Harvest：适用于大规模领域的自适应光子交换调度
Harvest: Adaptive Photonic Switching Schedules for Collective Communication in Scale-up Domains
AI总结：
Harvest通过自适应光子交换调度优化集体通信性能，减少完成时间，适用于多种算法和光子技术。
摘要：
随着芯片间硅光子学因带宽和能耗效率而受到关注，其电路交换性质引发了集体通信中的基本问题：何时以及如何重新配置互连以实现这些优势？建立直接的光学路径可以减少拥堵和传播延迟，但每次重新配置都会带来不可忽视的开销，使逐步重新配置变得不切实际。我们提出了Harvest，一种系统方法，用于合成拓扑重新配置调度，以最小化光子互连中的集体完成时间。给定一个集体通信算法及其固定的通信调度，Harvest确定互连在集体过程中应如何演变，明确平衡重新配置延迟与拥堵和传播延迟。我们将合成问题转化为一个动态规划问题，其下包含一个拓扑优化子问题，并证明该方法适用于任意的集体通信算法。此外，我们利用一个著名的AllReduce算法（递归倍增）的算法结构，合成最优的重新配置调度，而无需使用任何优化器。通过使用重新配置延迟参数化公式，Harvest自然适应各种光子技术。通过包级和流级评估，以及在商业GPU上的硬件仿真，我们证明了Harvest合成的调度在多个集体算法中显著减少了集体完成时间，优于静态互连和每步重新配置的基线。
英文摘要：
As chip-to-chip silicon photonics gain traction for their bandwidth and energy efficiency, their circuit-switched nature raises a fundamental question for collective communication: when and how should the interconnect be reconfigured to realize these benefits? Establishing direct optical paths can reduce congestion and propagation delay, but each reconfiguration incurs non-negligible overhead, making naive per-step reconfiguration impractical. We present Harvest, a systematic approach for synthesizing topology reconfiguration schedules that minimize collective completion time in photonic interconnects. Given a collective communication algorithm and its fixed communication schedule, Harvest determines how the interconnect should evolve over the course of the collective, explicitly balancing reconfiguration delay against congestion and propagation delay. We reduce the synthesis problem into a dynamic program with an underlying topology optimization subproblem and show that the approach applies to arbitrary collective communication algorithms. Furthermore, we exploit the algorithmic structure of a well-known AllReduce algorithm (Recursive Doubling) to synthesize optimal reconfiguration schedules without using any optimizers. By parameterizing the formulation using reconfiguration delay, Harvest naturally adapts to various photonic technologies. Using packet-level and flow-level evaluations, as well as hardware emulation on commercial GPUs, we show that the schedules synthesized by Harvest significantly reduce collective completion time across multiple collective algorithms compared to static interconnects and reconfigure-every-step baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
