This repository contains some small example implementations of buddy allocators. They are designed to allocate physical memory, although they could be used for other types of allocation, such as the heap. Eventually, the best performing one will be merged into flower.

First, clone the repo. Then, cd into it and do cargo +nightly run to run all the demo allocators. By default, the block size is 4kib and the amount of blocks is 100 000, so this may take a while for the linked lists example. Don't worry, it won't actually allocate anything -- only mock memory blocks. Pass -h or --help to get help and view the usage. You can edit the source code to change min/max block sizes, etc. To run the unit tests, run cargo test. Unfortunately there are no cargo benchmarks yet, but I have benchmarked it rather unscientifically on my Windows machine.

I tested the algorithms by timing various implementations using the builtin reporting, allocating a gibibyte in 4kib blocks (with printing off) on my Windows machine. If you have any other benchmarks to add, please see Contributing.

(MSI CX61-2QF)

Note: The throughput is extrapolated from the time it took to allocate 1 GiB in 4kib blocks. For implementations that have complexity >O(log n) (such as the naive list based implementation), this will not be accurate -- the throughput will slow down as more blocks are allocated. This should be accurate for ones that have a complexity of O(log n) or less though.

This implementation keeps a list per order of block. It is generic over the typeof lists used. I decided to use two kinds of lists: vectors (Vec from std), and doubly linked lists (LinkedList, also from std). Linked lists are often prized for their predictable push time (no reallocation necessary for pushing), while vectors have better cache locality as the elements are allocated in a contiguous memory block. I used doubly linked lists because they are faster for indexing than singly linked lists, as they can iterate from the back or front depending on whether the index is closer to the beginning or end of the list. I decided to test both to see which would perform better overall.

The implementation is recursive. To allocate a free block of order k, it first searches for any free blocks in the list of order k blocks. It does not keep a free list. If none are found, it recurses by trying to allocating a block of order k + 1. Finally, if at no point were any free blocks found it gives up and panics. As soon as one is it splits it in half, removing the original block from it's order list and pushing the halves to the order list immediately lower. It then returns the order and index of the first block in its order list. You can find this algorithm in find_or_split.

A quick, un-scientific benchmark on my Windows machine says that it took around two minutes to allocate a full gibibyte (1024^3 bytes). I did notice split second pauses every now and again when it had to reallocate the entire vector to push an element.

A similar benchmark says that it took twenty-five minutes to allocate a full gibibyte. This is over twelve times slower than the same implementation with vectors. However, this implementation wasn't optimised for linked lists, so it is slightly unfair. Unlike the implementation with vectors, I did not notice any pauses, but allocation gradually got slower and slower.

We can conclude that although doubly linked lists in theory are faster at pushing than vectors are, they were still 12 times slower than vectors. This could be because the implementation was slightly in favour of vectors (lots of indexing), or because the vectors had a higher cache locality and therefore experienced less cache misses, while linked lists experience high cache misses as they have individually heap-allocated elements.

This implementation keeps one red-black tree (from intrusive_collections) for all blocks and a free list for each order. The free lists were implemented for std's Vec and intrusive_collections's SinglyLinkedList. I chose a singly linked list as there would have been no real benefit to double linking -- the only method that would have benefited (negligibly so) is FreeList::remove, but this is always called at most on the second element in this free list, so there is no real point in optimizing this. The red-black tree individually heap allocates each node, which makes the cache efficiency worse, but unlike std's BTreeSet/BTreeMap its search is O(log n), while std's uses a linear search, which is not O(log n) (you can read about this here). However, std's trees do not individually heap allocate nodes, so cache locality is better. I decided that although this was true, since a buddy allocator must deal with incredibly large numbers of blocks, it was more important to have a more efficient search algorithm.

The implementation is recursive. To allocate a free block of order k, it first searches for any free blocks in the free list list of order k blocks. If none are found, it recurses by trying to allocating a block of order k + 1. Finally, if at no point were any free blocks found it gives up and panics. As soon as one is it splits it in half, removing the original block from the tree and inserting the halves, pushing their addresses to the relevant free list. It then returns a cursor pointing to the first block. You can find this algorithm in find_or_split. At the outermost layer of recursion (the function that actually calls the recursive find_or_split function), the returned block is marked as used and removed from the free list.

Using vectors as free lists took ~0.3s to allocate a full GiB. This is ~0.2s faster than the linked lists as free lists version. This is probably due to vectors having better cache locality.

Using linked lists as free lists took ~0.5s to allocate a full GiB. See the Vectors as Free Lists section above.

This implementation was 400x faster than the naive list based implementation (at best, using vectors as free lists). This is probably due to red-black trees having O(log n) operations across the board, faster than the searches, inserts, and removes of vectors or linked lists.

This implementation is not strictly a bitmap, per se, but is a modification of a bitmap system. Essentially, each block in the tree stores the largest order (fully merged) somewhere underneath it. For instance, a tree which is all free with 4 orders looks like this:

       3
    2      2
 1   1   1   1
0 0 0 0 0 0 0 0

If we allocate one order 0 block, it looks like this (T is taken):

       2
    1      2
 0   1   1   1
T 0 0 0 0 0 0 0

It is implemented as a flattened array, where for a tree like

   1
 2   3

the representation is 1; 2; 3. This has the nice property that if we use indices beginning at 1 (i.e indices and not offsets), then the index of the left child of any given index is 2 * index, and the right child is simply 2 * index + 1. The parent is floor(index / 2). Because all of these operations work with 2s, we can use efficient bitshifting to execute them (index << 1, (index << 1) | 1, and index >> 1).

We can do a binary search to find the a block that is free of the desired order. First, we check if there are any blocks of the desired order free by checking the root block. If there are, we check if the left child has enough free. If it does, then we again check it's left child, etc. If a block's left child does not have enough blocks free, we simply use its right child. We know that the right child must then have enough free, or the root block is invalid.

This implementation was very fast. On my computer, it only took ~0.07s to allocate 1GiB. I have seen it perform up to 0.04s on my computer, though -- performance does fluctuate a bit. I assume that this is to do with CPU load.

This implementation does not have very good cache locality, as levels are stored far from eachother, so a parent block can be very far from its child. However, everything else is still very fast, so it is made up for. It is also O(log n), but practically it is so fast that this does not really matter. For reference: allocating 8GiB took 0.6s for me, but I have seen it perform much better at >150ms on @gegy1000's laptop.

If you have any thing to add (such as an edit to the readme or another implementation or benchmark) feel free to submit a pull request! You can also create an issue. If you just want to chat, feel free to ping me on the Rust Discord (Restioson#8323).