MySQL Cluster NDB 7.3, MySQL Cluster NDB 7.4
... For legal information, see the Legal Notices. For help with using MySQL, please visit either the MySQL Forums or MySQL Mailing Lists, where you can discuss your issues with other MySQL users. For additional documentation on MySQL products, including translations of the documentation into other langu ...
... For legal information, see the Legal Notices. For help with using MySQL, please visit either the MySQL Forums or MySQL Mailing Lists, where you can discuss your issues with other MySQL users. For additional documentation on MySQL products, including translations of the documentation into other langu ...
Scotch and libScotch 5.1 User`s Guide - Gforge
... pieces of data, and edges reflect data dependencies. The mapping problem can then be addressed by assigning processor labels to the vertices of the graph, so that all processes assigned to some processor are loaded and run on it. In a SPMD context, this is equivalent to the distribution across proce ...
... pieces of data, and edges reflect data dependencies. The mapping problem can then be addressed by assigning processor labels to the vertices of the graph, so that all processes assigned to some processor are loaded and run on it. In a SPMD context, this is equivalent to the distribution across proce ...
Art of Multiprocessor Programming
... The importance of concurrency has not always been acknowledged. Here is a quote from a 1989 New York Times article on new operating systems for the IBM PC: Real concurrency–in which one program actually continues to function while you call up and use another–is more amazing but of small use to the a ...
... The importance of concurrency has not always been acknowledged. Here is a quote from a 1989 New York Times article on new operating systems for the IBM PC: Real concurrency–in which one program actually continues to function while you call up and use another–is more amazing but of small use to the a ...
Segmented Iterators and Hierarchical Algorithms
... Like fill1, fill steps through the range [first, last) from beginning to end. For each iteration it tests whether there are any remaining elements in the range, and, if there are, it performs the assignment *first = value and then increments first. The difference between fill1 and fill is how the ar ...
... Like fill1, fill steps through the range [first, last) from beginning to end. For each iteration it tests whether there are any remaining elements in the range, and, if there are, it performs the assignment *first = value and then increments first. The difference between fill1 and fill is how the ar ...
Data Structures and Algorithm Analysis in C
... analysis and its major weaknesses. Many examples are provided, including an indepth explanation of logarithmic running time. Simple recursive programs are analyzed by intuitively converting them into iterative programs. More complicated divide-and-conquer programs are introduced, but some of the ana ...
... analysis and its major weaknesses. Many examples are provided, including an indepth explanation of logarithmic running time. Simple recursive programs are analyzed by intuitively converting them into iterative programs. More complicated divide-and-conquer programs are introduced, but some of the ana ...
Fast Local Searches and Updates in Bounded Universes
... to that element’s successor. This pointer is then used to insert the element into the skip list. We then insert the appropriate prefixes of the element into the hash tables and the corresponding y-fast tries. The y-fast tries may need to be restructured as the buckets increase in size. The key obser ...
... to that element’s successor. This pointer is then used to insert the element into the skip list. We then insert the appropriate prefixes of the element into the hash tables and the corresponding y-fast tries. The y-fast tries may need to be restructured as the buckets increase in size. The key obser ...
2D DICTIONARY MATCHING IN SMALL SPACE by SHOSHANA
... succinct 2D dictionary matching with no slowdown and dynamic dictionary matching in small space. For each variant of the general 2D dictionary matching problem, we combined the original techniques presented in this thesis with new approaches to the classic problems. The first achievement of this the ...
... succinct 2D dictionary matching with no slowdown and dynamic dictionary matching in small space. For each variant of the general 2D dictionary matching problem, we combined the original techniques presented in this thesis with new approaches to the classic problems. The first achievement of this the ...
Universal Symbolic Execution and its Application to Likely Data
... predicates. The simplified local predicates are then used to generate local invariant templates. A local invariant template denotes a predicate that could potentially hold on any node in the data structure. 4. K RYSTAL collects local invariant templates across test executions and further simplifies ...
... predicates. The simplified local predicates are then used to generate local invariant templates. A local invariant template denotes a predicate that could potentially hold on any node in the data structure. 4. K RYSTAL collects local invariant templates across test executions and further simplifies ...
Repetition-Based Text Indexes
... signi cant portion of the results are from a joint publication with Erkki. The research has been carried out at the Department of Computer Science of the University of Helsinki. The department, headed by Professors Martti Tienari, Esko Ukkonen and Timo Alanko at di erent times during the research, h ...
... signi cant portion of the results are from a joint publication with Erkki. The research has been carried out at the Department of Computer Science of the University of Helsinki. The department, headed by Professors Martti Tienari, Esko Ukkonen and Timo Alanko at di erent times during the research, h ...
Quadtree
A quadtree is a tree data structure in which each internal node has exactly four children. Quadtrees are most often used to partition a two-dimensional space by recursively subdividing it into four quadrants or regions. The regions may be square or rectangular, or may have arbitrary shapes. This data structure was named a quadtree by Raphael Finkel and J.L. Bentley in 1974. A similar partitioning is also known as a Q-tree. All forms of quadtrees share some common features: They decompose space into adaptable cells Each cell (or bucket) has a maximum capacity. When maximum capacity is reached, the bucket splits The tree directory follows the spatial decomposition of the quadtree.