在批量位wise内存内处理中实现关系数据库分析处理
Enabling Relational Database Analytical Processing in Bulk-Bitwise Processing-In-Memory
AI总结：
本文扩展gem5以在批量位wise PIM中支持GROUP-BY与JOIN，并用Star Schema Benchmark证明其在性能、能耗、寿命及相对MonetDB速度上的显著提升。
摘要：
批量位wise内存内处理（PIM）是一种利用存储器阵列作为计算单元的新兴计算范式，已被证明可使数据库应用受益。本文展示了如何在批量位wise PIM中高效执行GROUP-BY和JOIN这两项此前工作未支持的数据库操作，以用于关系数据库分析处理。我们扩展了gem5仿真器，并在Star Schema Benchmark上评估了硬件修改。结果表明，与先前工作相比，我们的修改平均将执行时间改善1.83倍、能耗改善4.31倍，并将系统寿命提高3.21倍。我们还相对于现代先进的内存数据库MonetDB实现了4.65倍的加速。
英文摘要：
Bulk-bitwise processing-in-memory (PIM), an emerging computational paradigm utilizing memory arrays as computational units, has been shown to benefit database applications. This paper demonstrates how GROUP-BY and JOIN, database operations not supported by previous works, can be performed efficiently in bulk-bitwise PIM for relational database analytical processing. We extend the gem5 simulator and evaluated our hardware modifications on the Star Schema Benchmark. We show that compared to previous works, our modifications improve (on average) execution time by 1.83X, energy by 4.31X, and the system's lifetime by 3.21X. We also achieved a speedup of 4.65X over MonetDB, a modern state-of-the-art in-memory database.
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。
