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» Distributed Machine Learning: Scaling Up with Coarse-grained...
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PPOPP
2010
ACM
14 years 5 months ago
Leveraging parallel nesting in transactional memory
Exploiting the emerging reality of affordable multi-core architeces through providing programmers with simple abstractions that would enable them to easily turn their sequential p...
João Barreto, Aleksandar Dragojevic, Paulo ...
EUROPAR
2007
Springer
14 years 2 months ago
Experiences Understanding Performance in a Commercial Scale-Out Environment
Abstract. Clusters of loosely connected machines are becoming an important model for commercial computing. The cost/performance ratio makes these scale-out solutions an attractive ...
Robert W. Wisniewski, Reza Azimi, Mathieu Desnoyer...
IPPS
2010
IEEE
13 years 6 months ago
PreDatA - preparatory data analytics on peta-scale machines
Peta-scale scientific applications running on High End Computing (HEC) platforms can generate large volumes of data. For high performance storage and in order to be useful to scien...
Fang Zheng, Hasan Abbasi, Ciprian Docan, Jay F. Lo...
HPCN
1998
Springer
14 years 19 days ago
PARAFLOW: A Dataflow Distributed Data-Computing System
We describe the Paraflow system for connecting heterogeneous computing services together into a flexible and efficient data-mining metacomputer. There are three levels of parallel...
Roy Williams, Bruce Sears
ICDM
2010
IEEE
189views Data Mining» more  ICDM 2010»
13 years 5 months ago
S4: Distributed Stream Computing Platform
Abstract--S4 is a general-purpose, distributed, scalable, partially fault-tolerant, pluggable platform that allows programmers to easily develop applications for processing continu...
Leonardo Neumeyer, Bruce Robbins, Anish Nair, Anan...