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Reducing server data traffic using a hierarchical computation model

机译:使用分层计算模型减少服务器数据流量

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Commercial workloads impose heavy demands on memory and storage subsystems in a server and often result in a large amount of traffic in I/O and memory buses. To reduce the data movement between the storage subsystem and the processing units, we propose a hierarchical computing (HC) system that distributes processing elements across the storage hierarchy. We present a programming model that allows us to decompose database queries into simple operations. These operations are then distributed and executed by the different layers of the hierarchy depending on the affinity of the task to a particular layer. Commands percolate down into the lower layers of the hierarchy and partially processed information flows up into the higher layers, where subsequent operations can be performed. We evaluate the effectiveness of the proposed hierarchical computing model by performing full system simulations of a business decision support system (DSS) workload. On a group of TPC-H-like queries, hierarchical computing systems reduce the amount of data transferred over the processor to memory interconnect by 37-58 percent. We also observe that HC configurations show speedups between 1.14x and 1.45x when compared with CC-NUMA with 32 processors.
机译:商业工作负载对服务器中的内存和存储子系统提出了很高的要求,并经常导致I / O和内存总线中的大量流量。为了减少存储子系统和处理单元之间的数据移动,我们提出了一种分层计算(HC)系统,该系统在整个存储层次结构中分布处理元素。我们提出了一种编程模型,该模型允许我们将数据库查询分解为简单的操作。然后,根据任务对特定层的亲和力,由层次结构的不同层分发和执行这些操作。命令向下渗透到层次结构的较低层,部分处理的信息向上流入较高的层,可以在其中执行后续操作。我们通过执行业务决策支持系统(DSS)工作负载的完整系统模拟,评估提出的分层计算模型的有效性。在一组类似TPC-H的查询上,分层计算系统将通过处理器传输到内存互连的数据量减少了37-58%。我们还观察到,与具有32个处理器的CC-NUMA相比,HC配置显示出1.14倍至1.45倍的加速。

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