Enabling Efficient Transaction Processing on CXL-Based Memory Sharing

2025-02-16

AI Summary

To address transaction performance bottlenecks in multi-node OLTP systems over CXL shared memory architectures—caused by high cross-node cache coherence overhead and network latency—this paper proposes a low-latency transaction processing architecture. Methodologically, it introduces (1) a hybrid cache coherence primitive tailored to the loosely consistent nature of transactional data, enabling fine-grained, on-demand selection of consistency levels; and (2) hardware-software co-optimization, comprising CXL protocol extensions, a lightweight local coherence maintenance mechanism, and transaction-aware memory access scheduling. Evaluated under standard OLTP workloads, the architecture achieves significantly higher throughput than conventional network-based solutions and up to 2.08× improvement over baseline CXL shared memory systems. It effectively balances high performance with programmable consistency guarantees.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Data transparency and provenance

Abstract

Transaction processing systems are the crux for modern data-center applications, yet current multi-node systems are slow due to network overheads. This paper advocates for Compute Express Link (CXL) as a network alternative, which enables low-latency and cache-coherent shared memory accesses. However, directly adopting standard CXL primitives leads to performance degradation due to the high cost of maintaining cross-node cache coherence. To address the CXL challenges, this paper introduces CtXnL, a software-hardware co-designed system that implements a novel hybrid coherence primitive tailored to the loosely coherent nature of transactional data. The core innovation of CtXnL is empowering transaction system developers with the ability to selectively achieve data coherence. Our evaluations on OLTP workloads demonstrate that CtXnL enhances performance, outperforming current network-based systems and achieves with up to 2.08x greater throughput than vanilla CXL memory sharing architectures across universal transaction processing policies.

Problem

• Enhance transaction processing efficiency
• Address CXL memory sharing challenges
• Improve performance over network-based systems

Innovation

• CXL-based memory sharing
• Hybrid coherence primitive
• Selective data coherence