A large class of applications need to execute the same workflow on different datasets of
identical size. Efficient execution of such applications necessitates intelligent distribution of …

Focusing on algorithms for distributed-memory parallel architectures, Parallel Algorithms
presents a rigorous yet accessible treatment of theoretical models of parallel computation …

Distributed Memory Multicomputers (DMMs), such as the IBM SP-2, the Intel Paragon, and
the Thinking Machines CM-5, offer significant advantages over shared memory …

Recently, the high-performance computing community has realized that power is a
performance-limiting factor. One reason for this is that supercomputing centers have limited …

A heuristic algorithm that maps data processing tasks onto heterogeneous resources (ie
processors and links of various capacities) is presented. The algorithm tries to achieve a …

This paper addressesoptimal mapping of parallel programs composed of a chain of data
parallel tasks onto the processors of a parallel system. The input to this class of programs is …

Mapping applications onto parallel platforms is a challenging problem, that becomes even
more difficult when platforms are heterogeneous—nowadays a standard assumption. A high …

This paper addresses optimal mapping of parallel programs composed of a chain of data
parallel tasks onto the processors of a parallel system. The input to the programs is a stream …

This paper surveys parallel SCHEDULING problems and metrics correlated to and then
applys metrics to make decision in comparison to other policy schedulers. Parallel …

High Performance Fortran (HPF) has emerged as a standard language fordata parallel
computing. However, a wide variety of scientific applications are best programmed by a …