neoDBMS: Hardware/Software Co-Design for Accelerated Near-Data Processing in Modern Database Systems

随着半导体技术的进步，如今生产现代半导体存储(例如非易失性存储器)和功能强大的计算单元(FPGA、GPU、多核CPU)的组合已经变得经济实惠，这些计算单元共同位于或接近相同的芯片生产智能存储设备。在数据呈指数级增长的时代，数据移动已成为一个限制因素，因为它们是阻塞的、频繁的，并且会损害可扩展性。然而，现有的解决方案方法主要基于已有40年历史的体系结构，遵循将数据传输到处理元素的范例。这一过程既有时间上的惩罚，也有能源上的惩罚。“记忆墙”和“冯·诺伊曼瓶颈”放大了这些缺陷对性能的负面影响。本项目提案旨在探索能够执行近数据处理(NDP)的智能数据库存储的新体系结构、抽象和算法。我们的目标是智能存储设备，包括非易失性存储器或下一代3D-DRAM(如HMC)，以及使用现场可编程门阵列作为计算单元。我们打算调查以下研究问题：1)在更新环境和混合工作负载中对NDP的支持。2)在非易失性存储器上的DBMS中支持NDP和NDP-支持声明性数据布局。3)NDP使用共享虚拟内存。

With advances in semiconductor technologies, it has nowadays become economical to produce combinations of modern semiconductor storage (e.g., Non-Volatile Memories) and powerful compute-units (FPGA, GPU, many-core CPUs) co-located on, or close to, the same chip - yielding intelligent storage devices. Data movements have become a limiting factor in times of exponential data growth, since they are blocking, frequent, and impair scalability. However, existing solution approaches are mainly based on 40-year old architectures, following the paradigm of transporting data to the processing elements. This procedure has both time as well as energy penalties. The ``memory wall'' and the ``von Neumann bottleneck'' amplify the negative performance impact of those deficiencies. The present project proposal aims to explore new architectures, abstractions and algorithms for intelligent database storage capable of performing Near-Data Processing (NDP). We target intelligent storage devices, comprising Non-volatile Memories or next-generation 3D-DRAM (such as the HMC), as well as the use of FPGAs as computational-units. We intend to investigate the following research questions: 1) Support for NDP in update-environments and hybrid-workloads. 2) Support for NDP in DBMS on Non-volatile Memories and NDP-support for declarative data layouts. 3) NDP use of shared virtual memory.