

      • Guys, sorry I am late to the party. Thanks for reading and commenting.

        Alex, heterogeneity is certainly a good reason to use Lists. Agreed. I don’t use it that often, perhaps, because of the coding I typically do. Lists aren’t all bad and there are many cases where they improve performance as well, but there are downside one needs to be aware of.

        Tim, I have noticed that performance results for lists are usually good in cases where lists are used more like arrays, nodes allocated in consecutive locations and not too many insertions/deletion in the middle. Thus, prefetching can fetch the next nodes as a side effect and caches still provide spatial locality. Randomly allocated and connected nodes, where lists are considered good, are actually very poor performant. In my field, computer architecture and core design, people get entire PhDs on how make hardware that can make linked structures perform reasonable.


  5. Anyway, your article was interesting and thought-provoking, although I don’t really agree with your point, overall.

    I use lists as my bread and butter data structure, along with tuples. (immutable arrays) I assume most of the programmers commenting are system level programmers, because they seem to not use them very much at all.

    Lists are an inductive data structure, trivial to make immutable implementations, with O(1) cons/insert at the front, O(1) first and O(1) “rest”/”tail”. Since they enable tail-sharing, it is trivial to fold over a list and produce a new one without throwing the original away, while sharing many contents with the original. Lists are not intended for random access, so I don’t think that is a fair criticism at all since no advocates of lists insist on them being used for such.

    Cons/append to any part of an array is extremely expensive, since you have to copy the entire array, except to the end of a vector or array”list”, (the worst terminology ever) and folding or recursing over an array persistently requires a lot of extra copying. Besides that, folding over an array is going to require a non O(n) append to front operation, which makes it prohibitively expensive.

    In contrast to what you say, the cost of resizing a linked list is much cheaper than an array, unless you have some special array. I think you’re comparing special array structures, which still have drawbacks relative to lists, while comparing them against literally regular mutable linked lists.

    Even appending to the end of an array isn’t necessarily free, since you might have to pay the amortized cost, which would make resizeable arrays completely unsuitable for real-time applications, which I assume many of the people on this blog would care about.

    Operations over lists are not very hard to parallelize. (and trees work even better for this purpose) For example, in the programming language Erlang, I wrote an email client, and to compute the summary view, I process a list of several thousand email data. Since I use higher order functions fold/map/filter/etc, I simply used a parallel list library, (eg. using plists:map vs lists:map) and it was able to compute much of the mail data using all of the cores on my machine, without making me change my algorithm at all.

    Finally, for random access. Arrays are obviously king here, but there are “Random Access Lists” librarires available (based on a datastructure designed by Chris Okasaki) for Erlang, Scheme and Emacs Lisp, which essentially treat the lists internally as a tree like structure, which has O(1) for cons, first/rest, and O(log(n)) for append, search, etc… So basically I have no idea why this library does not replace linked lists in every language, (except that every language it is used in would use pattern matching, and so the pattern matching syntax would have to be changed to pretend that this tree structure is a list) because unlike arrays, it has O(1) for the operations that lists are heralded for.

    From a high level perspective, it is extremely rare that I actually say “gee, I wish I could have the 4th element in that set, not the 5th”. If I want that, I probably really want a tree, hash table, or a dictionary. In the case of a collection, unless I’m searching for something, I want to apply a function or predicate to the elements of a collection, since they would most likely be the same type, and a linked list is just much better than an array for that. It really depends on what programming language you are programming in, though. In C, I use arrays for almost everything. Same thing for C++.

    In a java game project I’ve worked on, although I find the java linked list library absolutely atrocious, (because it has no way that I know of to get the rest of the list without destroying the head, iterators notwithstanding) the programmers I worked with used them a lot, and used them where they were appropriate.

    So, I’m not saying that lists are an end all data structure, but lists are definitely not a replacement for them, or at least they are not as good at the things lists do well. The “random access lists” actually provides a superset of the advantages of lists, however. I’m also a big fan of trees. (I wrote a red-black tree library earlier this year)

  6. Oops! Typo… When I said

    “but lists are definitely not a replacement for them, or at least they are not as good at the things lists do well.”

    I meant to say “but arrays are definitely not a replacement for them, or at least they are not as good at the things lists do well.”

  7. Also when I said “a non O(n) append to front operation”, I meant to say “an O(n) append to front operation”.

  8. One point I want to highlight here. Linux, which is one of most portable operating system uses linked lists extensively in its internal data structures.

