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Lock-Free Red-Black Trees Using CAS
Lock-Free Red-Black Trees Using CAS

Lecture6MRM
Lecture6MRM

... disappearing obstacle meet. ...
Lecture 12: Heaps, Priority Queues, Heapsort, Greedy Algorithms
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... • One class should inherit from another if it “is” a special type of the other. • For example, the Dealer is a Player. He’s playing in the game and has all of the choices a Player has. – Except that he uses an algorithm to decide his move, which is why takeTurn() was overridden. ...
Kεφάλαιο 8
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TREE DATA STRUCTURES FOR GRAPHICS AND IMAGE
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... Write an algorithm to delete a node from a singly linked list at any position. Write an algorithm for deletion of node at any position from doubly linked list with example. 7. Write an algorithm to insert an element into unsorted linked list. 8. Write algorithm to insert an element in sorted linked ...
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IOSR Journal of Computer Engineering (IOSR-JCE)

... implementing the MST but on the cost of search efficiency. On the contrary, a very large value of Z will improve the search complexity but on the cost of extra space requirement. Many other data structures like 2-3 trees[3], B trees[3], AVL trees[3], m-way search trees[3] are used for efficient sear ...
Concurrency and Recovery in Generalized Search Trees
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... parallel, their interactions may be interleaved in a way that leads to incorrect results. An example for a B-tree is shown in Figure 1, where a node split changes the location of some keys, which causes a concurrently executing search operation to miss them. In order to avoid such situations, we app ...
Lecture 28 Non-blocking Algorithms
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... – If a second thread updates top in between read and write in push, data structure could be corrupted – With locks, can ensure that no other thread changes top while push is in progress – Without locks … ? ...
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... Need a double-rotation (first at 16, then at 5) In general, how and when? ...
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... from the root to some leaf node. If there are n search-key values in the file,  the path is no longer than log f/2(n) (worst ...
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Red–black tree

A red–black tree is a binary search tree with an extra bit of data per node, its color, which can be either red or black. The extra bit of storage ensures an approximately balanced tree by constraining how nodes are colored from any path from the root to the leaf. Thus, it is a data structure which is a type of self-balancing binary search tree.Balance is preserved by painting each node of the tree with one of two colors (typically called 'red' and 'black') in a way that satisfies certain properties, which collectively constrain how unbalanced the tree can become in the worst case. When the tree is modified, the new tree is subsequently rearranged and repainted to restore the coloring properties. The properties are designed in such a way that this rearranging and recoloring can be performed efficiently.The balancing of the tree is not perfect but it is good enough to allow it to guarantee searching in O(log n) time, where n is the total number of elements in the tree. The insertion and deletion operations, along with the tree rearrangement and recoloring, are also performed in O(log n) time.Tracking the color of each node requires only 1 bit of information per node because there are only two colors. The tree does not contain any other data specific to its being a red–black tree so its memory footprint is almost identical to a classic (uncolored) binary search tree. In many cases the additional bit of information can be stored at no additional memory cost.
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