论文标题
减轻数据分析加速中的数据冲突和设计集中化
Alleviating Datapath Conflicts and Design Centralization in Graph Analytics Acceleration
论文作者
论文摘要
以前的图形分析加速器通过减轻不规则的芯片内存储器访问，从而实现了吞吐量。但是，芯片侧数据索的冲突和设计集中化已成为阻碍进一步改善吞吐量的关键问题。在本文中，提出了一个通用解决方案，即多阶段分散的传播网络（MDP-NETWORK）来解决这些问题，这是受到交易延迟吞吐量的关键思想的启发。此外，通过部署MDP网络来解决实践中的每个问题，提出了一种新颖的高吞吐量分析加速器，即《神学》。该实验表明，与最先进的加速器相比，Higraph达到了高达2.2倍的速度（平均1.5倍）以及更好的可扩展性。
Previous graph analytics accelerators have achieved great improvement on throughput by alleviating irregular off-chip memory accesses. However, on-chip side datapath conflicts and design centralization have become the critical issues hindering further throughput improvement. In this paper, a general solution, Multiple-stage Decentralized Propagation network (MDP-network), is proposed to address these issues, inspired by the key idea of trading latency for throughput. Besides, a novel High throughput Graph analytics accelerator, HiGraph, is proposed by deploying MDP-network to address each issue in practice. The experiment shows that compared with state-of-the-art accelerator, HiGraph achieves up to 2.2x speedup (1.5x on average) as well as better scalability.
