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LEF: An Effective Routing Algorithm for Two-Dimensional Meshes
Thiem Van CHU Kenji KISE
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2019/10/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Computer System
Network-on-Chip, two-dimensional (2D) mesh, routing algorithm, Long Edge First (LEF),
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We design a new oblivious routing algorithm for two-dimensional mesh-based Networks-on-Chip (NoCs) called LEF (Long Edge First) which offers high throughput with low design complexity. LEF's basic idea comes from conventional wisdom in choosing the appropriate dimension-order routing (DOR) algorithm for supercomputers with asymmetric mesh or torus interconnects: routing longest dimensions first provides better performance than other strategies. In LEF, we combine the XY DOR and the YX DOR. When routing a packet, which DOR algorithm is chosen depends on the relative position between the source node and the destination node. Decisions of selecting the appropriate DOR algorithm are not fixed to the network shape but instead made on a per-packet basis. We also propose an efficient deadlock avoidance method for LEF in which the use of virtual channels is more flexible than in the conventional method. We evaluate LEF against O1TURN, another effective oblivious routing algorithm, and a minimal adaptive routing algorithm based on the odd-even turn model. The evaluation results show that LEF is particularly effective when the communication is within an asymmetric mesh. In a 16×8 NoC, LEF even outperforms the adaptive routing algorithm in some cases and delivers from around 4% up to around 64.5% higher throughput than O1TURN. Our results also show that the proposed deadlock avoidance method helps to improve LEF's performance significantly and can be used to improve O1TURN's performance. We also examine LEF in large-scale NoCs with thousands of nodes. Our results show that, as the NoC size increases, the performance of the routing algorithms becomes more strongly influenced by the resource allocation policy in the network and the effect is different for each algorithm. This is evident in that results of middle-scale NoCs with around 100 nodes cannot be applied directly to large-scale NoCs.