Silly question maybe - but, what would be faster:
-Deleting an item from a linked list every time an item is gone (whatever that item might be)
-Just marking the record as dead and overwriting it after x amount of time or conditions.
Would I not use less cpu time by avoiding all the removing and inserting rather than just overwriting.
Only way to find out is to use a profiler.
Keep in mind that there are other factors which are very important that you didn't specify in your question. For example, it makes a big difference if you know which element to delete, i.e. you have the pointer to it ready, or if you have to iterate over the linked list to find it. In that case, a vector could actually be several times faster than any linked list (depending on the number of elements) due to caching.
Overwriting is going to be faster. If deleting involves freeing allocated memory from the heap, then overwriting will definitely be faster. Memory allocation is relatively slow. Plus, over time your list memory would get fragmented which might cause cache misses. Even freeing memory that goes back into a memory manager of some sort would be slower since you have the overhead of managing the nodes of the list.
I have done some tests to investigate the relation between heap allocations and hardware cache behaviour. Empirical results are enlightening but also likely to be misleading, especially between different platforms and complex/indeterministic use cases.
There are two scenarios I am interested in: bulk allocation (to implement a custom memory pool) or consequent allocations (trusting the os).
Below are two example allocation tests in C++
//Consequent allocations
for(auto i = 1000000000; i > 0; i--)
int *ptr = new int(0);
store_ptr_in_some_container(ptr);
//////////////////////////////////////
//Bulk allocation
int *ptr = new int[1000000000];
distribute_indices_to_owners(ptr, 1000000000);
My questions are these:
When I iterate over all of them for a read only operation, how will cache
memory in CPU will likely to partition itself?
Despite empirical results (visible performance boost by bulk
solution), what does happen when some other, relatively very small
bulk allocation overrides cache from previous allocations?
Is it reasonable to mix the two in order two avoid code bloat and maintain code readability?
Where does std::vector, std::list, std::map, std::set stand in these concepts?
A general purpose heap allocator has a difficult set of problems to solve. It needs to ensure that released memory can be recycled, must support arbitrarily sized allocations and strongly avoid heap fragmentation.
This will always include extra overhead for each allocation, book-keeping that the allocator needs. At a minimum it must store the size of the block so it can properly reclaim it when the allocation is released. And almost always an offset or pointer to the next block in a heap segment, allocation sizes are typically larger than requested to avoid fragmentation problems.
This overhead of course affects cache efficiency, you can't help getting it into the L1 cache when the element is small, even though you never use it. You have zero overhead for each array element when you allocate the array in one big gulp. And you have a hard guarantee that each element is adjacent in memory so iterating the array sequentially is going to be as fast as the memory sub-system can support.
Not the case for the general purpose allocator, with such very small allocations the overhead is likely to be 100 to 200%. And no guarantee for sequential access either when the program has been running for a while and array elements were reallocated. Notably an operation that your big array cannot support so be careful that you don't automatically assume that allocating giant arrays that cannot be released for a long time is necessarily better.
So yes, in this artificial scenario is very likely you'll be ahead with the big array.
Scratch std::list from that quoted list of collection classes, it has very poor cache efficiency as the next element is typically at an entirely random place in memory. std::vector is best, just an array under the hood. std::map is usually done with a red-black tree, as good as can reasonably be done but the access pattern you use matters of course. Same for std::set.
I have a server that throughout the course of 24 hours keeps adding new items to a set. Elements are not deleted over the 24 period, just new elements keep getting inserted.
Then at end of period the set is cleared, and new elements start getting added again for another 24 hours.
Do you think a fast pool allocator would be useful here as to reuse the memory and possibly help with fragmentation?
The set grows to around 1 million elements. Each element is about 1k.
It's highly unlikely …but you are of course free to test it in your program.
For a collection of that size and allocation pattern (more! more! more! + grow! grow! grow!), you should use an array of vectors. Just keep it in contiguous blocks and reserve() when they are created and you never need to reallocate/resize or waste space and bandwidth traversing lists. vector is going to be best for your memory layout with a collection that large. Not one big vector (which would take a long time to resize), but several vectors, each which represent chunks (ideal chunk size can vary by platform -- I'd start with 5MB each and measure from there). If you follow, you see there is no need to resize or reuse memory; just create an allocation every few minutes for the next N objects -- there is no need for high frequency/speed object allocation and recreation.
The thing about a pool allocator would suggest you want a lot of objects which have discontiguous allocations, lots of inserts and deletes like a list of big allocations -- this is bad for a few reasons. If you want to create an implementation which optimizes for contiguous allocation at this size, just aim for the blocks with vectors approach. Allocation and lookup will both be close to minimal. At that point, allocation times should be tiny (relative to the other work you do). Then you will also have nothing unusual or surprising about your allocation patterns. However, the fast pool allocator suggests you treat this collection as a list, which will have terrible performance for this problem.
