Collaborative Research: PPoSS: LARGE: Research into the Use and iNtegration of Data Movement Accelerators (RUN-DMX)

协作研究：PPoSS：大型：数据移动加速器 (RUN-DMX) 的使用和集成研究

• 批准号: 2316177
• 负责人: Bahar Asgari
• 金额: $ 100万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2028-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316177&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; LARGE; into

The project aims to optimize communication overheads for high-performance computing (HPC) applications to better address the challenges caused by the rapid growth of data. Many important scientific simulations have large data inputs or outputs that may be sparse and that may also require extra effort and energy to move across processors, memories, and disks within a large HPC system. Moreover, an HPC node may have several different locations where data needs to be moved to and from while an HPC application is running. This work’s novelty lies in its focus on optimizing communication overheads for HPC systems and applications by supporting “computation-in-communication”. The integration of these new hardware advancements with appropriate software techniques will enhance the scalability of computing for high-performance applications including molecular dynamics simulations and applications like computational fluid dynamics codes. The project's impacts include: (1) enhanced scalability of computing for high-performance applications via reductions in data movement, (2) novel algorithmic and hardware designs for the broader HPC community to leverage emerging technologies combining communication and computation, and (3) open-source software infrastructure that can be used to facilitate education in parallel computing, HPC, and computer architecture at the graduate and undergraduate levels. The project's technical goals encompass several key components. First, it develops new algorithms to effectively leverage computation-in-communication paradigms. Second, it incorporates compression techniques to enhance data transfer efficiency, including the development of new, processing-friendly compressed formats specifically designed for sparse data. Third, the project designs novel hardware enhancements to optimize computation-in-communication. The project creates a full-stack solution for computation-in-communication with an open-source library focused on algorithmic and compression techniques which are co-designed with architecture designs to support efficient processing and manipulation of large data inputs. Combined, these hardware and software advancements will allow a larger class of applications to take advantage of next-generation data-centric HPC that minimizes communication overheads and data movement.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在优化高性能计算 (HPC) 应用程序的通信，以更好地应对数据快速增长带来的挑战。许多重要的科学模拟具有大量稀疏的数据输入或输出，并且可能需要额外的开销。此外，在 HPC 应用程序运行时，HPC 节点可能需要在多个不同位置之间移动数据。专注于通过支持“通信计算”来优化 HPC 系统和应用程序的通信开销这些新的硬件进步与适当的软件技术的集成将增强高性能应用程序的计算可扩展性，包括分子动力学模拟和计算流体动力学等应用程序。该项目的影响包括：(1) 通过减少数据移动来增强高性能应用程序的计算可扩展性，(2) 为更广泛的 HPC 社区提供新颖的算法和硬件设计，以利用通信和计算相结合的新兴技术，以及 (3) ）该项目的技术目标包括几个关键组成部分，它开发了新的算法来有效地利用计算。其次，它采用了压缩技术来提高数据传输效率，包括开发专门为稀疏数据设计的新的、易于处理的压缩格式。第三，该项目设计了新颖的硬件增强功能来优化通信中的计算。与专注于算法和压缩技术的开源库进行通信计算的全栈解决方案，这些算法和压缩技术与架构设计共同设计，以支持大数据输入的高效处理和操作。结合起来，这些硬件和软件的进步将允许。更大类别的应用程序利用下一代以数据为中心的 HPC，最大限度地减少通信开销和数据移动。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。