Augment the Kraken! Increasing the scientific capability and capacity of the NSF's most powerful supercomputer

强化海妖！

• 批准号: 1041709
• 负责人: Patricia Kovatch
• 金额: --
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1041709&HistoricalAwards=false
• 关键词: Augment; Kraken; Increasing; scientific; capability

1041709 AndrewsThis award 12 cabinets of compute nodes, with an additional 144 TF and 18 TB of memory, to Kraken, taking it to 1,174 TF and 147 TB. This will increase its node count from 8,256 to 9,408 by adding 1,152 nodes. With the addition of 1,152 nodes, Kraken will have significant room left to run other jobs simultaneously with 8,192 node jobs. Indeed, it will be possible to run a 8,192 job and a 1,024 node job at the same time. Given the ?bi-modal? distribution of the user jobs, with most job sizes at either 8,192 and above, or at 1,024 and below, this will greatly improve the responsiveness of the machine for users. The Kraken upgrade will allow users to extend their codes by scaling them up to the largest NSF supercomputer, with over 110,000 cores, implementing new models that could not have been run before this upgrade. There already a number of users scaling their applications to the full size of the current Kraken, and some of them are already prepared to use a larger system. Additionally, this will help facilitate scaling for applications already chosen to run on the Track 1 Blue Waters system, and prepare them for that very large machine.

1041709 andrews此次授予Kraken 12个机柜的计算节点，加上额外的144tf和18tb内存，使其达到1174 TF和147tb。这将通过添加1,152个节点将其节点数从8,256增加到9,408。随着1152个节点的增加，Kraken将有足够的空间来同时运行其他8192个节点作业。实际上，可以同时运行8,192个节点的作业和1,024个节点的作业。给定？双峰？用户作业的分布（大多数作业大小为8,192及以上，或1,024及以下）将极大地提高机器对用户的响应能力。Kraken的升级将允许用户扩展他们的代码，将它们扩展到最大的NSF超级计算机，拥有超过110,000个内核，实现在升级之前无法运行的新模型。已经有许多用户将他们的应用程序扩展到当前Kraken的完整尺寸，其中一些用户已经准备使用更大的系统。此外，这将有助于促进已经选择在Track 1 Blue Waters系统上运行的应用程序的扩展，并为非常大的机器做好准备。