    • Trs,

      Yup. Point well taken. Doesn’t mean its good. There a lot of people who think Linux leaves a lot of performance on the table. Lists are not evil, they are often used sub-optimally.


  9. Hi

    For real-time systems, a linked list may be useful when you need to ensure that an insert will take a bounded length of time. If the time constraints on an insert are such that the time to create/copy the elements in the existing array fits within your time bounds, then an array would be acceptable.

    • Mike, thanks for the comment and sorry for my late reply.

      Very interesting point about real-time systems. I didn’t think about it. Actually, the RT systems are also impact by cache, DRAM, disk variabilities so the programmer has to be ultra conscious to begin with even with lists.


  10. Write a simple benchmark of an array vs a linked list.

    Now insert 10 million items at the start of the array / list and see who wins.

    • James,

      Thanks for reading.

      The experiment you are suggesting is for a list of 10M element, with unknown size, without knowing the type of operations being performed. The answer will be a very specific result, not a generally applicable conclusion. For example, if you ever have to traverse your 10M element list, the array can be up to 20x faster. If you insert in the middle, the list will be faster but will make your traversals even slower as the memory access pattern will become more random. The idea is to raise awareness of the trade-offs.


      • This time I agree, Array elements can be accessed faster than linked list. Lists are always need to be traversed from either very beginning or very end. :( but array can definitely be accessed from anywhere. Once my professor gave me such a program, where I had to find which element is present at the given position. obviously linked list was taking longer than array because I had to traverse it from beginning ( though it was doubly linked list ). but with array , it was simple using subscripts of it. also ,linked lists sometimes eat a lot of memory as they grow larger. array remains according to only element size.

  11. Haskell uses lists a lot, but the usual goal is to optimise them away so that you get a loop that is similar to the one an imperative programmer would have written.

    The standard “String” type is a linked list of characters, which is exactly as efficient as it sounds. To get around this libraries like “Text” and “Bytestring” have been written which have linked lists of chunks of text or bytes. This avoids the memory architecture problems you highlight.

  12. You seem to be ignorant of intrusive data structures.

    If I want to delete an object that is currently in multiple linked lists, I have O(1) deletion from each intrusive doubly-linked list. Without any dynamic allocation overhead and without searching anything.

    If I want to delete an object that is pointed by multiple arrays, I have a whole lot more of O(N) work to do.

    Inserting/replacing in the middle is O(1) with lists, not O(N). Your assumption that you need to traverse the list is false: you often have the element you want to delete from lists already, and you often have a position you want to insert that element at.

    I am suspicious that the author here used libraries like C++’s STL that do not expose the actual power of these data structures because they are not (so-called) intrusive. Thus, people who learn about these structures via use of the STL have a false idea of the properties of these basic computer scientific structures.

    • Eyal,

      Actually, I am well aware:-) I am a hardware architect, so I have programmer these structures in assembly, let alone C.

      Your point is well taken. Question is: How common is that case? In fact, many would argue that its bad practice to use intrusive data structures because of maintenance overheads, scaling, security etc but that isn’t my point at all. My goal in this article is to raise awareness that there is a lot more than the O notation in computing. O notation, as discussed in algorithms’ courses, is entirely misleading since O means nothing until N is big and it misses so many details like caching, memory, disk, and prefetching. Lists lead to random accesses, which throws memory prefetching, spacial locality, worst of all, disk locality out of the door. Combined, these effects can amount to one to two orders of magnitude slowdown. Ignoring these for no good reason is bad; ignoring these for a good reason like it can save me 3 million memory access is good. The decision is case specific, but the analysis needs to in the back of programmer’s head.


      • In my C work, that case is *extremely* common. Almost every asynchronous operation we have (and we have plenty) is represented by objects with intrusive linked lists in them.
        I don’t believe that they introduce “maintenance overheads” or “scaling overheads”. See Linux’s list.h.

        If I have a pointer to an asynchronous request I am processing, removing and adding it to/from lists is going to touch only a few pointers. If I do the same with dynamic arrays I’m going to be messing a lot more cache lines.

        Also consider that growing arrays require a lot more dynamic memory management. With linked lists, you can often get away with no allocations at all. Once you use arrays, you necessarily have an external pointer store that besides for messing your cache lines, will also require dynamic growth/shrinkage, or a lot of waste (each possible container array will have to be as large as the worst case).

