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» Mechanisms for Mapping High-Level Parallel Performance Data
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CORR
2010
Springer
153views Education» more  CORR 2010»
15 years 1 months ago
GraphLab: A New Framework for Parallel Machine Learning
Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insuf...
Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny B...
HPDC
2002
IEEE
15 years 6 months ago
Flexibility, Manageability, and Performance in a Grid Storage Appliance
We present NeST, a flexible software-only storage appliance designed to meet the storage needs of the Grid. NeST has three key features that make it well-suited for deployment in...
John Bent, Venkateshwaran Venkataramani, Nick LeRo...
HPDC
2010
IEEE
15 years 2 months ago
MOON: MapReduce On Opportunistic eNvironments
MapReduce offers a flexible programming model for processing and generating large data sets on dedicated resources, where only a small fraction of such resources are every unavaila...
Heshan Lin, Xiaosong Ma, Jeremy S. Archuleta, Wu-c...
EUROPAR
2009
Springer
15 years 6 months ago
Capturing and Visualizing Event Flow Graphs of MPI Applications
A high-level understanding of how an application executes and which performance characteristics it exhibits is essential in many areas of high performance computing, such as applic...
Karl Fürlinger, David Skinner
SEMWEB
2004
Springer
15 years 6 months ago
Inferring Data Transformation Rules to Integrate Semantic Web Services
Abstract. OWL-S allows selecting, composing and invoking Web Serdifferent levels of abstraction: selection uses high level abstract descriptions, invocation uses low level groundi...
Bruce Spencer, Sandy Liu