通过数据库分析理解批量位近内存处理
Understanding Bulk-Bitwise Processing In-Memory Through Database Analytics
AI总结：
本文构建基于忆阻状态逻辑的PIMDB系统，以关系数据库分析为负载评估批量位PIM，证明其可显著减少内存读取、提升查询性能并降低能耗。
摘要：
批量位近内存处理（PIM）是一种新兴计算形式，由存储阵列自身并行执行大规模位运算，有望缓解内存墙问题。本文通过构建PIMDB来考察批量位PIM的能力。PIMDB是一个基于忆阻状态逻辑的全数字系统，利用并聚焦内存内批量位运算，旨在加速真实工作负载：关系数据库的分析处理。我们提出一种主机处理器编程模型，以在虚拟内存中支持批量位PIM；开发高效执行内存内过滤和聚合操作的技术；并将应用数据集适配到内存中。为理解批量位PIM，我们将其与同一主机系统上的等效内存数据库进行比较。结果表明，批量位PIM大幅减少所需内存读取操作次数，从而使TPC-H过滤操作加速1.6×至18×、完整查询加速56×至608×，同时这些基准的能耗分别降低1.7×至18.6×和0.81×至12×。我们的广泛评估使用gem5全系统仿真环境。仿真还评估了单元耐久性，结果显示所需耐久性处于RRAM器件现有耐久性范围内。
英文摘要：
Bulk-bitwise processing-in-memory (PIM), where large bitwise operations are performed in parallel by the memory array itself, is an emerging form of computation with the potential to mitigate the memory wall problem. This paper examines the capabilities of bulk-bitwise PIM by constructing PIMDB, a fully-digital system based on memristive stateful logic, utilizing and focusing on in-memory bulk-bitwise operations, designed to accelerate a real-life workload: analytical processing of relational databases. We introduce a host processor programming model to support bulk-bitwise PIM in virtual memory, develop techniques to efficiently perform in-memory filtering and aggregation operations, and adapt the application data set into the memory. To understand bulk-bitwise PIM, we compare it to an equivalent in-memory database on the same host system. We show that bulk-bitwise PIM substantially lowers the number of required memory read operations, thus accelerating TPC-H filter operations by 1.6$\times$--18$\times$ and full queries by 56$\times$--608$\times$, while reducing the energy consumption by 1.7$\times$--18.6$\times$ and 0.81$\times$--12$\times$ for these benchmarks, respectively. Our extensive evaluation uses the gem5 full-system simulation environment. The simulations also evaluate cell endurance, showing that the required endurance is within the range of existing endurance of RRAM devices.
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