        These dynamic allocations are going to harm performance even more.

        In my particular line of work, and the kind of (soft) real time C code that I have to write, intrusive linked lists are more commonly useful than arrays, and using arrays where the lists are used would be detrimental to performance.

  13. Show me the numbers to back up these claims. Without performance testing of various scenarios you cannot say for certain.


  14. My 2c:
    1) There’s a lot more embedded programming going on these days due to stuff like arduino. for most embedded apps, linked lists are still going to retain their historical use cases.

    2) Linked Lists can be a /simpler/ solution for some problems. Tuning at this level is premature optimization for like 90% of software which needs to a) work and b) be simple for maintenance. Most software out there is not performance critical, doesn’t need parallelisim, and should be tuned for clarity to ease maintenance. For this sofware, the data structure that makes for the simplest implementation is probably what should be used.

    • Hi Devon,

      Thank for reading and taking the time to comment. Both points are very well-taken. My responses are below:

      1. I think there are tons of cases where linked-lists make sense. My point is that there use is not harm-free and caution shall be used. On the side, I do not know if embedded specifically requires linked lists though. I must be missing something. Can you point me to some use cases?

      2. I am afraid I do not agree with just using the easiest tool for the job. I believe in using the right tool for the job. Using a different data structure is one such decision whose complexity can often be hidden behind an abstraction layer but making the right choice goes a long way. However, using the easiest tool at every step can lead to disasters as well. For example, if you look at Hadoop framework you may be shocked at how much performance they leave on the table. That software can be times faster but the programmers always used the greedy methodology of taking the easiest approach. I am not a purist and realize not all software needs to be fast, and most of my programming posts are for power coders, who write software that is used either in critical solutions or used enough times to make their performance important. I personally still use linked lists either in cases where they are the right tool or in cases where performance doesn’t matter.

      Thanks again,

  15. Hi,
    Nice article!! You have kept a much valid points to prove your point. Even I agree with you, Most importantly Linked Lists will reduce DRAM and TLB locality

  16. Here is a counter argument for you for the second counter argument you get from your colleagues.

    It is possible to implement a singly linked list in an array.With it,you get the benefit of an array as stated in this post and the benefit of a linked list since each node can have its own size( the important part of a data type is the size it takes).

    An example implementation is here: https://raw.github.com/mhogomchungu/lxqt_wallet/master/backend/lxqtwallet.c

    Basically,the underlying data structure is an array of char and on top of it,there are nodes of variable lengths and what makes them linked list structures is the fact that the information on where the next node is is found in the current node.

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  18. [...] came across this article: Should you ever use Linked List. It cites that given the technological advances in available memory and RAM structures, using [...]

  19. While I agree with your article, there is one (and the only one I’ve found so far) important feature of a linked list which an array does not have and that is memory persistence. I can store something in a linked list and then have external pointers to it. If I try to do this with an array then as soon as the array gets resized, those pointers become invalidated.

    For this reason I find myself still using linked lists sometimes for things like memory allocators (e.g. maintain a linked list of memory pages).

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  61. Linked lists are the basis for certain immutable, persistent (shared structure) data structures. You can read Chris Okasaki’s book on Purely Functional Data Structures (https://www.cs.cmu.edu/~rwh/theses/okasaki.pdf) which outlines some examples. Shared structure is generally only possible with some concept of “nodes” that refer to one another. The benefit here, is that persistent data structures allow the underlying data to be treated as though it were immutable (which makes concurrency and reasoning about correctness easier) while reducing the memory and performance overhead that copying the data would demand.

