Hybrid Memory Buffer Microarchitecture for High-Radix Routers

Abstract

Hierarchical high-radix router microarchitecture consisting of small SRAM-based intermediate buffers has been used in large-scale supercomputers interconnection networks. While hierarchical organization enables efficient scaling to higher switch port count, it requires intermediate buffers which can cause performance bottleneck. Shallow intermediate buffers can cause head-of-line blocking to create backpressure towards input buffers and reduce overall performance. Increasing intermediate buffer size overcomes this problem but becomes infeasible due to the large overhead. In this work, we propose to organise decentralized intermediate buffers as a centralized buffer and leverage alternate memory technology to increase its capacity. In particular, we exploit the high-density nature of Spin-Torque Transfer Magnetic RAM (STT-MRAM) to increase intermediate buffer depth while also providing near-zero leakage power. STT-MRAM has disadvantages such as higher write latency and higher write energy. To overcome these disadvantages, we propose DeepHiR, a novel deep hybrid buffer organization (STT-MRAM and SRAM) combined with a centralized buffer organization to provide high performance with minimal cost. Although the deep intermediate buffer provided by DeepHiR can effectively improve router performance, a large amount of input buffer will still cause a lot of hardware overhead. At the same time, deeper intermediate buffers also makes it take longer for the backpressure to propagate to the source node, thereby reducing the performance of DeepHiR. Therefore, we further propose ElasHiR, which leverages elastic input buffer design in the centralized row buffer to allow a part of the centralized row buffer to act as input buffer. ElasHiR adopts reduced input buffers and automatically determines the length of input buffer in the centralized row buffer. This design minimizes the buffer resource while achieving excellent efficiency. Evaluation results show that DeepHiR can achieve 56.7 percent performance improvement in packet latency under synthetic traffic, and the cost of energy and area is moderate. ElasHiR can reduce the input buffer by 93.8 percent with performance comparable to DeepHiR.