论文标题
应用于大数据问题的临近记忆处理架构
A Migratory Near Memory Processing Architecture Applied to Big Data Problems
论文作者
论文摘要
主流供应商生产的服务器由于这些系统的基本体系结构固有的瓶颈而在处理大数据查询方面效率低下。当前的服务器叶片包含通过互连芯片组连接到DRAM内存和磁盘的多层处理器。在DRAM和DISK存储数据时，多层处理器芯片执行所有计算，但没有处理能力。要执行数据库查询，必须在DRAM和小型缓存以及DRAM和DISK之间来回移动数据。对于大数据应用，此数据移动繁重。迁移近存储器服务器通过将大量轻质处理器直接放入存储系统中来解决此瓶颈。这些处理器直接在已适当的大数据应用程序的关系，顶点和边缘上运行，而无需在DRAM，CACHE和重量级多级处理器之间来回穿梭大量数据。本文介绍了这种体系结构在关系数据库中的应用选择和加入查询。初步结果表明端到端的数量级加速顺序。
Servers produced by mainstream vendors are inefficient in processing Big Data queries due to bottlenecks inherent in the fundamental architecture of these systems. Current server blades contain multicore processors connected to DRAM memory and disks by an interconnection chipset. The multicore processor chips perform all the computations while the DRAM and disks store the data but have no processing capability. To perform a database query, data must be moved back and forth between DRAM and a small cache as well as between DRAM and disks. For Big Data applications this data movement in onerous. Migratory Near Memory Servers address this bottleneck by placing large numbers of lightweight processors directly into the memory system. These processors operate directly on the relations, vertices and edges of Big Data applications in place without having to shuttle large quantities of data back and forth between DRAM, cache and heavyweight multicore processors. This paper addresses the application of such an architecture to relational database SELECT and JOIN queries. Preliminary results indicate end-to-end orders of magnitude speedup.
