Treffer: 面向集合通信硬件卸载的维序触发机制和数据缓存方法.

Title:
面向集合通信硬件卸载的维序触发机制和数据缓存方法. (Chinese)
Alternate Title:
Order-preserving triggering mechanism and data buffering method for collective communication hardware offloading. (English)
Source:
Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao; Dec2025, Vol. 47 Issue 6, p13-23, 11p
Database:
Complementary Index

Weitere Informationen

To further optimize the hardware offloading of collective communication based on the network interface card in the "Tianhe" network, and to support more types of collective communication algorithms and larger message sizes, the order-preserving triggering mechanism and data buffering method for collective communication hardware offloading was investigated. An order-preserving triggering mechanism for concurrent multitasking was proposed, which meets the desired semanties of collective communication and ensures the reproducibility of floating-point computation results. A dynamic network data buffering method based on Hash tables and pulsed credit flow control was proposed to alleviate the contradiction between limited hardware buffering resources and the high demand for buffering a large amount of network data from concurrent multitasking. Experimental results show that compared with software-based collective communication operations, this method can support the hardware offloading of various algorithms for several typical collective communication operations, with significant performance improvement. Meanwhile, the hardware implementation cost is low, especially with high utilization of buffering resources. [ABSTRACT FROM AUTHOR]

为了对"天河"网络中基于网卡的集合通信硬件卸载功能进行进一步优化, 以支持更多类型的集合通信算法以及更大的消息尺寸, 研究了面向集合通信硬件卸载的维序触发机制和数据缓存方法。提出面向多任务并发的维序触发机制, 既满足了期望的集合通信语义, 又确保了浮点计算操作结果的可复现性; 提出基于哈希表和脉冲信用流控的网络数据动态缓存方法, 以缓解有限的硬件缓存资源和多任务并发的大量网络数据缓存需求之间的矛盾问题。实验结果表明, 与基于软件方式的集合通信操作相比, 该方法可以支持多种典型集合通信操作的多种算法的硬件卸载, 且性能提升效果显著, 同时, 硬件实现代价较低, 尤其是在缓存资源方面具有较高的利用率. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)