Informatica

In this paper we consider parallel numerical integration algorithms for multi-dimensional integrals. A new hyper-rectangle selection strategy is proposed for the implementation of globally adaptive parallel quadrature algorithms. The well known master-slave parallel algorithm prototype is used for the realization of the algorithm. Numerical results on the SP2 computer and on a cluster of workstations are reported. A test problem where the integrand function has a strong corner singularity is investigated. A modified parallel integration algorithm is proposed in which a list of subproblems is distributed among slave processors.

In this paper we describe implementation of numerical adaptive algorithms for multi-dimensional quadrature on distributed-memory parallel systems. The algorithms are targeted at clusters of workstations with standard message passing interfaces, e.g., PVM or MPI. The most important issues are communication and load balancing. Static and dynamic partitioning of the region are considered. Numerical results on various workstation clusters are reported.

This paper deals with load balancing of parallel algorithms for distributed-memory computers. The parallel versions of BLAS subroutines for matrix-vector product and LU factorization are considered. Two task partitioning algorithms are investigated and speed-ups are calculated. The cases of homogeneous and heterogeneous collections of computers/processors are studied, and special partitioning algorithms for heterogeneous workstation clusters are presented.