Allocating programs containing branches and loops within a multiple processor system
IEEE Transactions on Software Engineering
Parallel processing: the Cm* experience
Parallel processing: the Cm* experience
Processor allocation in an N-cube multiprocessor using gray codes
IEEE Transactions on Computers
Data Structures and Algorithms
Data Structures and Algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Strategies for retargeting of existing sequential programs for parallel processing
Strategies for retargeting of existing sequential programs for parallel processing
Neural networks and dynamic complex systems
ANSS '89 Proceedings of the 22nd annual symposium on Simulation
A latency simulator for many-core systems
Proceedings of the 44th Annual Simulation Symposium
A data layout optimization framework for NUCA-based multicores
Proceedings of the 44th Annual IEEE/ACM International Symposium on Microarchitecture
Hi-index | 0.00 |
The mapping problem is the problem of implementing a computational task on a target architecture in order to maximize some performance metric. For a hypercube-interconnected multiprocessor, the mapping problem arises when the topology of a task graph is different from a hypercube. It is desirable to find a mapping of tasks to processors that minimizes average path length and hence interprocessor communication. The problem of finding an optimal mapping, however, has been proven to be NP-complete. Several different approaches have been taken to discover suitable mappings for a variety of target architectures. Since the mapping problem is NP-complete, approximation algorithms are used to find good mappings instead of optimal ones. Usually, greedy and/or local search algorithms are introduced to approximate the optimal solutions. This paper presents a greedy mapping algorithm for hypercube interconnection structures, which utilizes the graph-oriented mapping strategy to map a communication graph to a hypercube. The strategy is compared to previous strategies for attacking the mapping problem. A simulation is performed to estimate both the worst-case bounds for the greedy mapping strategy and the average performance.