On the Memory Underutilization: Exploring Disaggregated Memory on HPC Systems
On the Memory Underutilization: Exploring Disaggregated Memory on HPC Systems
复制标题
关于内存利用率不足：探索 HPC 系统上的分解内存
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
M. Gokhale
中科院分区：
文献类型：
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作者：
I. Peng;R. Pearce;M. Gokhale
大规模高性能计算 (HPC) 系统由紧密耦合在节点中的大量计算和内存资源组成。我们对四个生产 HPC 集群的内存利用率进行了大规模研究。我们的结果表明，超过 90% 的作业使用的节点内存容量低于 15%，并且 90% 的时间内存利用率低于 35%。最近，分解架构越来越受到关注，因为它可以有选择地扩展资源并提高资源利用率。基于这些观察，我们探索使用分解内存来支持内存密集型应用程序，而大多数作业在节点内存减少的 HPC 系统上保持不变。我们设计并开发了一个用户空间远程内存分页库，使应用程序能够探索现有 HPC 集群上的分解内存。我们在基准测试中量化了访问模式和网络连接的影响。我们对图形处理和蒙特卡罗应用程序的案例研究评估了应用程序特性和本地内存容量的影响，并强调了分解内存吞吐量扩展的潜力。
Large-scale high-performance computing (HPC) systems consist of massive compute and memory resources tightly coupled in nodes. We perform a large-scale study of memory utilization on four production HPC clusters. Our results show that more than 90% of jobs utilize less than 15% of the node memory capacity, and for 90% of the time, memory utilization is less than 35%. Recently, disaggregated architecture is gaining traction because it can selectively scale up a resource and improve resource utilization. Based on these observations, we explore using disaggregated memory to support memory-intensive applications, while most jobs remain intact on HPC systems with reduced node memory. We designed and developed a user-space remote-memory paging library to enable applications exploring disaggregated memory on existing HPC clusters. We quantified the impact of access patterns and network connectivity in benchmarks. Our case studies of graph-processing and Monte-Carlo applications evaluated the impact of application characteristics and local memory capacity and highlighted the potential of throughput scaling on disaggregated memory.
DOI：
10.1109/cluster.2019.8891023
发表时间：
2019-09
期刊：
2019 IEEE International Conference on Cluster Computing (CLUSTER)
影响因子：
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作者：
Yue Zhu;Weikuan Yu;Bing Jiao;K. Mohror;A. Moody;Fahim Chowdhury
通讯作者：
Yue Zhu;Weikuan Yu;Bing Jiao;K. Mohror;A. Moody;Fahim Chowdhury