SPX: Collaborative Research: Cross-stack Memory Optimizations for Boosting I/O Performance of Deep Learning HPC Applications

新的计算应用正在智能网络、科学探索、业务管理、安全和医疗保健中出现。这些应用程序依赖于非常大量的数据。必须以快速有效的方式使用这些数据。使用大型超级计算机来分析此类数据的情况正在增加。他们使用的技术被称为深度学习（DL）高性能计算（HPC）。研究人员正在使用DL HPC来理解这些大量的数据并获得有用的信息。要做到这一点，他们必须重新设计高性能计算系统。一个关键的挑战是如何大规模地使用数据存储和计算机内存等资源。该项目将构建Metis，这是一个高性能数据存储系统，使用新的端到端硬件支持的内存和存储设计，以满足DL HPC应用的需求。目标是满足下一代超级计算机不断提高的数据管理性能所带来的挑战。该项目将连接几个不同的计算社区，并增加它们之间的互动。该项目包括教育和参与活动，这将大大增加社区对高性能计算系统的理解。这些活动包括扩大参与活动，以吸引和留住新学生。将特别重视来自代表性不足群体的学生。该项目将鼓励学生对大型计算系统设计的设计和研究感兴趣。该项目汇集了微架构、分布式计算系统（即云和HPC系统）、存储系统和功率/能量建模方面的研究人员，以提高DL HPC数据处理性能。这项研究将产生一种全新的软硬件协同设计的内存压缩技术，该技术可以透明地压缩DL应用程序内存，而运行时性能开销可以忽略不计。Metis将利用新的压缩基板实现分布式、智能、操作系统级数据缓存，有效利用程序内存压缩释放的物理内存。开发的技术将为创新的高性能计算和广泛学科的科学应用打开大门，这在以前是不可能的。Metis专注于解决在百亿亿次时代提高性能的挑战，同时吸引来自多个领域的研究人员，这与SPX项目的目标和目标非常一致。此外，该研究还将创造有关内存压缩设计原则的新知识，并为将深度学习应用程序无缝集成到下一代深度学习感知超级计算机基础设施中提供见解。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。

New computing applications are emerging in smart networks, scientific explorations, business management, security, and healthcare. These applications depend on very large amounts of data. This data must be used in a fast and efficient manner. The use of large supercomputers to analyze such data is on the rise. The techniques they use are referred to as deep learning (DL) high-performance computing (HPC). Researchers are using DL HPC to make sense of this flood of data and obtain useful information. To do this they must redesign HPC systems. A key challenge is how to use resources such as data storage and computer memory at a huge scale. This project will build Metis, a high-performance data storage system that uses new, end-to-end, hardware-supported memory and storage design to meet the needs of DL HPC applications. The goal is to satisfy the challenge posed by increasing data management performance for next-generation supercomputers. The project will connect several different computing communities and increase interactions among them. The project includes educational and engagement activities which will greatly increase the community's understanding of HPC systems. These activities include broadening participation activities to attract and retain new students. Special emphasis will be given to students from underrepresented groups. The project will encourage student interest in design and research in large-scale computing systems design.This project brings together researchers in micro-architecture, distributed computing systems, namely cloud and HPC systems, storage systems, and power/energy modeling to boost DL HPC data processing performance. The research will yield a fundamentally new software-hardware co-designed memory compression technique that transparently compresses DL application memories with negligible runtime performance overhead. Metis will leverage the novel compression substrate to enable a distributed, intelligent, operating-system-level data cache that effectively exploits the physical memory freed via program-memory compression. The developed techniques will open doors for innovative HPC and scientific applications in a broad range of disciplines, which have not been previously possible. Metis' focus on addressing the challenges of increasing performance in the Exascale era, along with engaging researchers from multiple areas, aligns it very well with the goals and objectives of the SPX program. Additionally, the research will also create new knowledge on design principles of memory compression, and yield insights to provide seamless integration of DL applications into the next-generation DL-aware supercomputer infrastructure.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.