Once you implement that block+vector approach, a better performance comparison (at that point) would be to compare boost's pool_allocator vs std::allocator. Of course, you could test all three, but memory fragmentation is likely going to be reduced far more by that block of vectors approach, if you implement it correctly. Reference:
If you are seriously concerned about performance, use fast_pool_allocator when dealing with containers such as std::list, and use pool_allocator when dealing with containers such as std::vector.
I was reading up on the two different ways of implementing a stack: linked list and dynamic arrays. The main advantage of a linked list over a dynamic array was that the linked list did not have to be resized while a dynamic array had to be resized if too many elements were inserted hence wasting alot of time and memory.
That got me wondering if this is true for C++ (as there is a vector class which automatically resizes whenever new elements are inserted)?
It's difficult to compare the two, because the patterns of their memory usage are quite different.
Vector resizing
A vector resizes itself dynamically as needed. It does that by allocating a new chunk of memory, moving (or copying) data from the old chunk to the new chunk, the releasing the old one. In a typical case, the new chunk is 1.5x the size of the old (contrary to popular belief, 2x seems to be quite unusual in practice). That means for a short time while reallocating, it needs memory equal to roughly 2.5x as much as the data you're actually storing. The rest of the time, the "chunk" that's in use is a minimum of 2/3rds full, and a maximum of completely full. If all sizes are equally likely, we can expect it to average about 5/6ths full. Looking at it from the other direction, we can expect about 1/6th, or about 17% of the space to be "wasted" at any given time.
When we do resize by a constant factor like that (rather than, for example, always adding a specific size of chunk, such as growing in 4Kb increments) we get what's called amortized constant time addition. In other words, as the array grows, resizing happens exponentially less often. The average number of times items in the array have been copied tends to a constant (usually around 3, but depends on the growth factor you use).
linked list allocations
Using a linked list, the situation is rather different. We never see resizing, so we don't see extra time or memory usage for some insertions. At the same time, we do see extra time and memory used essentially all the time. In particular, each node in the linked list needs to contain a pointer to the next node. Depending on the size of the data in the node compared to the size of a pointer, this can lead to significant overhead. For example, let's assume you need a stack of ints. In a typical case where an int is the same size as a pointer, that's going to mean 50% overhead -- all the time. It's increasingly common for a pointer to be larger than an int; twice the size is fairly common (64-bit pointer, 32-bit int). In such a case, you have ~67% overhead -- i.e., obviously enough, each node devoting twice as much space to the pointer as the data being stored.
Unfortunately, that's often just the tip of the iceberg. In a typical linked list, each node is dynamically allocated individually. At least if you're storing small data items (such as int) the memory allocated for a node may be (usually will be) even larger than the amount you actually request. So -- you ask for 12 bytes of memory to hold an int and a pointer -- but the chunk of memory you get is likely to be rounded up to 16 or 32 bytes instead. Now you're looking at overhead of at least 75% and quite possibly ~88%.
As far as speed goes, the situation is rather similar: allocating and freeing memory dynamically is often quite slow. The heap manager typically has blocks of free memory, and has to spend time searching through them to find the block that's most suited to the size you're asking for. Then it (typically) has to split that block into two pieces, one to satisfy your allocation, and another of the remaining memory it can use to satisfy other allocations. Likewise, when you free memory, it typically goes back to that same list of free blocks and checks whether there's an adjoining block of memory already free, so it can join the two back together.
Allocating and managing lots of blocks of memory is expensive.
cache usage
Finally, with recent processors we run into another important factor: cache usage. In the case of a vector, we have all the data right next to each other. Then, after the end of the part of the vector that's in use, we have some empty memory. This leads to excellent cache usage -- the data we're using gets cached; the data we're not using has little or no effect on the cache at all.
With a linked list, the pointers (and probable overhead in each node) are distributed throughout our list. I.e., each piece of data we care about has, right next to it, the overhead of the pointer, and the empty space allocated to the node that we're not using. In short, the effective size of the cache is reduced by about the same factor as the overall overhead of each node in the list -- i.e., we might easily see only 1/8th of the cache storing the date we care about, and 7/8ths devoted to storing pointers and/or pure garbage.
Summary
A linked list can work well when you have a relatively small number of nodes, each of which is individually quite large. If (as is more typical for a stack) you're dealing with a relatively large number of items, each of which is individually quite small, you're much less likely to see a savings in time or memory usage. Quite the contrary, for such cases, a linked list is much more likely to basically waste a great deal of both time and memory.
