Memory storage patterns in parallel processing
Memory storage patterns in parallel processing
Tiling multidimensional iteration spaces for nonshared memory machines
Proceedings of the 1991 ACM/IEEE conference on Supercomputing
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
The high performance Fortran handbook
The high performance Fortran handbook
The ADDAP system on the iPSC/860: automatic data distribution and parallelization
Journal of Parallel and Distributed Computing
Efficient Algorithms for Data Distribution on Distributed Memory Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
A Framework for Exploiting Task and Data Parallelism on Distributed Memory Multicomputers
IEEE Transactions on Parallel and Distributed Systems
Journal of Computational and Applied Mathematics
A computation + communication load balanced loop partitioning method for distributed memory systems
Journal of Parallel and Distributed Computing
IEEE Transactions on Parallel and Distributed Systems
Automatic Data Layout Using 0-1 Integer Programming
PACT '94 Proceedings of the IFIP WG10.3 Working Conference on Parallel Architectures and Compilation Techniques
Library support for hierarchical multi-processor tasks
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Tlib-a library to support programming with hierarchical multi-processor tasks
Journal of Parallel and Distributed Computing
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Multiprocessor task (M-task) programming is a suitable parallel programming model for coding application problems with an inherent modular structure. An M-task can be executed on a group of processors of arbitrary size, concurrently to other M-tasks of the same application program. The data of a multiprocessor task program usually include composed data structures, like vectors or arrays. For distributed memory machines or cluster platforms, those composed data structures are distributed within one or more processor groups. Thus, a concise parallel programming model for M-tasks requires a standardized distributed data format for composed data structure. Additionally, functions for data-re-distribution with respect to different data distribution and processor group layouts are needed to glue program parts together. In this paper, we present a data-re-distribution library which extends the M-task programming with Tlib, a library providing operations to split processor groups and map M-tasks to processor groups.