心灵感应数据中心：利用共享CXL内存实现快速RPC
Telepathic Datacenters: Fast RPCs using Shared CXL Memory
AI总结：
本文提出RPCool框架，利用CXL共享内存传递指针避免序列化，并通过内存权限管理和RDMA回退保障安全与可扩展性，相比现有机制大幅降低RPC往返延迟。
摘要：
数据中心应用通常依赖远程过程调用（RPC）来实现快速、高效且安全的通信。然而，RPC速度慢、效率低且难以使用，因为它们需要通过分组串行网络链路进行昂贵的序列化和压缩才能通信。Compute Express Link 3.0（CXL）提供了一种替代方案，允许应用通过跨机器集群的缓存一致性共享内存接口来共享数据。RPCool是一个利用CXL共享内存能力的新框架。RPCool通过传递共享内存中数据结构的指针来避免序列化。虽然避免序列化很有用，但直接共享指针丰富的数据消除了传统网络复制数据所提供的隔离性，使接收方容易受到无效指针和发送方对共享数据的并发更新的影响。RPCool通过谨慎且高效的内存权限管理来恢复这种安全性。CXL共享内存能力的另一个重大挑战是它们不太可能扩展到整个数据中心。RPCool通过回退到基于RDMA的通信来解决这一问题。总体而言，与最先进的基于RDMA和基于CXL的RPC机制相比，RPCool将往返延迟分别降低了1.93倍和7.2倍。此外，在各种工作负载中，RPCool的性能与其他RPC机制相当或更好。
英文摘要：
Datacenter applications often rely on remote procedure calls (RPCs) for fast, efficient, and secure communication. However, RPCs are slow, inefficient, and hard to use as they require expensive serialization and compression to communicate over a packetized serial network link. Compute Express Link 3.0 (CXL) offers an alternative solution, allowing applications to share data using a cache-coherent, shared-memory interface across clusters of machines. RPCool is a new framework that exploits CXL's shared memory capabilities. RPCool avoids serialization by passing pointers to data structures in shared memory. While avoiding serialization is useful, directly sharing pointer-rich data eliminates the isolation that copying data over traditional networks provides, leaving the receiver vulnerable to invalid pointers and concurrent updates to shared data by the sender. RPCool restores this safety with careful and efficient management of memory permissions. Another significant challenge with CXL shared memory capabilities is that they are unlikely to scale to an entire datacenter. RPCool addresses this by falling back to RDMA-based communication. Overall, RPCool reduces the round-trip latency by 1.93$\times$ and 7.2$\times$ compared to state-of-the-art RDMA and CXL-based RPC mechanisms, respectively. Moreover, RPCool performs either comparably or better than other RPC mechanisms across a range of workloads.
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。
