SHF: Small: Collaborative Research: High-performance hybrid micromagnetic-electromagnetic simulators for memory and high-performance data storage devices

SHF：小型：协作研究：用于内存和高性能数据存储设备的高性能混合微磁-电磁模拟器

• 批准号: 1116082
• 负责人: Eric Michielssen
• 金额: $ 24.93万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116082&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; performance

Data is at the heart of the digital revolution. Changing work habits, increasingly stringent electronic record-keeping mandates, and developments in the health care, energy, and retail sectors all suggest a growing demand for data storage and memories for the foreseeable future. Fast, low-power, ultra-high density, and non-volatile magnetic storage and memory systems are projected to accommodate the bulk of the global data storage needs for decades to come and spawn revolutionary data-intensive computing modalities along the way. Their timely development, however, critically depends on the availability of fast and accurate computational tools capable of simulating electromagnetic field and magnetization dynamics in complete magnetic data storage and memory systems. Unfortunately, current simulators are not up to this task. This study focuses on the development of high-performance hybrid micro magnetic-electromagnetic simulators for modeling next-generation magnetic memory and data storage systems. The simulators leverage analytically preconditioned time-domain integral equation methods to solve the Maxwell and Landau-Lifshitz-Gilbert-Slonczewski equations, which govern coupled electromagnetic field and magnetization phenomena. To facilitate the analysis of such phenomena in complex systems involving billions of degrees of freedom, the simulators are implemented on massively parallel Central Processing Unit (CPU) and Graphics Processing Unit (GPU) computers. These simulators are used for the design of next generation magnetic random memory devices as well as bit patterned media and heat-assisted magnetic recording storage systems. Such advanced memory and storage systems are to be essential to future high-performance computing systems.

数据是数字革命的核心。不断变化的工作习惯、日益严格的电子记录保存要求以及医疗保健、能源和零售部门的发展都表明，在可预见的未来，对数据存储和存储的需求将不断增长。快速、低功耗、超高密度和非易失性的磁存储和存储系统预计将满足未来几十年的大量全球数据存储需求，并在此过程中催生革命性的数据密集型计算模式。然而，它们的及时发展关键取决于能够在完整的磁数据存储和存储系统中模拟电磁场和磁化动力学的快速和准确的计算工具的可用性。不幸的是，目前的模拟器不能胜任这项任务。本研究致力于开发高性能的微磁-电磁混合模拟器，用于模拟下一代磁存储和数据存储系统。该模拟器利用解析预条件的时域积分方程法来求解控制耦合电磁场和磁化现象的Maxwell和Landau-Lifshitz-Gilbert-SLonczewski方程。为了便于分析涉及数十亿个自由度的复杂系统中的这种现象，模拟器在大规模并行的中央处理单元(CPU)和图形处理单元(GPU)计算机上实现。这些模拟器用于设计下一代磁随机存储设备以及位图案化介质和热辅助磁记录存储系统。这种先进的内存和存储系统对未来的高性能计算系统是必不可少的。