PIM-STM：面向存内计算系统的软件事务内存
PIM-STM: Software Transactional Memory for Processing-In-Memory Systems
AI总结：
本文提出PIM-STM库，在首个商用存内计算系统UPMEM上实现多种软件事务内存算法，通过系统评估设计空间、内存层级选择及对传统CPU事务内存应用的加速效果，验证了存内计算架构在事务内存场景下的性能与内存效率优势。
摘要：
存内计算（PIM）是一种通过在现有DRAM内存芯片上增加轻量级逻辑的新型方法。通过允许将计算卸载到PIM系统，该架构能够规避影响许多现代工作负载的数据瓶颈问题。本文通过引入PIM-STM来解决如何高效构建事务内存（TM）抽象的软件实现问题，PIM-STM是一个为UPMEM（首个商用PIM系统）提供多种TM实现的库。通过广泛的研究，我们评估了在这一新兴架构上TM算法设计空间中不同替代方案的效率。我们进一步量化了使用UPMEM系统不同内存层级（在延迟与容量方面具有不同权衡）来存储不同TM实现所用元数据的影响。最后，我们评估了使用PIM-STM加速原本为传统基于CPU系统设计的事务内存应用时，在性能和内存效率方面可获得的收益。
英文摘要：
Processing-In-Memory (PIM) is a novel approach that augments existing DRAM memory chips with lightweight logic. By allowing to offload computations to the PIM system, this architecture allows for circumventing the data-bottleneck problem that affects many modern workloads. This work tackles the problem of how to build efficient software implementations of the Transactional Memory (TM) abstraction by introducing PIM-STM, a library that provides a range of diverse TM implementations for UPMEM, the first commercial PIM system. Via an extensive study we assess the efficiency of alternative choices in the design space of TM algorithms on this emerging architecture. We further quantify the impact of using different memory tiers of the UPMEM system (having different trade-offs for what concerns latency vs capacity) to store the metadata used by different TM implementations. Finally, we assess the gains achievable in terms of performance and memory efficiency when using PIM-STM to accelerate TM applications originally conceived for conventional CPU-based systems.
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