AI 中文总结
针对存算一体系统中PIM-tree牺牲空间局部性导致范围聚合查询处理受阻的问题，提出查询密度驱动分区方案及B⁺-Forest，实现时空负载均衡与高效范围聚合查询，性能优于对比方案。
AI 中文摘要
存算一体（PIM）系统由多个带小型本地内存的处理器组成，近年来已成为商用产品，作为突破内存墙的手段受到广泛关注，尤其适用于内存数据库技术领域。最新的面向PIM的索引PIM-tree已被证明能在偏斜查询下实现渐近良好的时空负载均衡——查询负载与数据大小在处理器间实现均衡——但该方法以牺牲空间局部性为代价。遗憾的是，这种空间局部性的牺牲阻碍了面向PIM的范围聚合查询处理。为在PIM系统上同时实现时空负载均衡与高效执行范围聚合查询，我们提出一种查询密度驱动的键范围分区方案，该方案平衡了PIM处理器间的查询密度，使我们能通过一个参数在查询负载与数据大小间取得平衡。随后，我们基于该分区方案开发了面向PIM的B⁺树变体B⁺-Forest。实验结果表明，与基于空间约束、查询负载均衡、密度无感知分区的B⁺树相比，B⁺-Forest表现出更高的偏斜抗性；在点查询中其性能与PIM-tree相当，且能高效支持范围聚合查询。
英文摘要
Processing-in-Memory (PIM) systems, which consist of many processors with small local memory, have recently emerged as commercial products and attracted much attention as a means of overcoming the memory wall, particularly in the context of in-memory database technology. The state-of-the-art PIM-oriented index PIM-tree has been demonstrated to achieve asymptotically good spatiotemporal load balancing---query loads and data sizes are balanced among processors---for skewed queries, by trading spatial locality. Unfortunately, such a sacrifice of spatial locality hinders the PIM-oriented processing of range-aggregate queries. To achieve both spatiotemporal load balancing and efficiently executing range-aggregate queries on PIM systems, we develop a query density-driven key-range partitioning scheme. It balances query density among PIM processors, allowing us to strike a balance between query load and data size via a parameter. We then develop B${}^\text{+}$-Forest, a PIM-oriented B${}^\text{+}$-tree variant based on our partitioning scheme. Experimental results demonstrated that it exhibits higher skew resistance than a B${}^\text{+}$-tree based on space-constrained, query-load-balanced, density-unaware partitioning, and performance comparable to PIM-tree in point-get queries, as well as efficient support for range-aggregate queries.
CommentsAn extended version of the ICPP 2026 paper
