Malleability in resource allocation for improved system efficiency in high-performance computing

资源分配的可塑性可提高高性能计算的系统效率

• 批准号: EP/Y53061X/1
• 负责人: Mark Parsons
• 金额: $ 20.82万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY53061X%2F1
• 关键词: Malleability; resource; allocation; improved; system

A significant part of the environmental impact and CO2 emissions of a high-performance computing (HPC) system can be attributed to its manufacturing as well as its operation (including running idle). Once a system has been installed, it is therefore imperative that it is used as close to full capacity as possible and that science throughput should be maximised at all times, in order to get the best return on investment on both the monetary and carbon cost of the system. This highly desirable 100% utilisation rate is however near impossible to achieve in practice. The workload of a system is managed by its resource allocator, which attempts to place jobs from a submission queue (that users continuously add new jobs to) to fill gaps in the available resources. It is not always possible to attain perfect job placement and as a result, resources sit idle.Malleability in resource allocation introduces the concept that the resources (the number of compute cores or nodes, or even the system) that have been requested by a user at job submission time are not fixed and can be changed if this change means a job can be scheduled to run, and thus complete, sooner.MIRA ("Malleability In Resource Allocation for improved system efficiency in high-performance computing") will investigate the concept of malleability in compute resource allocation within a single system as well as across multiple systems, to improve overall system utilisation and science throughput, thereby maximising the "science per Joule'' that can be achieved.

高性能计算 (HPC) 系统的环境影响和二氧化碳排放的很大一部分可归因于其制造及其运行（包括空闲运行）。一旦安装了系统，就必须尽可能接近满负荷使用，并且始终最大限度地提高科学吞吐量，以便在货币和碳成本方面获得最佳投资回报。系统。然而，这种非常理想的 100% 利用率在实践中几乎不可能实现。系统的工作负载由其资源分配器管理，该分配器尝试从提交队列（用户不断向其中添加新作业）放置作业以填补可用资源中的空白。实现完美的工作安排并不总是可能的，因此，资源闲置。资源分配的可延展性引入了这样的概念：用户请求的资源（计算核心或节点的数量，甚至系统的数量）作业提交时间不固定，可以更改，如果此更改意味着可以安排作业运行，从而更快地完成。MIRA（“资源分配的可延展性，以提高高性能计算中的系统效率”）将研究计算延展性的概念单个系统内以及跨多个系统的资源分配，以提高整体系统利用率和科学吞吐量，从而最大化可实现的“每焦耳科学”。