Join-based tree algorithms

In computer science, join-based tree algorithms are a class of algorithms for self-balancing binary search trees. This framework aims at designing highly-parallelized algorithms for various balanced binary search trees. The algorithmic framework is based on a single operation join. ^[1] Under this framework, the join operation captures all balancing criteria of different balancing schemes, and all other functions join have generic implementation across different balancing schemes. The join-based algorithms can be applied to at least four balancing schemes: AVL trees, red–black trees, weight-balanced trees and treaps.

The join${\displaystyle (L,k,R)}$ operation takes as input two binary balanced trees ${\displaystyle L}$ and ${\displaystyle R}$ of the same balancing scheme, and a key ${\displaystyle k}$, and outputs a new balanced binary tree ${\displaystyle t}$ whose in-order traversal is the in-order traversal of ${\displaystyle L}$, then ${\displaystyle k}$ then the in-order traversal of ${\displaystyle R}$. In particular, if the trees are search trees, which means that the in-order of the trees maintain a total ordering on keys, it must satisfy the condition that all keys in ${\displaystyle L}$ are smaller than ${\displaystyle k}$ and all keys in ${\displaystyle R}$ are greater than ${\displaystyle k}$.

History

The join operation was first defined by Tarjan ^[2] on red–black trees, which runs in worst-case logarithmic time. Later Sleator and Tarjan ^[3] described a join algorithm for splay trees which runs in amortized logarithmic time. Later Adams ^[4] extended join to weight-balanced trees and used it for fast set–set functions including union, intersection and set difference. In 1998, Blelloch and Reid-Miller extended join on treaps, and proved the bound of the set functions to be ${\displaystyle O(m\log(1+{\tfrac {n}{m}}))}$ for two trees of size ${\displaystyle m}$ and ${\displaystyle n(\geq m)}$, which is optimal in the comparison model. They also brought up parallelism in Adams' algorithm by using a divide-and-conquer scheme. In 2016, Blelloch et al. formally proposed the join-based algorithms, and formalized the join algorithm for four different balancing schemes: AVL trees, red–black trees, weight-balanced trees and treaps. In the same work they proved that Adams' algorithms on union, intersection and difference are work-optimal on all the four balancing schemes.

Join algorithms

The function join${\displaystyle (t_{1},k,t_{2})}$ considers rebalancing the tree, and thus depends on the input balancing scheme. If the two trees are balanced, join simply creates a new node with left subtree t₁, root k and right subtree t₂. Suppose that t₁ is heavier (this "heavier" depends on the balancing scheme) than t₂ (the other case is symmetric). Join follows the right spine of t₁ until a node c which is balanced with t₂. At this point a new node with left child c, root k and right child t₂ is created to replace c. The new node may invalidate the balancing invariant. This can be fixed with rotations.

The following is the join algorithms on different balancing schemes.

The join algorithm for AVL trees:

function joinRightAVL(TL, k, TR)     (l, k', c) := expose(TL)     if h(c) ≤ h(TR) + 1         T' := Node(c, k, TR)         if h(T') ≤ h(l) + 1             return Node(l, k', T')         elsereturn rotateLeft(Node(l, k', rotateRight(T')))     else          T' := joinRightAVL(c, k, TR)         T := Node(l, k', T')         if h(T') ≤ h(l) + 1             return T         elsereturn rotateLeft(T)  function joinLeftAVL(TL, k, TR)     /* symmetric to joinRightAVL */  function join(TL, k, TR)     if h(TL) > h(TR) + 1         return joinRightAVL(TL, k, TR)     else if h(TR) > h(TL) + 1         return joinLeftAVL(TL, k, TR)     elsereturn Node(TL, k, TR)

Where:

• ${\displaystyle h(v)}$ is the height of node ${\displaystyle v}$.
• ${\displaystyle {\text{expose}}(v)}$ extracts the left child ${\displaystyle l}$, key ${\displaystyle k}$, and right child ${\displaystyle r}$ of node ${\displaystyle v}$ into a tuple ${\displaystyle (l,k,r)}$.
• ${\displaystyle {\text{Node}}(l,k,r)}$ creates a node with left child ${\displaystyle l}$, key ${\displaystyle k}$, and right child ${\displaystyle r}$.

