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

科学的进步往往依赖于计算机技术提供更快的计算机能够处理不断增加的数据量。然而，当前计算机系统的存储器容量和密度的增长处于危险之中，因为动态随机存取存储器（DRAM）（当前占主导地位的主存储器技术）在扩展方面面临严重的障碍。非易失性存储器或持久性存储器是一种新兴的替代技术，其提供高集成密度、与当前主存储器类似的速度、与当前主存储器类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，预期持久存储器将越来越多地增加或取代DRAM作为主存储器，并且还预期这种变化将发生在基于图形处理单元（GPU）的计算系统中，所述基于GPU的计算系统是用于高性能计算的主导加速器。然而，为了充分发挥其潜力，需要对GPU上的持久化模型进行研究。该项目研究集成软件和硬件技术，使GPU能够有效地利用非易失性存储器。该项目的成功结果将通过减少文件访问所涉及的管理费用来加快数据访问速度。所产生的软件（持久性GPU基准测试，编译器和调谐器）和原型平台将提供给其他研究人员。该项目的教育和推广活动旨在培养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持久内存（PM）作为设备内存的GPU上实现高效的持久化编程？研究成果包括：（1）一个开源的GPU PM基准测试套件，它代表了各种应用领域;（2）探索GPU和指令集架构支持中的持久化模型;（3）通过消除日志记录对持久化模型的优化;（4）编译器通道和性能调节器，用于自动确定性能最佳的存储器持久化和恢复模型，该奖项反映了NSF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。

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.