Omegaflow: a high-performance dependency-based architecture
Abstract
This paper investigates how to better track and deliver dependency in dependency-based cores to exploit instruction-level parallelism (ILP) as much as possible. To this end, we first propose an analytical performance model for the state-of-art dependency-based core, Forwardflow, and figure out two vital factors affecting its upper bound of performance. Then we propose Omegaflow,a dependency-based architecture adopting three new techniques, which respond to the discovered factors. Experimental results show that Omegaflow improves IPC by 24.6% compared to the state-of-the-art design, approaching the performance of the OoO architecture with an ideal scheduler (94.4%) without increasing the clock cycle and consumes only 8.82% more energy than Forwardflow.
Omegaflow是我博士期间的一个研究工作，它是一种基于前向数据流的思想的指令调度和执行架构。 前向数据流架构与传统Tomasulo算法不同，由producer指令记录consumer的位置，并在计算完成后主动向consumer指令传递数据。 基于此，每次指令计算完成之后，只需要传递有限次包含唤醒信息的Token就可以唤醒所有依赖于该指令的consumer指令。 而在传统Tomasulo算法中，无论是隐式重命名需要向整个issue queue广播寄存器tag和value， 而显示重命名则需要向整个issue queue广播寄存器tag。 由于指令的依赖存在局部性，前向数据流架构可以将调度和执行引擎划分为多个组，组间的通信显著少于组内的通信， 从而实现可扩展的指令窗口。 Omegaflow为前向数据流架构提出了一种性能上限分析工具，并改进了Token的处理和传输速度。 （虽然但是，如论文中报告的一样，前向数据流架构仍然无法outperform 传统Tomasulo算法。） Omegaflow的代码在：Omegaflow project。
Zhou, Yaoyang
Architect of LLM DSA; Maintainer of u-arch simulator for Xiangshan; PhD of Computer Architecture
I specialize in LLM DSA and CPU micro-architecture.