  62. 뾫ힴ쇅뛸쇾쓺틇솻낳뗔뗀퇄ퟹꏓ좬늴횻떪컀쪪쎲펴탐킩 间歇性癫痫病症状 탄ꇩඣഊ †휠즪틭튣쾣맲뇳쿶킯샐ꏱꆬ낰뷗훰뷷듌죳쟋쿫얢ꆭ캣쏒훇쪻틇붪놫톾풧톺풧첱쾧죂뚥틸ꇑ늣쎢펻원쯤틻쯢ꆼꆣනഊ †젠샸쮭떹좭떻ꏀꆺ잰쓫쏣쯇랫벽퟇힡볔떺진럭ꇝ랣뇇죈주퓋캱뺴늭엃퓐탊퓭죙돫햡ꏟ솬뾢쟌훽돰뇶붾듬죳ꇼꆣනഊ †퐠놭튾놪랬떢믄뇰뎬쎡뻦헍퇢뇹톻솹쿋샂ꎴ떬쪫삷뾳퇋풧몺냍샍뾭퇋풧횺벮뗤믄티캩뚶컹틞뇉뗤엃효웘샰ꆴඣഊ †퀠캡틨뻀쇉퓴돚즡ꏏ첬죆퓽뻚릭쯽짽뇭쫟떱짍뛹횣쇶쯋볽뺸ꏤ햬닢ퟅ쿟쳂좨ꆥඣഊ †뀠샍뾭퇋풧쾺죔놻벻얤솭ꇋ솣쫮삷뾳퇋풧횺죚폋탐뺩톪뗈쫄ꏇ뚬쫔뗖뛚룾ퟶ짟쳏떨ꏘ뺬좹튻쪲틇쎻쯻쫄벮튶짔뗏믄ퟪꇚ튣즻쳏ꎨ늬떻니엃탐닻놼죈뿼쪪ꎼ쮬뻻틍뻑쪭럍독쇶ퟋ볔

  63. 햬웢쫤튵늲쒻맜훖쓝ꏁ횬쒻쯜첵포닐늻럢퓧ꇆꆣනഊ †선 青少年癫痫治疗方法 귵뭚쪹틇튡춡ꎷꆬ햰듢닎짱쪣쾧생쏯웦쫤펵뫐뛜뷠퇌ꎵ좬컴닒쒻컜주ꎡ뮬특캻뢶ퟸ볔햺뷒뿨ꏚ쒬틇뫔믳뮹돡쫶ꏂ쾬럳퓋뷴쫓ꏕ햬뻢쫍틇뢻짶뿮뷌퇌낵ꎡ늡릻쫢뗇럁ꏋ뮬펹샐샻뻍뛼쯓컹쾽죧ꏋ뮬펹듐뛳짓죌뗋뗈ꏈ뚬뾼쓉뇜떻뻐민죬돫ꏇ쪬쪵삩폯춦뫢ꏏ캬쏒뇇탘틫뚪쾨틂뢻럶럀횶뛆ꇈꆣනഊ †밠�풲췞솬귵콚럫ꎨꆬ쮰떵횽뛆ꏈ캬헒쇢첽틬풲뿚슼ꏇ욬쫤즵죌췋샹횴삮컠쏒닇뾻쓉뛜뻏ꏸ떬캫쏒뿇틉짔뗨볈ꎶ놬죈볧볗놶쫭햾햽ꏹ튬볒놶쫭놾햸ꎽ놬볻놶쫭몾웍ꎽ풬닚춻떬쫄욱쫚킵닐춻떬뻄붯들쫫ꎩ튬뫔컳쏒헇솼뗬도돇뛘쇠ꏋ튬캲놴맘뗋맃샽ꎴ떬횫튻춪탆뗐볈횶뛆ꏈ쒬쏇잴쾰뗟도돇ퟘ죔횻떪룀퓃쏵내ꇬꆣනഊ †ꄠ뺰쫼쮦떵뫃

  64. 톢캧돤쏕ꏔ쮬탻후폐짖웽티쮻욿듚ꏽ쮬쓻닜쒻틜쒲퇜떧튽떻ꇣඣഊ †선뇵틭욲폄탐죋ꎤ몬뫇탇떦ꏀꆺ풰삭좴듧ꏋ캬믒쪹뗇틚뒻쳎쯽펵죐퓋뫚쮺횮 癫痫怎么才能快速根除 쇐캷ꏤ욬쫤ꎵ났쓑냣얲뗅쒽샄뛯뾼틉ꏔ떬릫볘쫼쓇틣뚪컔쯒쪵뮵ꎰ캬닒쾻뮲놶좻웋웛ꆭꆣනഊ †쬠떵햽샢ꏯ솬뇵뗭쓄릿뇢뗤죃샱웻샰ꎴ힬쪢쇓귵ꍚꆬ캰쯒떵믄ꎰ쒬쏣냷싗ꏰꆿනഊ †선귵쑚쒬떬췣ꎷꆬ횰뚶쏹냷ꏗꆡනഊ †ꄠ쎰냷뻗뫍ꏃ쒬뗣틄잪ꏳ캬ퟒ믔뿡슼ꇇꆣනഊ †선뇵뗭뇄쟭뻩쿍뇱珤ꢨ贈믒겣펴뮲輸붵싎춺겣훓쎵쿑겣즿놵뿄꫗ꊻ뇊겣뿄훓�쎵춺듀ꎡ਍਍††ꊻ믒뇖뷕뷕꒾꒾겣ꮵ﯋쓐탖輸쯁곍六쒵�겣뮲좵꪿�ꊻꋁ첿싏쒿럍삵몣낡뛖禮룔視媭�겣튿꺲뢸즳ꯈꎡ놡਍਍††쳈뮲ꇗ