Yes, what you say is true for C++. For this reason, the default container inside std::stack, which is the standard stack class in C++, is neither a vector nor a linked list, but a double ended queue (a deque). This has nearly all the advantages of a vector, but it resizes much better.
Basically, an std::deque is a linked list of arrays of sorts internally. This way, when it needs to resize, it just adds another array.
First, the performance trade-offs between linked-lists and dynamic arrays are a lot more subtle than that.
The vector class in C++ is, by requirement, implemented as a "dynamic array", meaning that it must have an amortized-constant cost for inserting elements into it. How this is done is usually by increasing the "capacity" of the array in a geometric manner, that is, you double the capacity whenever you run out (or come close to running out). In the end, this means that a reallocation operation (allocating a new chunk of memory and copying the current content into it) is only going to happen on a few occasions. In practice, this means that the overhead for the reallocations only shows up on performance graphs as little spikes at logarithmic intervals. This is what it means to have "amortized-constant" cost, because once you neglect those little spikes, the cost of the insert operations is essentially constant (and trivial, in this case).
In a linked-list implementation, you don't have the overhead of reallocations, however, you do have the overhead of allocating each new element on freestore (dynamic memory). So, the overhead is a bit more regular (not spiked, which can be needed sometimes), but could be more significant than using a dynamic array, especially if the elements are rather inexpensive to copy (small in size, and simple object). In my opinion, linked-lists are only recommended for objects that are really expensive to copy (or move). But at the end of the day, this is something you need to test in any given situation.
Finally, it is important to point out that locality of reference is often the determining factor for any application that makes extensive use and traversal of the elements. When using a dynamic array, the elements are packed together in memory one after the other and doing an in-order traversal is very efficient as the CPU can preemptively cache the memory ahead of the reading / writing operations. In a vanilla linked-list implementation, the jumps from one element to the next generally involves a rather erratic jumps between wildly different memory locations, which effectively disables this "pre-fetching" behavior. So, unless the individual elements of the list are very big and operations on them are typically very long to execute, this lack of pre-fetching when using a linked-list will be the dominant performance problem.
As you can guess, I rarely use a linked-list (std::list), as the number of advantageous applications are few and far between. Very often, for large and expensive-to-copy objects, it is often preferable to simply use a vector of pointers (you get basically the same performance advantages (and disadvantages) as a linked list, but with less memory usage (for linking pointers) and you get random-access capabilities if you need it).
The main case that I can think of, where a linked-list wins over a dynamic array (or a segmented dynamic array like std::deque) is when you need to frequently insert elements in the middle (not at either ends). However, such situations usually arise when you are keeping a sorted (or ordered, in some way) set of elements, in which case, you would use a tree structure to store the elements (e.g., a binary search tree (BST)), not a linked-list. And often, such trees store their nodes (elements) using a semi-contiguous memory layout (e.g., a breadth-first layout) within a dynamic array or segmented dynamic array (e.g., a cache-oblivious dynamic array).
Yes, it's true for C++ or any other language. Dynamic array is a concept. The fact that C++ has vector doesn't change the theory. The vector in C++ actually does the resizing internally so this task isn't the developers' responsibility. The actual cost doesn't magically disappear when using vector, it's simply offloaded to the standard library implementation.
std::vector is implemented using a dynamic array, whereas std::list is implemented as a linked list. There are trade-offs for using both data structures. Pick the one that best suits your needs.
As you indicated, a dynamic array can take a larger amount of time adding an item if it gets full, as it has to expand itself. However, it is faster to access since all of its members are grouped together in memory. This tight grouping also usually makes it more cache-friendly.
Linked lists don't need to resize ever, but traversing them takes longer as the CPU must jump around in memory.
That got me wondering if this is true for c++ as there is a vector class which automatically resizes whenever new elements are inserted.
Yes, it still holds, because a vector resize is a potentially expensive operation. Internally, if the pre-allocated size for the vector is reached and you attempt to add new elements, a new allocation takes place and the old data is moved to the new memory location.
From the C++ documentation:
vector::push_back - Add element at the end
Adds a new element at the end of the vector, after its current last element. The content of val is copied (or moved) to the new element.
This effectively increases the container size by one, which causes an automatic reallocation of the allocated storage space if -and only if- the new vector size surpasses the current vector capacity.
http://channel9.msdn.com/Events/GoingNative/GoingNative-2012/Keynote-Bjarne-Stroustrup-Cpp11-Style
Skip to 44:40. You should prefer std::vector whenever possible over a std::list, as explained in the video, by Bjarne himself. Since std::vector stores all of it's elements next to each other, in memory, and because of that it will have the advantage of being cached in memory. And this is true for adding and removing elements from std::vector and also searching. He states that std::list is 50-100x slower than a std::vector.
