Efficient Computing for Particle Physics (Lead Proposal)

粒子物理的高效计算（主要提案）

• 批准号: ST/T002816/1
• 负责人: Sinead Farrington
• 金额: $ 1.34万
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FT002816%2F1
• 关键词: Efficient; Computing; Particle; Physics; Lead

STFC science projects are highly data-intensive, and depend crucially on high performance software and computing, as well as on instruments and facilities. Software is used for: control and operation of experiments; filtering and data reduction; signal processing and reconstruction; statistical data analysis; and experiment simulation. The high-luminosity LHC upgrade (HL-LHC) in 2026, will require greater computing performance than any other current science facility. At present, there is no means of meeting HL-LHC computing requirements within a reasonable funding envelope. In particular, the use of current software with a ten times larger dataset will not scale, since the "Moore's Law" decrease of computing costs with time has effectively ceased due to our inability to efficiently use modern computing platforms. It is clear that this is an urgent issue that must be addressed promptly, while there is still time to put in place the required new technologies. We will engage with industry and train ourselves in new computing architectures. We will identify the parts of our millions of lines of code that will benefit most strongly from adaptation to these new methods.

STFC的科学项目是高度数据密集型的，并且在很大程度上依赖于高性能的软件和计算，以及仪器和设施。软件用于：实验的控制和操作;滤波和数据简化;信号处理和重建;统计数据分析;以及实验模拟。2026年的高亮度LHC升级（HL-LHC）将需要比目前任何其他科学设施更高的计算性能。目前，没有办法在合理的资金范围内满足HL-LHC的计算要求。特别地，使用具有十倍大的数据集的当前软件将不能扩展，因为计算成本随时间的“摩尔定律”降低已经由于我们不能有效地使用现代计算平台而有效地停止。显然，这是一个必须迅速解决的紧迫问题，同时仍有时间建立所需的新技术。我们将与行业合作，并在新的计算架构中训练自己。我们将确定数百万行代码中的哪些部分将从适应这些新方法中受益最大。