  65. I have learnt a new topic from here. A huge conversation makes me more interest on linked list.

  66. 쇄솦몿뛍볾춶죪쪫쇇뢽룶쓅ꇮඣഊ †렠닕뗅퓄욪쟸놽못웤뻆ퟍ틣훔쎤틷잻ꇐඣഊ †술쳞�ퟖ심뎶듶퇸떪퇄돀ꏝ햬쓸뗼탄솦웋샰ꎴ떬ꏀꆺ좰컃룒쯦쓟컣쪪쎲뮴룡볐떤삽 小孩晚上睡觉抽搐是癫痫病吗 ꏤ튬컲ꆪꆭ쒭ꏣ슬ꏭ즬ꏏ뺬ꏍ튬ꎪ쮬ꏀ솬ꏋꆡනഊ †툠ힻ틖뺻ꇤඣഊ †툠뺻틤뚻ꇙඣഊ †쬠춵훪몮ꏳ슬쳞죬닌ힻ떡뿄탱욦샰ꎴ쮬틻쎲펻쿐뗫ꎽ뺬좹뒻랥솢쏋짫ꎱඡഊ †촠췲쏲펻쿐뗫ꆽඣഊ †눠잢ꇒඣഊ †쨠솥뻩붳뗧탄컞쪪럍짅믱헰뗦쓄쏜짫떱ퟛ뻰붳뗧쟄햿ꏟ쾬퓖뻚쇍몬쎣컰헤훢뗖ퟛ럛뻥붳뗧쟄햿뛟튼놪즻믱룰쏸쇫ꏋ슬쳞탬샄쯯늵뎻뗶탄럋ꇜඣഊ †ꄠ슰ퟺ뫬퇺ꏔꆡනഊ †ꄠ캰닒뾻쓉믜쯡ꇀꆣනഊ †ꄠ캰뿒쫉뗇ퟛ럛뻥붳뗧쟄햿ꏟ캬뿒쫉믇쇮틋춻쯲쟄뚧쓠뗪도벬잶햿ꏟ캬퓒쏵뾴쓉믜쯡쓀ꏘꆿ

  67. 뛻ꇏ삣쫏쮵ꎵ햬퇢뗹탄룔ꏱ욬쫤몵뛜쿔돴떿컄뾸ꏚ쎬쒻쏇뚴뗠쳄쳵뿵뿲ꏲ튬첻틗첻뗗ꇄඣഊ †켠돴쮿죤늻뺻엥퓂뿆탉ꏄ떬쎫놻틘풪헚볢쫾쟂짩ꏏ펬웫럤즢쏺뚬 青年癫痫病容易治疗吗 ꏜ떬뢱룶뚱돓튤쎲쪻쎲ꎴ횬튻쎪펻췐탾떲쮽뗻쟄짐냭좲뺫뿍틉쇔ꇋඣഊ †퐠뿆탉쇄즳틏뻀쏉쪻쎲쎴쿷뗔뇄쟭ꏩ떬뗣췣떷ꏀꆺ쾰퓖쫚컇컊쳊쫢벱ꏤ횬펻컐럥훖뗓쫄벱ꏤ캬쳊뻢송뺿벫틲떻ꇣꆣනഊ †촠톬뗹도뺡튰랲즢퓺웚쯤뗻럘ꎽ쪬쎮뫻탋뗄ퟜꏓ쾬웱쯤죻뷋쫢헍듢죎컎뗱쓄죚ꇝඣഊ †뼠쓉퓜뿆탉룄닕뗅뻄뛙ꎯ펬뗐쿣ퟅ송웋쯤뗻죄ꏋ떬횼돂쿶쇖틋뚻쫎벱뗤샄돤ꆡඣഊ †켠돴햿샻쇭틋킻쮩킼ꏷ좬못컳뗊ꏀꆺ좰컎뗱뻄쳟쓥죚쫝쫇쎲ꎴ떬뗘퓣쓚ꏄ킬틨캪쏒퟇쫶쎲ꆴꆣනഊ †퐠뿆탉쏄쾻쳫뺫ꏃ뺬믍듘뗰ꏀꆺ좰컎뗱쓄죚뫝볜

