Inkeeping with my interests in algorithms (see here), I would like to know if there are (contrary to my previous question), algorithms and data structures that are mainstream in parallel programming. It is probably early to ask about mainstream parallel algos and ds, but some of the gurus here may have had good experiences/bad experiences with some of them.
EDIT: I am more interested in successful practical applications of algos and ds than in academic papers.
Thanks
Maybe, I totally miss the point, but there are a ton of mainstream parallel algos and data structures, e.g. matrix multiplication, FFT, PDE and linear equation solvers, integration and simulation (Monte-Carlo / random numbers), searching and sorting, and so on. Take a look at the Designing and Building Parallel Programs or Patterns for Parallel Programming. And then there is CUDA and the like. What are you after?
Many of Google's whitepapers, especially but not exclusively ones linked from this page, describe successful practical applications of parallel distributed computing and/or their DS and algorithmic underpinnings. For example, this paper deals with modifying a DBMS's data structures to extract intra-transaction parallelism; this one (and some others) introduces the popular mapreduce architecture, since implemented e.g. in hadoop; this one is about highly parallelizable approximate matrix factoring suitable for use in "kernel methods" in machine learning; etc, etc...