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» Predicting locality phases for dynamic memory optimization
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PDCN
2004
13 years 11 months ago
K-Means VQ algorithm using a low-cost parallel cluster computing
It is well-known that the time and memory necessary to create a codebook from large training databases have hindered the vector quantization based systems for real applications. T...
Paulo Sergio Lopes de Souza, Alceu de Souza Britto...
LCPC
2009
Springer
14 years 2 months ago
A Balanced Approach to Application Performance Tuning
Abstract. Current hardware trends place increasing pressure on programmers and tools to optimize scientific code. Numerous tools and techniques exist, but no single tool is a pana...
Souad Koliai, Stéphane Zuckerman, Emmanuel ...
ASPDAC
2006
ACM
158views Hardware» more  ASPDAC 2006»
14 years 3 months ago
Analysis of scratch-pad and data-cache performance using statistical methods
— An effectively designed and efficiently used memory hierarchy, composed of scratch-pads or cache, is seen today as the key to obtaining energy and performance gains in data-do...
Javed Absar, Francky Catthoor
CGO
2006
IEEE
14 years 1 months ago
Profiling over Adaptive Ranges
Modern computer systems are called on to deal with billions of events every second, whether they are instructions executed, memory locations accessed, or packets forwarded. This p...
Shashidhar Mysore, Banit Agrawal, Timothy Sherwood...
RECSYS
2010
ACM
13 years 10 months ago
Incremental collaborative filtering via evolutionary co-clustering
Collaborative filtering is a popular approach for building recommender systems. Current collaborative filtering algorithms are accurate but also computationally expensive, and so ...
Mohammad Khoshneshin, W. Nick Street