Sort vs. Hash Join Revisited for Near-Memory Execution
Sort vs. Hash Join Revisited for Near-Memory Execution
复制标题
重新审视排序与哈希连接以实现近内存执行
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Boris Grot
中科院分区：
文献类型：
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作者：
Nooshin Mirzadeh;Onur Kocberber;B. Falsafi;Boris Grot
内存和CPU之间的数据移动是众所周知的分析能源瓶颈。近存储器处理（NMP）是一种很有前途的方法，通过将大部分计算转移到新兴的堆叠DRAM芯片中的存储器阵列来消除这一瓶颈。最近在这一领域的工作一直局限于可以本地化到单个DRAM分区的常规计算。本文研究了Join工作负载，这是分析的基础，其特征是不规则的内存访问模式。我们考虑了几个连接算法，并表明，虽然近数据执行可以提高能源效率和性能，有效的NMP算法必须考虑的地方，访问粒度，和堆栈存储器设备的微架构。
Data movement between memory and CPU is a well-known energy bottleneck for analytics. Near-Memory Processing (NMP) is a promising approach for eliminating this bottleneck by shifting the bulk of the computation toward memory arrays in emerging stacked DRAM chips. Recent work in this space has been limited to regular computations that can be localized to a single DRAM partition. This paper examines a Join workload, which is fundamental to analytics and is characterized by irregular memory access patterns. We consider several join algorithms and show that while near-data execution can improve both energy-efficiency and performance, effective NMP algorithms must consider locality, access granularity, and microarchitecture of the stacked memory devices.
影响因子：
2.5
作者：
Albutiu, Martina-Cezara;Kemper, Alfons;Neumann, Thomas
通讯作者：
Neumann, Thomas