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» Estimating Execution Time of Distributed Applications
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ISPASS
2009
IEEE
15 years 10 months ago
Machine learning based online performance prediction for runtime parallelization and task scheduling
—With the emerging many-core paradigm, parallel programming must extend beyond its traditional realm of scientific applications. Converting existing sequential applications as w...
Jiangtian Li, Xiaosong Ma, Karan Singh, Martin Sch...
SIGMETRICS
2009
ACM
151views Hardware» more  SIGMETRICS 2009»
15 years 9 months ago
MapReduce optimization using regulated dynamic prioritization
We present a system for allocating resources in shared data and compute clusters that improves MapReduce job scheduling in three ways. First, the system uses regulated and user-as...
Thomas Sandholm, Kevin Lai
ICPP
2005
IEEE
15 years 8 months ago
Filter Decomposition for Supporting Coarse-Grained Pipelined Parallelism
We consider the filter decomposition problem in supporting coarse-grained pipelined parallelism. This form of parallelism is suitable for data-driven applications in scenarios wh...
Wei Du, Gagan Agrawal
ICPPW
2005
IEEE
15 years 8 months ago
A Practical Approach to the Rating of Barrier Algorithms Using the LogP Model and Open MPI
Large–scale parallel applications performing global synchronization may spend a significant amount of execution time waiting for the completion of a barrier operation. Conseque...
Torsten Hoefler, Lavinio Cerquetti, Torsten Mehlan...
PADS
2003
ACM
15 years 8 months ago
Maya: a Multi-Paradigm Network Modeling Framework
This paper presents Maya, a multi-paradigm, scalable and extensible network modeling framework for emulating distributed applications. A novel three-tier architecture is proposed ...
Junlan Zhou, Zhengrong Ji, Mineo Takai, Rajive Bag...