Solving Irregularly Structured Problems in Parallel

This book constitutes the refereed proceedings of the 4th International Symposium on Solving Irregularly Structured Problems in Parallel, IRREGULAR'97, held in Paderborn, Germany, in June 1997.
The 18 revised full papers presented were carefully selected by the program committee for inclusion in the volume; also included are full papers by the five invited speakers. Among the topics covered are discrete algorithms, randomized methods and approximation algorithms, implementations, programming environments, systems and applications, and scheduling and load balancing.

Inhalt
Parallel mesh generation.- Efficient massively parallel quicksort.- Practical parallel list ranking.- On computing all maximal cliques distributedly.- A probabilistic model for best-first search B&B algorithms.- Programming irregular parallel applications in Cilk.- A variant of the biconjugate gradient method suitable for massively parallel computing.- Efficient implementation of the improved quasi-minimal residual method on massively distributed memory computers.- Programming with shared data abstractions.- EXPLORER: Supporting run-time parallelization of DO-ACROSS loops on general networks of workstations.- Engineering diffusive load balancing algorithms using experiments.- Comparative study of static scheduling with task duplication for distributed systems.- A new approximation algorithm for the register allocation problem.- A parallel approximation algorithm for resource constrained scheduling and bin packing.- Virtual Data Space-A Universal load balancing scheme.- Improving cache performance through tiling and data alignment.- A support for non-uniform parallel loops and its application to a flame simulation code.- Performance optimization of combined variable-cost computations and I/O.- Parallel shared-memory state-space exploration in stochastic modeling.- Parallel software caches.- Communication efficient parallel searching.- Parallel sparse Cholesky factorization.- Unstructured graph partitioning for sparse linear system solving.