If you really want a stack, you should really use std::stack instead of making your own.
std::realloc is dangerous in c++ if the malloc'd memory contains non-pod types. It seems the only problem is that std::realloc wont call the type destructors if it cannot grow the memory in situ.
A trivial work around would be a try_realloc function. Instead of malloc'ing new memory if it cannot be grown in situ, it would simply return false. In which case new memory could be allocated, the objects copied (or moved) to the new memory, and finally the old memory freed.
This seems supremely useful. std::vector could make great use of this, possibly avoiding all copies/reallocations.
preemptive flame retardant: Technically, that is same Big-O performance, but if vector growth is a bottle neck in your application a x2 speed up is nice even if the Big-O remains unchanged.
BUT, I cannot find any c api that works like a try_realloc.
Am I missing something? Is try_realloc not as useful as I imagine? Is there some hidden bug that makes try_realloc unusable?
Better yet, Is there some less documented API that performs like try_realloc?
NOTE: I'm obviously, in library/platform specific code here. I'm not worried as try_realloc is inherently an optimization.
Update:
Following Steve Jessops comment's on whether vector would be more efficient using realloc I wrote up a proof of concept to test. The realloc-vector simulates a vector's growth pattern but has the option to realloc instead. I ran the program up to a million elements in the vector.
For comparison a vector must allocate 19 times while growing to a million elements.
The results, if the realloc-vector is the only thing using the heap the results are awesome, 3-4 allocation while growing to the size of million bytes.
If the realloc-vector is used alongside a vector that grows at 66% the speed of the realloc-vector The results are less promising, allocating 8-10 times during growth.
Finally, if the realloc-vector is used alongside a vector that grows at the same rate, the realloc-vector allocates 17-18 times. Barely saving one allocation over the standard vector behavior.
I don't doubt that a hacker could game allocation sizes to improve the savings, but I agree with Steve that the tremendous effort to write and maintain such an allocator isn't work the gain.
vector generally grows in large increments. You can't do that repeatedly without relocating, unless you carefully arrange things so that there's a large extent of free addresses just above the internal buffer of the vector (which in effect requires assigning whole pages, because obviously you can't have other allocations later on the same page).
So I think that in order to get a really good optimization here, you need more than a "trivial workaround" that does a cheap reallocation if possible - you have to somehow do some preparation to make it possible, and that preparation costs you address space. If you only do it for certain vectors, ones that indicate they're going to become big, then it's fairly pointless, because they can indicate with reserve() that they're going to become big. You can only do it automatically for all vectors if you have a vast address space, so that you can "waste" a big chunk of it on every vector.
As I understand it, the reason that the Allocator concept has no reallocation function is to keep it simple. If std::allocator had a try_realloc function, then either every Allocator would have to have one (which in most cases couldn't be implemented, and would just have to return false always), or else every standard container would have to be specialized for std::allocator to take advantage of it. Neither option is a great Allocator interface, although I suppose it wouldn't be a huge effort for implementers of almost all Allocator classes just to add a do-nothing try_realloc function.
If vector is slow due to re-allocation, deque might be a good replacement.
You could implement something like the try_realloc you proposed, using mmap with MAP_ANONYMOUS and MAP_FIXED and mremap with MREMAP_FIXED.
Edit: just noticed that the man page for mremap even says:
mremap() uses the Linux page table
scheme. mremap() changes the
mapping between
virtual addresses and memory pages. This can be used to implement
a very efficient
realloc(3).
realloc in C is hardly more than a convenience function; it has very little benefit for performance/reducing copies. The main exception I can think of is code that allocates a big array then reduces the size once the size needed is known - but even this might require moving data on some malloc implementations (ones which segregate blocks strictly by size) so I consider this usage of realloc really bad practice.
As long as you don't constantly reallocate your array every time you add an element, but instead grow the array exponentially (e.g. by 25%, 50%, or 100%) whenever you run out of space, just manually allocating new memory, copying, and freeing the old will yield roughly the same (and identical, in case of memory fragmentation) performance to using realloc. This is surely the approach that C++ STL implementations use, so I think your whole concern is unfounded.
Edit: The one (rare but not unheard-of) case where realloc is actually useful is for giant blocks on systems with virtual memory, where the C library interacts with the kernel to relocate whole pages to new addresses. The reason I say this is rare is because you need to be dealing with very big blocks (at least several hundred kB) before most implementations will even enter the realm of dealing with page-granularity allocation, and probably much larger (several MB maybe) before entering and exiting kernelspace to rearrange virtual memory is cheaper than simply doing the copy. Of course try_realloc would not be useful here, since the whole benefit comes from actually doing the move inexpensively.