CAREER: Rethinking System Stack for the Load-Store I/O Era

计算机硬件技术的快速发展对弥合硬件能力和系统软件之间的差距提出了挑战。随着从智能手机等日常设备到数据中心和超级计算机等复杂系统的数据生成和处理需求呈指数级增长，需要充分利用新硬件技术的潜力。该项目的重点是Compute Express Link(CXL)，这是一种尖端的互连技术，有望彻底改变内存、存储和计算设备在计算机内部和计算机之间的交互方式。CXL准备彻底改变内存和存储与近数据计算功能的集成。尽管CXL具有潜力，但当前的操作系统(OS)，包括广泛使用的平台(如Linux)，并未完全具备充分利用CXL功能的能力，从而导致硬件未得到充分利用和其他效率低下。这个职业项目致力于重新设计Linux内核，以优化其与CXL技术的兼容性。总体目标是开发一种新的加载存储I/O堆栈，该堆栈可以有效地管理启用CXL的内存、存储和计算设备。这一举措将带来更高的性能、资源效率和简化的编程要求。该项目涉及一种整体方法，将跨操作系统、运行时环境、应用程序和CXL设备的协同设计工作整合在一起。这项研究的结果预计将导致计算方面的重大进步，包括提高内存和存储利用率，用于高效数据管理的新型操作系统和运行时级技术，以及为基于CXL的设备定制的新编程模型的开发。通过开源所有开发的软件，该项目还承诺促进系统设计和计算机体系结构领域的跨学科合作。该项目还纳入了广泛的教育计划，以培养下一代计算机科学家，为学生提供最新的、研究驱动的学习和行业合作机会。最终，这项研究将为更高效、更具成本效益和更高性能的计算解决方案铺平道路，使广泛的数据密集型应用程序和服务受益，如数据分析、缓存、键值存储和机器学习。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The rapid evolution of computer hardware technology imposes challenges in bridging the gap between hardware capabilities and systems software. As data generation and processing needs grow exponentially across everyday devices like smartphones to complex systems like datacenters and supercomputers, there is a need to harness the full potential of new hardware technologies. This project focuses on Compute Express Link (CXL), a cutting-edge interconnect technology that promises to revolutionize how memory, storage, and computing devices interact within and across computers. CXL is poised to revolutionize the integration of memory and storage with near-data compute capabilities. Despite its potential, current operating systems (OS), including widely-used platforms like Linux, are not fully equipped to leverage CXL's capabilities, leading to underutilized hardware and other inefficiencies. This CAREER project endeavors to redesign the Linux kernel to optimize its compatibility with CXL technology. The overarching goal is to develop a new load-store I/O stack that can effectively manage CXL-enabled memory, storage, and compute devices. This initiative will result in enhanced performance, resource efficiency, and simplified programming requirements. The project involves a holistic approach, integrating co-design efforts across the OS, runtime environments, applications, and CXL devices. The outcomes of this research are expected to lead to significant advancements in computing, including improved memory and storage utilization, novel OS and runtime-level techniques for efficient data management, and the development of new programming models tailored for CXL-based devices. By open-sourcing all developed software, the project also promises to foster cross-disciplinary collaboration in the fields of system design and computer architecture. The project also incorporates extensive education plans to train the next-generation of computer scientists, providing students with up-to-date, research-driven learning and industry collaboration opportunities. Ultimately, this research will pave the way for more efficient, cost-effective, and high-performing computing solutions, benefiting a wide range of data-intensive applications and services, such as data-analytics, caching, key-value stores, and machine learning.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.