  68. 욻ꆬඣഊ †선죹훋펻쇃틋햻ꏐ뺬뷍벫쪸쎮짻웏캷췤쟵쯡믉췷ꏋ쪬쫕훖몮ꏳ재쇠돺튤틷즻쇹뾢믌뗘솽볋좸뗋쳄쓥ꇚඣഊ †ꎲඡഊ †옠쯟퇒즪햫뒽ꎬ뎬힯쇅죹뿋쯬뗙럄죉ꎥ 数据 났쏙짻뗱펶뺪펫떢ퟜꏓ풬듚춬횷즮쳏쇸쿋샂ꆴඣഊ †퐠뿚훕뷐돡틉떻쇀첢믥떯헄럳ꎨ톬튪뗻춶쪬뎱쟶훊벮ꏤ랬뎢틶즻엹몭ꏰ쮬폹뗐진욱쇸뎷틉욻ꆬඣഊ †선쏹컻믤퓊헚훳탐ퟐꏟ뚬쫙뢱뗐튽릻뻉듞뗳퇄특룖ꏐ좬쫄쯇쏻틇즻짺좱컋쫞ꏽ뒬뿋틌뢲뗐튽햻탳별ꇂඣഊ †똠쟸헒냢쏙짻뗱펶떪ퟜꏓ힬돩뗉헄탳ꏍ쮬뫆퇵힭쓅횳쳖뗬럘풨훲솮ꎦ붬솫죹뗋쫄솵펦즲짺뗺퇄ힹ송틋뎻ꇯඣഊ †선죹퓋헚훳뫐돡훥ힱꎲ뾬닖뗀쇄솦벿몸컵죞쓋뗜ꎵ떬좫릴틮뗬럄쾢ꏖ쎬쏷쫷틇춻훹잱랰쾽ꏲ좬튴횻풱퓚떭ퟘ좪ꎦ쪬횼컕럞춨

  69. 中国方面,如今正在加大黄金储备,为未来挑战美国在全球贸易和金融中的主导地位做准备.

  70. 헺짛ꏈ햬럹쿦뛠ꏔ놬웈ힴ룅ퟷ쟔뒿뗳웄쫸뫆틍쿬ꇳඣഊ †유짅뗏쇄좽췋톬쏹펻쿐ퟐ 癫痫吃药多久可以治愈 ꏅ뢬ퟷ럔뿉뗬뛄맔ꎥ좬삭햴췆ꏹ벬헻닐헰ꇐඣഊ †켠돴펿탐튩췢ꏢ햬쟢쫠맩힫뗓헄럆욨컄뺪쎫ꏮ뚬쟸듒ퟸ틅릻뫉죆햻웽ꏸ펬짫뫭뗳쟄훠틱쾣뫠폴ꎦ붬탸ퟐ믅능ꆹඣഊ †젠쫴닇쒻돃뗶헣뇦쪾ꏂ튬쪻낱믫ꏡ뮬햹쓦헃쟢쫠맩힫쏓낻러ꆨඣഊ †눠릻쿽퓖믚쎹떻놽슩쪶솵떦쫄몱ꏲ햬뗢럘톽뻛몦뛜ꏠ쾬돴ힿ튢뗢ꎽ쎬퓷돂뗇룄쫟틖뻑잭쟄샄솴ꇋඣഊ †눠횻떪쫀쎲풴튭ꏲ쎬펻돐쫶ꏖ힬훨벹햤횽뗐쇄좽ꇋඣഊ †댠듽훋춮ꏢ뮬펹뫐탃떩쟀뒿뗳웄쿸ꎢ햬퓽닚춻떣룄샏ꎴ욬훤폐뫐탃즩짭뛏풼뿚닖뗀뫄죆횻욮ꏸ킬컞벪욫룤짟ꇮඣഊ †툠뾻뺴훍떪ꏀ쪬쳇룬쪮퓩ힺ볔떺룄쫟ꇖඣഊ †쬠틹쿔돴늿벻ꎱ뺬뗍쪱엇뛣

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