重新思考进程间通信与内存操作卸载
Rethinking Inter-Process Communication with Memory Operation Offloading
AI总结：
本文提出统一的IPC运行时套件，通过整合硬件和软件内存卸载技术，提升共享内存通信的效率与性能。
摘要：
随着多模态和AI驱动的服务每请求交换数百兆字节的数据，现有进程间通信（IPC）运行时在内存复制上花费的CPU周期越来越多。尽管硬件和软件机制都在探索内存卸载，但当前的IPC堆栈缺乏一个统一的运行时模型来有效协调它们。本文提出了一套统一的IPC运行时套件，将硬件和软件基于的内存卸载集成到共享内存通信中。系统刻画了卸载策略与IPC执行之间的交互，包括同步、缓存可见性和并发性，并引入了多种IPC模式，以平衡吞吐量、延迟和CPU效率。通过异步流水线、选择性缓存注入和混合协调，系统将卸载从设备特定的功能转变为通用系统能力。在真实工作负载上的评估显示，指令计数减少了高达22%，吞吐量提高了高达2.1倍，延迟减少了高达72%，证明了协调的IPC卸载在现代数据密集型系统中能够带来切实的端到端效率提升。
英文摘要：
As multimodal and AI-driven services exchange hundreds of megabytes per request, existing IPC runtimes spend a growing share of CPU cycles on memory copies. Although both hardware and software mechanisms are exploring memory offloading, current IPC stacks lack a unified runtime model to coordinate them effectively. This paper presents a unified IPC runtime suite that integrates both hardware- and software-based memory offloading into shared-memory communication. The system characterizes the interaction between offload strategies and IPC execution, including synchronization, cache visibility, and concurrency, and introduces multiple IPC modes that balance throughput, latency, and CPU efficiency. Through asynchronous pipelining, selective cache injection, and hybrid coordination, the system turns offloading from a device-specific feature into a general system capability. Evaluations on real-world workloads show instruction count reductions of up to 22%, throughput improvements of up to 2.1x, and latency reductions of up to 72%, demonstrating that coordinated IPC offloading can deliver tangible end-to-end efficiency gains in modern data-intensive systems.
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。
