Can far memory improve job throughput?
Can far memory improve job throughput?
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DOI：
10.1145/3342195.3387522
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Emmanuel Amaro;Christopher Branner-Augmon;Zhihong Luo;Amy Ousterhout;M. Aguilera;Aurojit Panda;Sylvia Ratnasamy;S. Shenker
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文献类型：
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作者：
Emmanuel Amaro;Christopher Branner-Augmon;Zhihong Luo;Amy Ousterhout;M. Aguilera;Aurojit Panda;Sylvia Ratnasamy;S. Shenker
随着内存需求的增长，内存技术的进步速度慢，足够的主内存的可用性越来越多地在大型计算簇中瓶颈。一种解决方案是内存分解，在其中作业可以在其他服务器上远程访问内存或远处的内存。本文首先提出更快的交换机制和远距离感知的集群调度程序，使得可以在机架尺度上支持远处的内存。然后，它检查了这种使用远处可增加作业吞吐量的条件。我们发现，虽然远距离内存不是灵丹妙药，但对于内存密集的工作负载，即使不改变可用的总内存量，也可以以10％或更高的订单提供性能改进。
As memory requirements grow, and advances in memory technology slow, the availability of sufficient main memory is increasingly the bottleneck in large compute clusters. One solution to this is memory disaggregation, where jobs can remotely access memory on other servers, or far memory. This paper first presents faster swapping mechanisms and a far memory-aware cluster scheduler that make it possible to support far memory at rack scale. Then, it examines the conditions under which this use of far memory can increase job throughput. We find that while far memory is not a panacea, for memory-intensive workloads it can provide performance improvements on the order of 10% or more even without changing the total amount of memory available.