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» Automatic Performance Debugging of SPMD Parallel Programs
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IEEECIT
2010
IEEE
14 years 8 months ago
Scaling the iHMM: Parallelization versus Hadoop
—This paper compares parallel and distributed implementations of an iterative, Gibbs sampling, machine learning algorithm. Distributed implementations run under Hadoop on facilit...
Sebastien Bratieres, Jurgen Van Gael, Andreas Vlac...
IPPS
2000
IEEE
15 years 2 months ago
Controlling Distributed Shared Memory Consistency from High Level Programming Languages
One of the keys for the success of parallel processing is the availability of high-level programming languages for on-the-shelf parallel architectures. Using explicit message passi...
Yvon Jégou
PPOPP
2012
ACM
13 years 5 months ago
Internally deterministic parallel algorithms can be fast
The virtues of deterministic parallelism have been argued for decades and many forms of deterministic parallelism have been described and analyzed. Here we are concerned with one ...
Guy E. Blelloch, Jeremy T. Fineman, Phillip B. Gib...
SPIESR
1998
151views Database» more  SPIESR 1998»
14 years 11 months ago
Synthesizing Parallel Imaging Applications Using the CAP Computer-Aided Parallelization Tool
Abstract. Imaging applications such as filtering, image transforms and compression/decompression require vast amounts of computing power when applied to large data sets. These appl...
Benoit A. Gennart, Marc Mazzariol, Vincent Messerl...
PLDI
1993
ACM
15 years 1 months ago
Global Optimizations for Parallelism and Locality on Scalable Parallel Machines
Data locality is critical to achievinghigh performance on large-scale parallel machines. Non-local data accesses result in communication that can greatly impact performance. Thus ...
Jennifer-Ann M. Anderson, Monica S. Lam