2199 research outputs found
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TechKNOW Volume 2, Issue 2
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Luna Negra, Fall 1996
No full texthttps://kent-islandora.s3.us-east-2.amazonaws.com/node/17190/86992-thumbnail.jpgAdditional Staff- John Beitzel; Brad "Java" Bonnell; Amber Boyce; Kurt Brown; Amy Caminate; Ray "DM" Emigh; Bea Fontaine; Michael Goelich; Elizabeth Goodwin; Joseph Gut; M. Catherine Lee; Amy Lile; Jenn "Sage" Lin; Robert Make; Lori Mau; Malinda Peterson; Rita Revy; Gail Schadt
Contributor- Nara Abercrombie-Snyder, Kaye Adams, Deb Andersen, Angela Bilia, Amber Boyce, Jason Bryant, Julie Carlson, Lisa Couch, Veronica Falletta, Jandy Hanna, Andy Kohler, Carmella Labriola, Micky MacAdam, Robert Maefs, Kristina McLaughlin, Jennfier McVeigh, Lori Mau, Teresa Marie Metcalfe, Kristina Molina, Merle Mollenkopf, Summer Paris, John Phillips, Andrews Revy, Gail Schadt, Thomas Sweterlitsch, Mickey Thompson, Susan Yurik, Trina Baker, Steven Beauchamp, Heather Benjamin, Michael Christopher, Garth Ferguson, Justin Hart, Melville MacKay, J. Prodan, James Patrick Sinisi, W. Gregory Stewart, N. Anne Highlands Tiley, Ryan G. Van Cleave, Anne Wilson, Randy Peterson, Rita Revy
Patrons/Donors/Sponsors/Advertisers- Bonnie Graham, Frank Bertayou, Lonnie Ray Johnson, Dr. David Ewbank, Dr. Clyde Jertaft, Maggie Anderson, Dr. Kathe Davis, The Wick Poetry Program, Marilyn Seguin, Brady\u27s Cafe</p
An Associative Implementation of Classical Convex Hull Algorithm
No full texthttps://kent-islandora.s3.us-east-2.amazonaws.com/node/17416/87349-thumbnail.jpgThis paper will present the implementation and comparison of new parallel algorithms for the convex hull problem. These algorithms are a parallel adaptation of the Jarvis March and the Quickhull algorithms. The computational model selected for these algorithms is the associative computing model (ASC) and the multiple associative computing model (MASC). Both models support massive parallelism through the use of data parallelism and constant time associative search and maximum functions. Also, ASC can be supported on many SIMD computers. These algorithms requires O(n) space, O(log n) (i.e., O(log2 n)) average running time, and O(n) worst case running time. These algorithms have been compared using random data.</p
TechKNOW Volume 2, Issue 1
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TechKNOW Volume 2, Issue 3
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Ferrante Winery, 1996
No full texthttps://kent-islandora.s3.us-east-2.amazonaws.com/wineryne/62/thumbnail.jpgView of the new tasting room and wine shop after a fire destroyed the restaurant in November of 1994.</p
Solving a 2D knapsack problem on an associative computer augmented with a linear network
No full texthttps://kent-islandora.s3.us-east-2.amazonaws.com/node/16712/87543-thumbnail.jpgThis paper describes a parallelization of the sequential dynamic programming method for solving a 2D knapsack problem where multiples of n rectangular objects are optimally packed into a knapsack of size L × W and are only obtainable with guillotine-type (side to side) cuts. The parallel algorithm is described and analyzed for the associative model. The associative model (ASC) for parallel computation supports a generalized version of an associative style of computing that has been used since the introduction of associative SIMD computers in the early 1970\u27s. In particular, this model supports data parallelism, constant time maximum and minimum operations, one or more instruction streams (ISs) which are sent to an equal number of partition sets of processors, and assignment of tasks to the ISs using control parallelism. This algorithm runs in O(W(n+L+W)) time using O(L) processors, where L ≥ W for a 2D knapsack problem with a capacity of L × W. This result is cost optimal with respect to the best sequential implementation. Moreover, an efficient ASC algorithm for this well-known problem should give insight to how the associative model compares to other parallel models.</p
The Art and Influences of Someroski
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ICON Spring 1996
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VLDC String Matching for Associative Computing and Multiple Broadcast Mesh
No full texthttps://kent-islandora.s3.us-east-2.amazonaws.com/node/16801/87547-thumbnail.jpgThis paper presents a new parallel algorithm far string matching with variable length "don\u27t care" (VLDC). The initial computational model used is the associative computing model (ASC) enhanced with a linear network. ASC is a natural extension of the data parallel paradigm to a complete model far parallel computation. It supports massively parallelism through the use of data parallelism and constant time functions such as associative search and maximum value. It is also shown that the same algorithm is equally adaptable to the mesh with multiple broadcast. The algorithm has a run time of O(m) using O(n) processors, given a pattern of size m and a text of size n. The algorithm has the unique feature of permitting the identification of all match continuation points in the text after each "don\u27t care" character.</p