Recursive Diagonal Torus: An Interconnection Network for Massively Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
CANPC '98 Proceedings of the Second International Workshop on Network-Based Parallel Computing: Communication, Architecture, and Applications
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ISCA '81 Proceedings of the 8th annual symposium on Computer Architecture
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IEEE Transactions on Computers
IEEE Transactions on Computers
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IEEE Transactions on Computers
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IEEE Transactions on Parallel and Distributed Systems
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IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Twisted Torus Topologies for Enhanced Interconnection Networks
IEEE Transactions on Parallel and Distributed Systems
Embedding Meshes and Tori on Double-Loop Networks of the Same Size
IEEE Transactions on Computers
The IBM Blue Gene/Q Interconnection Fabric
IEEE Micro
L-Networks: A Topological Model for Regular 2D Interconnection Networks
IEEE Transactions on Computers
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Twisted torus topologies have been proposed as an alternative to toroidal rectangular networks, improving distance parameters and providing network symmetry. However, twisting is apparently less amenable to task mapping algorithms of real life applications. In this paper we make an analytical study of different mapping and concentration techniques on 2D twisted tori that try to compensate for the twisted peripheral links. We introduce a performance model based on the network average distance and the detection of the set of links which receive the highest load. The model also considers the amount of local and global communications in the network. Our model shows that the twisted torus can improve latency and maximum throughput over rectangular torus, especially when global communications dominate over local ones and when some concentration is employed. Simulation results corroborate our synthetic model. For real applications from the NPB benchmark suite, the use of the twisted topologies with an appropriate mapping provides overall average application speedups of 2.9%, which increase to 4.9% when concentrated topologies (c = 2) are considered.