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IEEECIT
2010
IEEE
13 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...
ECCV
2002
Springer
15 years 4 days ago
Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration
We investigate in this article the rigid registration of large sets of points, generally sampled from surfaces. We formulate this problem as a general Maximum-Likelihood (ML) estim...
Sébastien Granger, Xavier Pennec
HPDC
2010
IEEE
13 years 11 months ago
Exploring application and infrastructure adaptation on hybrid grid-cloud infrastructure
Clouds are emerging as an important class of distributed computational resources and are quickly becoming an integral part of production computational infrastructures. An importan...
Hyunjoo Kim, Yaakoub El Khamra, Shantenu Jha, Mani...
HPDC
2012
IEEE
12 years 20 days ago
Understanding the effects and implications of compute node related failures in hadoop
Hadoop has become a critical component in today’s cloud environment. Ensuring good performance for Hadoop is paramount for the wide-range of applications built on top of it. In ...
Florin Dinu, T. S. Eugene Ng
CLOUD
2010
ACM
14 years 3 months ago
Comet: batched stream processing for data intensive distributed computing
Batched stream processing is a new distributed data processing paradigm that models recurring batch computations on incrementally bulk-appended data streams. The model is inspired...
Bingsheng He, Mao Yang, Zhenyu Guo, Rishan Chen, B...