Performance modelling and dynamic scheduling of geometric multigrid problems on heterogeneous supercomputing clusters

超级计算机是解决计算问题的重要工具，这些问题对于单个计算机来说太大了，往往具有重大意义。超级计算机是一个由联网的单个计算机组成的大型集合，大型问题的处理是通过将它们分成多个部分，并将每个部分分配给每台计算机并行处理。这样一个大问题的一个众所周知的例子是气象局执行的天气预报，气象局在他们的超级计算机上运行详细的气候模型，以预测接下来几天的天气。然而，超级计算机的获取、升级和运行都非常昂贵--购买或升级一台拥有数万个微处理器的现代高端超级计算机的成本高达数千万英镑，电费高达数百万英镑。为了让超级计算机所有者对他们的超级计算机做出明智的财务决策，了解他们的特定计算需求与当前超级计算机配置以及未来潜在配置之间的关系至关重要。这项研究是与设计和制造喷气发动机的劳斯莱斯航空航天部门合作进行的。英特尔是一家半导体制造商，主要以其计算机微处理器而闻名，这项研究有两个目标。第一个是开发HYPENS（罗尔斯·罗伊斯公司用于在超级计算机上计算流体动力学的发动机）中模拟处理时间与不同超级计算机配置（包括罗尔斯·罗伊斯公司目前拥有和运营的超级计算机配置）之间关系的模型，并开发工具以协助开发这些模型。这就需要全面建模HYDRONIC如何将模拟划分为并行处理的部分，然后在英特尔的帮助下建模不同的硬件配置如何处理这些部分。这些模式将提供两个好处：识别HYDRONIC中的低效率，让劳斯莱斯Royce评估他们的超级计算机的潜在升级对HYPERANT性能的影响。本研究的第二个目标是开发一种调度算法，该算法使用HYPERANT模拟的开发性能模型，以便在提交模拟作业时分配超级计算机的比例，以便最大化完成的总体模拟工作与消耗的电力之间的比率。这样的调度算法将不限于HYDRONIC，因为它将与具有性能模型的任何仿真引擎兼容，因此它对操作超级计算机的任何商业或研究组都是潜在的益处。

The supercomputer is an essential tool for solving computational problems far too large for a single computer, problems that tend to have great significance. A supercomputer is a large collection of individual computers networked together, and large problems are processed by dividing them into pieces and giving one to each computer to process in parallel. A well known example of such a large problem is weather forecasting as performed by the Met Office, who run a detailed model of the climate on their supercomputer in order to predict the weather over the following days. However supercomputers are very expensive to obtain, upgrade and operate - the cost of purchasing or upgrading a modern high-end supercomputer with tens of thousands of microprocessors is tens of millions of pounds and its electricity bill is millions of pounds. In order for a supercomputer owner to make informed financial decisions regarding their supercomputer it is crucial to understand the relationship between their particular computational needs and the configuration of their current supercomputer and of potential future configurations.This research is a partnership with the aerospace division of Rolls-Royce which designs and manufactures jet engines, and also with Intel who are a semiconductor manufacturer primarily known for their computer microprocessors, and the research has two goals. The first is to develop models of the relationship between the processing time of simulations in HYDRA, Rolls-Royce's engine for computing fluid dynamics on a supercomputer, and varying supercomputer configurations including that currently owned and operated by Rolls-Royce, and also to develop tools to assist with the development of those models. This entails modelling comprehensively how HYDRA divides simulations into pieces for parallel processing, then modelling how different hardware configurations fare in processing those pieces with the assistance of Intel. These models will provide two benefits: identifying inefficiencies in HYDRA, and allowing Rolls-Royce to evaluate potential upgrades to their supercomputer in regards to the effect on the performance of HYDRA.The second goal of this research is to develop a scheduling algorithm that uses the developed performance models of HYDRA simulations in order to allocate proportions of a supercomputer to simulation jobs as they are submitted in order to maximise the ratio between overall simulation work performed and electricity consumed. Such a scheduling algorithm will not be limited to HYDRA as it will be compatible with any simulation engine that has a performance model, thus it is of potential benefit to any business or research group that operates a supercomputer.