EAGER: Adaptive Shared Memory Management for Heterogeneous CPU-GPU Architectures

如今，为亿级计算构建高效的存储系统已变得极其具有挑战性。在亿级计算环境中运行的应用程序正变得越来越多样化和复杂，这使得管理共享内存系统变得尤为困难。图形处理单元(GPU)正在作为加速器进入亿级计算领域，这给共享内存带来了新的技术挑战。非易失性存储器(NVM)在存储器/存储层次结构中具有广阔的前景。它可以用来扩大内存容量和/或提高能效。然而，如何将NVM有效地集成到现有的存储体系结构中，以构建一个高效的存储系统，仍然是一个悬而未决的问题。该项目提出了一种自适应共享内存管理方案，以解决异构型CPU-GPU架构中的内存干扰问题。将开发一个基于混合存储器模型的灵活框架，用于将非易失性存储器集成到共享存储器系统中。此外，该项目还将探索为异类架构构建统一地址空间的设计。该项目解决了探索用于异类体系结构的共享内存管理方案方面的挑战。所提出的同构和异质体系结构中的内存干扰管理技术能够很好地适应亿级计算环境中的高并发性和大不同的干扰模式。这些技术为管理异类系统的共享内存提供了引人注目的解决方案。所提出的工作可以作为一个起点，以克服大规模存储和计算机系统设计方面的大数据和亿级计算挑战。此外，拟议的研究将有助于内存密集型应用的服务器集群最有效地利用这些现有/新兴的架构和系统技术来应对异类CPU-GPU系统中的内存挑战。拟议的高效共享内存管理可以使大数据分析、生物、化学、地球科学等众多内存密集型应用受益。此外，该项目将把研究和教育结合在一起。它将为本科生和研究生提供参与研究的机会，并帮助培养高性能计算领域的新一代计算机科学家和工程师。

Building efficient memory systems for exascale computing has nowadays become extremely challenging. Applications running in exascale computing environments are becoming increasingly diverse and complicated, making managing shared memory system particularly difficult. Graphic Processing Units (GPUs) are entering exascale computing as accelerators, imposing new technical challenges in sharing memory. Non-volatile memory (NVM) has promising future in the memory/storage hierarchy. It can be used to enlarge memory capacity and/or improve energy efficiency. However, how to efficiently integrate NVM into current memory architecture to build a highly efficient memory system remains an open problem. This project proposes an adaptive shared memory management scheme to address the memory interference problem in heterogeneous CPU-GPU architectures. A flexible framework based on a hybrid memory model for integrating non-volatile memory into a shared memory system will be developed. In addition, the project will explore designs to build a unified address space for heterogeneous architectures. This project addresses the challenges in exploring shared memory management schemes for heterogeneous architectures. Proposed techniques of management for memory interference in both homogeneous and heterogeneous architectures are well amenable to the high concurrency and widely different interference patterns in exascale computing environments. These techniques provide a compelling solution to managing shared memory for heterogeneous systems. The proposed work can serve as a starting point to conquer the bigdata and exascale computing challenges in terms of large-scale memory and computer system design. Moreover, the proposed research will facilitate the server clusters for memory-intensive applications to most effectively utilize those existing/emerging architecture and system technologies to tackle the memory challenge in heterogeneous CPU-GPU systems. The proposed efficient shared memory management can benefit numerous memory-intensive applications such as big data analytics, biology, chemistry, earth science, etc. In addition, the project will integrate research and education together. It will provide opportunities for undergraduate and graduate students to participate in the research and help train a new generation of computer scientists and engineers in the area of high-performance computing.