Parallel programming in OpenMP
Parallel programming in OpenMP
Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers (2nd Edition)
Programming for parallelism and locality with hierarchically tiled arrays
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
The rise and fall of High Performance Fortran: an historical object lesson
Proceedings of the third ACM SIGPLAN conference on History of programming languages
User-defined distributions and layouts in chapel: philosophy and framework
HotPar'10 Proceedings of the 2nd USENIX conference on Hot topics in parallelism
Effortless and Efficient Distributed Data-Partitioning in Linear Algebra
HPCC '10 Proceedings of the 2010 IEEE 12th International Conference on High Performance Computing and Communications
Automatic Data Partitioning Applied to Multigrid PDE Solvers
PDP '11 Proceedings of the 2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing
Trasgo: a nested-parallel programming system
The Journal of Supercomputing
The university of Florida sparse matrix collection
ACM Transactions on Mathematical Software (TOMS)
A preliminary nested-parallel framework to efficiently implement scientific applications
VECPAR'04 Proceedings of the 6th international conference on High Performance Computing for Computational Science
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Layout methods for dense and sparse data are often seen as two separate problems with their own particular techniques. However, they are based on the same basic concepts. This paper studies how to integrate automatic data-layout and partition techniques for both dense and sparse data structures. In particular, we show how to include support for sparse matrices or graphs in Hitmap, a library for hierarchical tiling and automatic mapping of arrays. The paper shows that it is possible to offer a unique interface to work with both dense and sparse data structures. Thus, the programmer can use a single and homogeneous programming style, reducing the development effort and simplifying the use of sparse data structures in parallel computations. Our experimental evaluation shows that this integration of techniques can be effectively done without compromising performance.