Research Group of Prof. Dr. J. Garcke
Institute for Numerical Simulation

  author = {J. Garcke and M. Hegland and O. Nielsen},
  title = {Parallelisation of Sparse Grids for Large Scale Data
  journal = {ANZIAM Journal},
  year = {2006},
  volume = {48},
  pages = {11-22},
  number = {1},
  abstract = {Sparse Grids 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 sparse grid combination technique prescribes how
		  approximations on a collection of anisotropic grids can be
		  combined to approximate the high dimensional functions. In
		  this paper we study the parallelisation of fitting data
		  onto a sparse grid. The computation can be done entirely by
		  fitting partial models on a collection of regular grids.
		  This allows parallelism over the collection of 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. Using
		  a simple timing model we confirm that the most effective
		  methods are obtained when both types of parallelism are
		  used. },
  annote = {journal},
  file = {sgParallelAnziam.pdf:http\://},
  pdf = { 1},
  doi = {10.1017/S1446181100003382}