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

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以归因于其制造和运行(包括空闲)。因此，一旦安装了一个系统，就必须尽可能地将其使用到接近满负荷的程度，并且在任何时候都应该最大限度地提高科学产量，以便从该系统的货币成本和碳成本上获得最佳的投资回报。然而，在实践中几乎不可能达到这个非常理想的100%使用率。系统的工作负载由其资源分配器管理，资源分配器试图从提交队列中放置作业(用户不断向其中添加新作业)，以填补可用资源中的空白。资源分配中的延展性引入了这样的概念，即用户在作业提交时请求的资源(计算核心或节点的数量，甚至系统)不是固定的，并且如果这种改变意味着作业可以被调度来运行从而很快完成，则可以被改变。MIRA(资源分配中的延展性，用于提高高性能计算中的系统效率)将研究在单个系统内以及跨多个系统的计算资源分配中的延展性的概念，以提高整体系统利用率和科学吞吐量，从而最大限度地提高可实现的“每焦耳科学”。

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.