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» Clustering performance data efficiently at massive scales
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ARTCOM
2009
IEEE
15 years 9 months ago
Maximizing the Efficiency of Parallel Apriori Algorithm
In this paper we attempt to maximize the efficiency of the parallel Apriori Algorithm. The paper analyzes the performance of the algorithm over different datasets and over n proce...
Ketan Shah, Sunita Mahajan
PADL
2009
Springer
16 years 6 months ago
Using Bloom Filters for Large Scale Gene Sequence Analysis in Haskell
Analysis of biological data often involves large data sets and computationally expensive algorithms. Databases of biological data continue to grow, leading to an increasing demand ...
Ketil Malde, Bryan O'Sullivan
CLUSTER
2005
IEEE
15 years 11 months ago
Implementation and Performance of Portals 3.3 on the Cray XT3
The Portals data movement interface was developed at Sandia National Laboratories in collaboration with the University of New Mexico over the last ten years. Portals is intended t...
Ron Brightwell, Trammell Hudson, Kevin T. Pedretti...
PROCEDIA
2011
14 years 9 months ago
10x10: A General-purpose Architectural Approach to Heterogeneity and Energy Efficiency
Two decades of microprocessor architecture driven by quantitative 90/10 optimization has delivered an extraordinary 1000-fold improvement in microprocessor performance, enabled by...
Andrew A. Chien, Allan Snavely, Mark Gahagan
BMCBI
2010
139views more  BMCBI 2010»
15 years 6 months ago
A highly efficient multi-core algorithm for clustering extremely large datasets
Background: In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput t...
Johann M. Kraus, Hans A. Kestler