Multiple-Way Network Partitioning
IEEE Transactions on Computers
Finding good approximate vertex and edge partitions is NP-hard
Information Processing Letters
Hypergraph-Partitioning-Based Decomposition for Parallel Sparse-Matrix Vector Multiplication
IEEE Transactions on Parallel and Distributed Systems
Zoltan Data Management Service for Parallel Dynamic Applications
Computing in Science and Engineering
Partitioning for Complex Objectives
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Graph partitioning for high-performance scientific simulations
Sourcebook of parallel computing
A hypergraph partitioning based approach for scheduling of tasks with batch-shared I/O
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
IEEE Transactions on Parallel and Distributed Systems
Efficient distributed mesh data structure for parallel automated adaptive analysis
Engineering with Computers
A repartitioning hypergraph model for dynamic load balancing
Journal of Parallel and Distributed Computing
Evaluation of Hierarchical Mesh Reorderings
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Parallel re-initialization of level set functions on distributed unstructured tetrahedral grids
Journal of Computational Physics
The university of Florida sparse matrix collection
ACM Transactions on Mathematical Software (TOMS)
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
On partitioning problems with complex objectives
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing
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A hypergraph model for mapping applications with an all-neighbor communication pattern to distributed-memory computers is proposed, which originated in finite element triangulations. Rather than approximating the communication volume for linear algebra operations, this new model represents the communication volume exactly. To this end, a hypergraph partitioning problem is formulated where the objective function involves a new metric. This metric, the @l(@l-1)-metric, accurately models the communication volume for an all-neighbor communication pattern occurring in a concrete finite element application. It is a member of a more general class of metrics, which also contains more widely used metrics, such as the cut-net and the (@l-1)-metric. In addition, we develop a heuristic to minimize the communication volume in the new @l(@l-1)-metric. For the solution of several real-world finite element problems, experimental results based on this new heuristic demonstrate a small reduction in communication volume compared to a standard graph partitioner and do not show significant reductions in communication volume compared to a hypergraph partitioner using the common (@l-1)-metric. However, for this set of problems, the new approach does reduce actual communication times. As a by-product, we observe that it also tends to reduce the number of messages. Furthermore, the new approach dramatically reduces the communication volume for a set of sparse matrix problems that are more irregularly-structured than finite element problems.