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

该项目旨在优化高性能计算（HPC）应用程序的通信开销，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都有大量的数据输入或输出，这些数据可能是稀疏的，并且可能还需要额外的努力和精力来在大型HPC系统中的处理器、内存和磁盘之间移动。此外，在HPC应用程序运行时，HPC节点可能有几个不同的位置，需要在这些位置来回移动数据。这项工作的新颖之处在于它的重点是通过支持“通信中的计算”来优化高性能计算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计算的可扩展性，包括分子动力学模拟和计算流体动力学代码等应用程序。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，以利用结合通信和计算的新兴技术；(3)开源软件基础设施，可用于促进并行计算、HPC和计算机体系结构在研究生和本科生层面的教育。该项目的技术目标包括几个关键组件。首先，它开发了新的算法来有效地利用通信中的计算范式。其次，它结合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、处理友好的压缩格式。第三，该项目设计了新颖的硬件增强功能，以优化通信计算。该项目通过一个专注于算法和压缩技术的开源库为通信计算创建了一个全栈解决方案，该库与架构设计共同设计，以支持对大数据输入的有效处理和操作。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高性能计算，从而最大限度地减少通信开销和数据移动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.