Detecting Parallelism in C Programs with Recursive Data Structures?
... that use recursive data structures: (1) function-call parallelism including parallel recursive calls on tree-like structures; (2) forall parallelism for loops traversing arrays of list/tree-like structures; and (3) foreach parallelism for loops traversing list/tree-like structures, which is similar ...
... that use recursive data structures: (1) function-call parallelism including parallel recursive calls on tree-like structures; (2) forall parallelism for loops traversing arrays of list/tree-like structures; and (3) foreach parallelism for loops traversing list/tree-like structures, which is similar ...
Dynamic Planar Convex Hull
... hull of S by |S| − 2 points, reporting the changes to the convex hull is in many applications not desirable. Instead of reporting the changes one maintains a data structure that allows queries for points on the convex hull. Typical examples are the extreme point in a given direction, the tangent(s) ...
... hull of S by |S| − 2 points, reporting the changes to the convex hull is in many applications not desirable. Instead of reporting the changes one maintains a data structure that allows queries for points on the convex hull. Typical examples are the extreme point in a given direction, the tangent(s) ...
Data structures for various distributions of data
... In the past years the computers have been increasing their processing power and storage capacity very rapidly. As the process is expected to continue in the future, it might seem that efficient algorithms and data structures become less important. However, the more one has, the more he wants. The de ...
... In the past years the computers have been increasing their processing power and storage capacity very rapidly. As the process is expected to continue in the future, it might seem that efficient algorithms and data structures become less important. However, the more one has, the more he wants. The de ...
The X-tree: An Index Structure for High
... For high-dimensional data sets, reducing the dimensionality is an obvious and important possibility for diminishing the dimensionality problem and should be performed whenever possible. In many cases, the data sets resulting from reducing the dimensionality will still have a quite large dimensional ...
... For high-dimensional data sets, reducing the dimensionality is an obvious and important possibility for diminishing the dimensionality problem and should be performed whenever possible. In many cases, the data sets resulting from reducing the dimensionality will still have a quite large dimensional ...
ON-LINE PLANARITY TESTING*
... Our main result is expressed by the following theorem. THEOREM 1.1. Let G be a planar graph that is dynamically updated by adding vertices and edges, and let n be the current number of vertices of G. There exists a data structure for the on-line planarity-testing p’oblem in G with the following perf ...
... Our main result is expressed by the following theorem. THEOREM 1.1. Let G be a planar graph that is dynamically updated by adding vertices and edges, and let n be the current number of vertices of G. There exists a data structure for the on-line planarity-testing p’oblem in G with the following perf ...
Represent the given sparse matrix using Linked List
... The stack is a common data structure for representing things that need to maintain in a particular order. Conceptually, a stack is simple: a data structure that allows adding and removing elements in a particular order. Every time an element is added, it goes on the top of the stack; the only elemen ...
... The stack is a common data structure for representing things that need to maintain in a particular order. Conceptually, a stack is simple: a data structure that allows adding and removing elements in a particular order. Every time an element is added, it goes on the top of the stack; the only elemen ...
Suffix Trees and their Applications
... Suffix Trees and Data Compression 7.1 Simple Data Compression . . . . . . . . . . . . . . . . . . . . . . . . . ...
... Suffix Trees and Data Compression 7.1 Simple Data Compression . . . . . . . . . . . . . . . . . . . . . . . . . ...
Indexing and Index Tuning
... Index pages can typically hold many more entries than leaf pages. Variable sized records and search keys mean different nodes will contain different numbers of entries. Even with fixed length fields, multiple records with the same search key value (duplicates) can lead to variable-sized data entr ...
... Index pages can typically hold many more entries than leaf pages. Variable sized records and search keys mean different nodes will contain different numbers of entries. Even with fixed length fields, multiple records with the same search key value (duplicates) can lead to variable-sized data entr ...
Functional data structures and algorithms - Milan Straka
... mentations of persistent structures, is to represent all versions using one shared data structure and identify the versions using a usually integral key, possibly accompanied by access pointers of the structure (i.e., a tree root or a list head) or other data. In this manner, sharing of parts of the ...
... mentations of persistent structures, is to represent all versions using one shared data structure and identify the versions using a usually integral key, possibly accompanied by access pointers of the structure (i.e., a tree root or a list head) or other data. In this manner, sharing of parts of the ...
Predecessor Data Structures - Algorithms for Massive Data Sets
... • The predecessor problem: Maintain a set S ⊆ U = {0, ..., u-1} under operations • predecessor(x): return the largest element in S that is ≤ x. • sucessor(x): return the smallest element in S that is ≥ x. • insert(x): set S = S ∪ {x} • delete(x): set S = S - {x} u-1 ...
... • The predecessor problem: Maintain a set S ⊆ U = {0, ..., u-1} under operations • predecessor(x): return the largest element in S that is ≤ x. • sucessor(x): return the smallest element in S that is ≥ x. • insert(x): set S = S ∪ {x} • delete(x): set S = S - {x} u-1 ...
Algorithms
... http://www.cs.princeton.edu/introcs/13loop/Hello.java http://www.cs.princeton.edu/introcs/13loop/Hello.class http://www.cs.princeton.edu/introcs/13loop/Hello.html ...
... http://www.cs.princeton.edu/introcs/13loop/Hello.java http://www.cs.princeton.edu/introcs/13loop/Hello.class http://www.cs.princeton.edu/introcs/13loop/Hello.html ...
BOXes: Efficient Maintenance of Order
... over the path-based schemes. First, these schemes maintain document order. Second, each label only requires O(log N ) bits, which is asymptotically minimum. Third, comparing numeric labels can be faster than prefix matching. Finally, ...
... over the path-based schemes. First, these schemes maintain document order. Second, each label only requires O(log N ) bits, which is asymptotically minimum. Third, comparing numeric labels can be faster than prefix matching. Finally, ...
Stratified B-trees and Versioned Dictionaries.
... arrays matching the filter. If multiple results are found, return the one written in the nearest ancestor to v – or in the lowest level if there is more than one such entry. In practice, the internal nodes of each B-tree can be held in memory so that this involves O(1) IOs per array. For a range que ...
... arrays matching the filter. If multiple results are found, return the one written in the nearest ancestor to v – or in the lowest level if there is more than one such entry. In practice, the internal nodes of each B-tree can be held in memory so that this involves O(1) IOs per array. For a range que ...
Chapter 15
... (depending on the details of the code), and even if it does, the error message may be quite strange – Look for a missing when a program that uses nodes
with type parameters gets a strange error message or
doesn't run correctly
...
... (depending on the details of the code), and even if it does, the error message may be quite strange – Look for a missing
Binary search tree
In computer science, binary search trees (BST), sometimes called ordered or sorted binary trees, are a particular type of containers: data structures that store ""items"" (such as numbers, names and etc.) in memory. They allow fast lookup, addition and removal of items, and can be used to implement either dynamic sets of items, or lookup tables that allow finding an item by its key (e.g., finding the phone number of a person by name).Binary search trees keep their keys in sorted order, so that lookup and other operations can use the principle of binary search: when looking for a key in a tree (or a place to insert a new key), they traverse the tree from root to leaf, making comparisons to keys stored in the nodes of the tree and deciding, based on the comparison, to continue searching in the left or right subtrees. On average, this means that each comparison allows the operations to skip about half of the tree, so that each lookup, insertion or deletion takes time proportional to the logarithm of the number of items stored in the tree. This is much better than the linear time required to find items by key in an (unsorted) array, but slower than the corresponding operations on hash tables.They are a special case of the more general B-tree with order equal to two.