The join algorithm for red–black trees:

function joinRightRB(TL, k, TR)     if r(TL) = ⌊r(TR)/2⌋ × 2         return Node(TL, ⟨k, red⟩, TR)     else          (L', ⟨k', c'⟩, R') := expose(TL)         T' := Node(L', ⟨k', c'⟩, joinRightRB(R', k, TR))         if c' = black and T'.right.color = T'.right.right.color = red             T'.right.right.color := black             return rotateLeft(T')         elsereturn T'  function joinLeftRB(TL, k, TR)     /* symmetric to joinRightRB */  function join(TL, k, TR)     if ⌊r(TL)/2⌋ > ⌊r(TR)/2⌋ × 2         T' := joinRightRB(TL, k, TR)         if (T'.color = red) and (T'.right.color = red)             T'.color := black         return T'     else if ⌊r(TR)/2⌋ > ⌊r(TL)/2⌋ × 2         /* symmetric */     else if TL.color = black and TR = black         return Node(TL, ⟨k, red⟩, TR)     elsereturn Node(TL, ⟨k, black⟩, TR)

Where:

• ${\displaystyle r(v)}$ of a node ${\displaystyle v}$ means twice the black height of a black node, and the twice the black height of a red node.
• ${\displaystyle {\text{expose}}(v)}$ extracts the left child ${\displaystyle l}$, key ${\displaystyle k}$, color ${\displaystyle c}$, and right child ${\displaystyle r}$ of node ${\displaystyle v}$ into a tuple ${\displaystyle (l,\langle k,c\rangle ,r)}$.
• ${\displaystyle {\text{Node}}(l,\langle k,c\rangle ,r)}$ creates a node with left child ${\displaystyle l}$, key ${\displaystyle k}$, color ${\displaystyle c}$, and right child ${\displaystyle r}$.

The join algorithm for weight-balanced trees:

function joinRightWB(TL, k, TR)     (l, k', c) := expose(TL)     if w(TL) =α w(TR)         return Node(TL, k, TR)     else          T' := joinRightWB(c, k, TR)         (l1, k1, r1) := expose(T')         if w(l) =α w(T')             return Node(l, k', T')         else if w(l) =α w(l1) and w(l)+w(l1) =α w(r1)             return rotateLeft(Node(l, k', T'))         elsereturn rotateLeft(Node(l, k', rotateRight(T'))  function joinLeftWB(TL, k, TR)     /* symmetric to joinRightWB */  function join(TL, k, TR)     if w(TL) >α w(TR)         return joinRightWB(TL, k, TR)     else if w(TR) >α w(TL)         return joinLeftWB(TL, k, TR)     elsereturn Node(TL, k, TR)

Where:

• ${\displaystyle w(v)}$ is the weight of node ${\displaystyle v}$.
• ${\displaystyle w_{1}=_{\alpha }w_{2}}$ means weights ${\displaystyle w_{1}}$ and ${\displaystyle w_{2}}$ are α-weight-balanced.
• ${\displaystyle w_{1}>_{\alpha }w_{2}}$ means weight ${\displaystyle w_{1}}$ is heavier than weight ${\displaystyle w_{2}}$ with respect to the α-weight-balance.
• ${\displaystyle {\text{expose}}(v)}$ extracts the left child ${\displaystyle l}$, key ${\displaystyle k}$, and right child ${\displaystyle r}$ of node ${\displaystyle v}$ into a tuple ${\displaystyle (l,k,r)}$.
• ${\displaystyle {\text{Node}}(l,k,r)}$ creates a node with left child ${\displaystyle l}$, key ${\displaystyle k}$ and right child ${\displaystyle r}$.

