@InProceedings{	  Garcke.Hegland.Nielsen:2003,
  author	= {J. Garcke and M. Hegland and O. Nielsen},
  title		= {Parallelisation of Sparse Grids for Large Scale Data
		  Analysis},
  booktitle	= {Proceedings of the International Conference on
		  Computational Science 2003 (ICCS 2003) Melbourne,
		  Australia},
  year		= {2003},
  editor	= {P. Sloot and D. Abramson and A. Bogdanov and J. Dongarra
		  and A. Zomaya and Y. Gorbachev},
  series	= {Lecture Notes in Computer Science},
  volume	= 2659,
  pages		= {683-692},
  publisher	= {Springer},
  pdf		= {http://wissrech.ins.uni-bonn.de/research/pub/garcke/wopla2003.pdf}
		  ,
  optnote	= {also as SFB 611 preprint 65, Universit\"at Bonn, 2003},
  abstract	= {Sparse Grids (SG), due to Zenger, are the basis for
		  efficient high dimensional approximation and have recently
		  been applied successfully to predictive modelling. They are
		  spanned by a collection of simpler function spaces
		  represented by regular grids. The combination technique
		  prescribes how approximations on simple grids can be
		  combined to approximate the high dimensional functions. It
		  can be improved by iterative refinement.
		  
		  Fitting sparse grids admits the exploitation of parallelism
		  at various stages. The fit can be done entirely by fitting
		  partial models on regular grids. This allows parallelism
		  over the partial grids. In addition, each of the partial
		  grid fits can be parallelised as well, both in the assembly
		  phase where parallelism is done over the data and in the
		  solution stage using traditional parallel solvers for the
		  resulting PDEs. A simple timing model confirms that the
		  most effective methods are obtained when both types of
		  parallelism are used.},
  annote	= {proc,other}
}