SHF: Small: Collaborative Research: Efficient Memory Persistency for GPUs

科学进步往往有赖于计算机技术提供越来越快的计算机，能够处理越来越多的数据。然而，当前计算机系统的存储容量和密度的增长处于危险之中，因为当前占主导地位的主存储技术动态随机存取存储器(DRAM)在扩展方面面临着严重的障碍。非易失性存储器或永久存储器是一种新兴的替代技术，它具有高集成密度、与当前主存储器相似的速度、与当前主存储器相似的字节寻址能力、以及比当前主存储器更低的待机功率。因此，永久存储器有望逐渐增加或取代DRAM作为主存储器，这种变化也有望发生在基于图形处理单元(GPU)的计算系统中，这些计算系统是高性能计算的主要加速器。然而，为了充分发挥其潜力，需要研究基于GPU的持久化模型。该项目研究集成的软件和硬件技术，以使GPU能够有效地使用非易失性存储器。该项目的成功成果将通过减少与文件访问有关的管理费用而导致更快地访问数据。制作的软件(永久GPU基准、编译器和调谐器)和原型平台将提供给其他研究人员使用。该项目的教育和推广活动旨在培训这一学科的下一代程序员。该项目的研究回答了这样一个问题：在GPU系统上，需要什么架构支持才能在以永久存储器(PM)为设备存储器的GPU上实现高效的持久性编程？研究贡献包括：(1)代表各种应用领域的开源GPU PM基准测试套件；(2)探索GPU中的持久性模型和指令集体系结构支持；(3)通过消除日志记录对持久性模型进行优化；(4)编译器通道和性能调整器，以自动确定最佳性能的内存持久性和恢复模型，并相应地转换代码。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。

Scientific progress often depends on computer technology providing ever faster computers capable of processing ever increasing amounts of data. The growth in memory capacity and density of current computer systems, however, is in peril as Dynamic Random Access Memory (DRAM), the current dominant main memory technology, faces serious roadblocks in scaling. Non-volatile memory or persistent memory is an emerging alternative technology that offers high integration density, speed similar to current main memory, byte addressability similar to current main memory, and lower standby power than current main memory. Hence, persistent memory is expected to increasingly augment or replace DRAM as main memory, and such a change is also expected to happen in Graphics Processing Unit (GPU) based computing systems which are the dominant accelerators for high performance computing. However, in order to fully realize its potential, research on persistency models on GPUs is needed. This project investigates integrated software and hardware techniques to enable GPUs to make efficient use of non-volatile memory. Successful outcomes of this project will lead to faster access to data by reducing overheads involved with file access. The software produced (persistent GPU benchmarks, compiler, and tuner) and prototyping platform will be made available to other researchers. Education and outreach activities in this project seek to train the next generation of programmers in this discipline.The research in this project answers the question: on a GPU system, what architecture supports are needed to achieve efficient persistency programming on GPUs with persistent memory (PM) as their device memory? The research contributions include: (1) an open-source GPU PM benchmark suite that is representative of various application domains; (2) an exploration of persistency models in GPUs and Instruction Set Architecture support; (3) optimizations on the persistency models by removing the need for logging; (4) a compiler pass and performance tuner to automatically determine the best-performing memory persistency and recovery model, and transform the code accordingly.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.