Join-based algorithms

In the following, ${\displaystyle {\text{expose}}(v)}$ extracts the left child ${\displaystyle l}$, key ${\displaystyle k}$, and right child ${\displaystyle r}$ of node ${\displaystyle v}$ into a tuple ${\displaystyle (l,k,r)}$. ${\displaystyle {\text{Node}}(l,k,r)}$ creates a node with left child ${\displaystyle l}$, key ${\displaystyle k}$ and right child ${\displaystyle r}$. "${\displaystyle s_{1}||s_{2}}$" means that two statements ${\displaystyle s_{1}}$ and ${\displaystyle s_{2}}$ can run in parallel.

Split

To split a tree into two trees, those smaller than key x, and those larger than key x, we first draw a path from the root by inserting x into the tree. After this insertion, all values less than x will be found on the left of the path, and all values greater than x will be found on the right. By applying Join, all the subtrees on the left side are merged bottom-up using keys on the path as intermediate nodes from bottom to top to form the left tree, and the right part is asymmetric. For some applications, Split also returns a boolean value denoting if x appears in the tree. The cost of Split is ${\displaystyle O(\log n)}$, order of the height of the tree.

The split algorithm is as follows:

function split(T, k)     if (T = nil)         return (nil, false, nil)     else         (L, m, R) := expose(T)         if k < m             (L', b, R') := split(L, k)             return (L', b, join(R', m, R))         else if k > m             (L', b, R') := split(R, k)             return (join(L, m, L'), b, R'))         elsereturn (L, true, R)

Join2

This function is defined similarly as join but without the middle key. It first splits out the last key ${\displaystyle k}$ of the left tree, and then join the rest part of the left tree with the right tree with ${\displaystyle k}$. The algorithm is as follows:

function splitLast(T)     (L, k, R) := expose(T)     if R = nil         return (L, k)     else         (T', k') := splitLast(R)         return (join(L, k, T'), k')  function join2(L, R)     if L = nil         return R     else         (L', k) := splitLast(L)         return join(L', k, R)

The cost is ${\displaystyle O(\log n)}$ for a tree of size ${\displaystyle n}$.

Insert and delete

The insertion and deletion algorithms, when making use of join can be independent of balancing schemes. For an insertion, the algorithm compares the key to be inserted with the key in the root, inserts it to the left/right subtree if the key is smaller/greater than the key in the root, and joins the two subtrees back with the root. A deletion compares the key to be deleted with the key in the root. If they are equal, return join2 on the two subtrees. Otherwise, delete the key from the corresponding subtree, and join the two subtrees back with the root. The algorithms are as follows:

function insert(T, k)     if T = nil         return Node(nil, k, nil)     else         (L, k', R) := expose(T)         if k < k'             return join(insert(L,k), k', R)         else if k > k'             return join(L, k', insert(R, k))         elsereturn T  function delete(T, k)     if T = nil         return nil     else         (L, k', R) := expose(T)         if k < k'             return join(delete(L, k), k', R)         else if k > k'             return join(L, k', delete(R, k))         elsereturn join2(L, R)

Both insertion and deletion requires ${\displaystyle O(\log n)}$ time if ${\displaystyle |T|=n}$.

Set–set functions

Several set operations have been defined on weight-balanced trees: union, intersection and set difference. The union of two weight-balanced trees t₁ and t₂ representing sets A and B, is a tree t that represents A ∪ B. The following recursive function computes this union:

function union(t1, t2)     if t1 = nil         return t2else if t2 = nil         return t1else         (l1, k1, r1) := expose(t1)         (t<, b, t>) := split(t2, k1)         l' := union(l1, t<) || r' := union(r1, t>)         return join(l', k1, r')

Similarly, the algorithms of intersection and set-difference are as follows:

function intersection(t1, t2)     if t1 = nil or t2 = nil         return nil     else         (l1, k1, r1) := expose(t1)         (t<, b, t>) = split(t2, k1)         l' := intersection(l1, t<) || r' := intersection(r1, t>)         if b             return join(l', k1, r')         elsereturn join2(l', r')  function difference(t1, t2)     if t1 = nil          return nil     else if t2 = nil          return t1else         (l1, k1, r1) := expose(t1)         (t<, b, t>) := split(t2, k1)         l' = difference(l1, t<) || r' = difference(r1, t>)         if b             return join2(l', r')         elsereturn join(l', k1, r')

The complexity of each of union, intersection and difference is ${\displaystyle O\left(m\log \left({\tfrac {n}{m}}+1\right)\right)}$ for two weight-balanced trees of sizes ${\displaystyle m}$ and ${\displaystyle n(\geq m)}$. This complexity is optimal in terms of the number of comparisons. More importantly, since the recursive calls to union, intersection or difference are independent of each other, they can be executed in parallel with a parallel depth ${\displaystyle O(\log m\log n)}$. ^[1] When ${\displaystyle m=1}$, the join-based implementation applies the same computation as in a single-element insertion or deletion if the root of the larger tree is used to split the smaller tree.

Build

The algorithm for building a tree can make use of the union algorithm, and use the divide-and-conquer scheme:

function build(A[], n)     if n = 0         return nil     else if n = 1         return Node(nil, A[0], nil)     else         l' := build(A, n/2) || r' := (A+n/2, n-n/2)         return union(L, R)

This algorithm costs ${\displaystyle O(n\log n)}$ work and has ${\displaystyle O(\log ^{3}n)}$ depth. A more-efficient algorithm makes use of a parallel sorting algorithm.

function buildSorted(A[], n)     if n = 0         return nil     else if n = 1         return Node(nil, A[0], nil)     else         l' := build(A, n/2) || r' := (A+n/2+1, n-n/2-1)         return join(l', A[n/2], r')  function build(A[], n)     A' := sort(A, n)     return buildSorted(A, n)

This algorithm costs ${\displaystyle O(n\log n)}$ work and has ${\displaystyle O(\log n)}$ depth assuming the sorting algorithm has ${\displaystyle O(n\log n)}$ work and ${\displaystyle O(\log n)}$ depth.

Filter

This function selects all entries in a tree satisfying a predicate ${\displaystyle p}$, and return a tree containing all selected entries. It recursively filters the two subtrees, and join them with the root if the root satisfies ${\displaystyle p}$, otherwise join2 the two subtrees.

function filter(T, p)     if T = nil         return nil     else         (l, k, r) := expose(T)         l' := filter(l, p) || r' := filter(r, p)         if p(k)             return join(l', k, r')         elsereturn join2(l', R)

This algorithm costs work ${\displaystyle O(n)}$ and depth ${\displaystyle O(\log ^{2}n)}$ on a tree of size ${\displaystyle n}$, assuming ${\displaystyle p}$ has constant cost.

Used in libraries

The join-based algorithms are applied to support interface for sets, maps, and augmented maps ^[5] in libraries such as Hackage, SML/NJ, and PAM. ^[5]

References

1. Blelloch, Guy E.; Ferizovic, Daniel; Sun, Yihan (2016), "Just Join for Parallel Ordered Sets", Symposium on Parallel Algorithms and Architectures, Proc. of 28th ACM Symp. Parallel Algorithms and Architectures (SPAA 2016), ACM, pp. 253–264, arXiv: 1602.02120 , doi:10.1145/2935764.2935768, ISBN   978-1-4503-4210-0
2. Tarjan, Robert Endre (1983), "Data structures and network algorithms", Data structures and network algorithms, Siam, pp. 45–56
3. Sleator, Daniel Dominic; Tarjan, Robert Endre (1985), "Self-adjusting binary search trees", Journal of the ACM, Siam
4. Adams, Stephen (1992), "Implementing sets efficiently in a functional language", Implementing sets efficiently in a functional language, Citeseer, CiteSeerX   10.1.1.501.8427 .
5. Blelloch, Guy E.; Ferizovic, Daniel; Sun, Yihan (2018), "PAM: parallel augmented maps", Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, ACM, pp. 290–304

External links

• PAM, the parallel augmented map library
• Hackage, Containers in Hackage