Research on optimization of virtual machine memory access based on NUMA architecture

With the rapid development of big data and artificial intelligence(AI),the cloud platform architecture system is constantly developing,optimizing,and improving.As such,new applications,like deep computing and high-performance computing,require enhanced computing power.To meet this requirement,a non-uniform memory access(NUMA)configuration method is proposed for the cloud computing system according to the affinity,adaptability,and availability of the NUMA architecture processor platform.The proposed method is verified based on the test environment of a domestic central processing unit(CPU).

NUMA结构的高效实时稳定的垃圾回收算法

针对非一致性内存访问架构（NUMA）在垃圾回收（GC）过程中存在大量的远程内存读写导致GC性能降低的问题，对GC过程的各个阶段进行分析与研究，提出了一种基于NUMA结构的高效实时稳定的GC算法。该算法首先基于NUMA结构改进传统分代GC机制的堆空间布局，然后通过控制GC过程中扫描活跃对象阶段的初始根对象选取、动态负载均衡阶段截取任务队列的选取以及复制活跃对象阶段对象复制位置的选取，大大减少GC过程中的远程访问次数。这种改进的GC机制对所有NUMA结构具有通用性。以Godson-3处理器的NUMA平台为例进行的实验结果显示，优化的GC机制极大地缩短了GC的时间，而且提高了应用程序的性能以及稳定性。在SPECjvm2008测试中，GC时间平均缩短了14．6％（GC总时间缩短4．1％-41．58％），应用程序的性能平均提升了4．68％（最高提升17．8％），应用程序的性能稳定性提升了76．2％。

NUMA架构下数据热度的内存数据库日志恢复技术

在内存数据库故障恢复技术中,命令日志是针对内存数据库设计的粗粒度的、轻量级的日志记录方式。但在非统一内存访问(non-uniform memory access,NUMA)体系架构下面向数据的数据库设计中利用命令日志进行故障恢复时,由于数据访问频率不均衡,导致负责高频数据恢复的CPU线程负载加重,而其他CPU相对空闲。针对这种工作负载不均衡所导致的恢复时间开销增大的情况,提出了NUMA体系架构下基于热度记录的内存数据库日志恢复算法。该算法中,每一条数据的访问次数作为该数据的热度记录下来。在并行恢复时,根据数据热度,将数据比较均衡地划分到各个节点的CPU线程执行恢复操作,以此来提高数据库的恢复速度。实验结果表明,该方案比NUMA架构下的常规恢复方案快,而且数据的热度越高,恢复速度的提升越明显,最高提升了19%。

NUMA架构的龙芯3A板级设计及工程化技术研究

针对关键应用对信息处理能力提出的性能要求以及国产化需求,在分析龙芯3A处理器架构特点的基础上,设计了基于NUMA并行处理架构的龙芯3A高性能处理模块,并对抗恶劣环境的关键问题进行了分析和设计,解决了散热、电源监控及供电优化、启动速度等问题。通过测试验证,性能可以满足关键应用对信息处理能力的要求,从而有效解决了龙芯3A访存能力有限的问题。同时对SMP和NUMA架构下,龙芯3A处理器CPU数量的增加对访存性能的提升的关系进行了探讨。

MPD:结点具有多个并行缓存一致性域的CC-NUMA系统

大规模高速缓存一致性非均匀存储访问(cache coherence non-uniform memory access,CC-NUMA)系统通常采用两级一致性域方法来降低缓存一致性协议维护开销,提升系统性能.两级一致性域系统中,多个处理器互连,形成结点内一致性域;多个结点互连,形成结点间一致性域.然而,受限于处理器直连能力与处理器可识别ID数,系统的单结点规模有限,系统规模的扩展不得不依靠增加结点数来实现,使得大规模CC-NUMA系统的结点间互连复杂度上升,跨结点访问带宽和延迟急剧增长,影响了系统性能的有效扩展.MPD系统通过在结点内构建多个并行缓存一致性域,突破了处理器直连能力与可识别ID数对单结点规模的限制,能够大幅减少结点数量,并将部分结点间访问转化为结点内访问,实现系统性能的有效扩展.理论分析和实验结果表明:采用同规格处理器的32路系统中,结点内4个并行缓存一致性域的MPD系统可实现结点数目减少75%、一致性目录存储开销节省40%以上、平均访问延迟降低约27.9%、系统整体性能提升约14.4%.

基于CPU与GPU的异构模板计算优化研究

模板计算是一类使用固定模板的算法,被广泛应用于图像处理、计算流体动力学模拟等领域,现有的模板计算存在计算并行度弱、缓存命中率低、无法充分利用计算资源等问题。在消息传递接口(MPI)计算模型和跨平台多线程(OpenMP)计算模型的基础上提出MPI+OpenMP、统一计算设备架构(CUDA)+OpenMP两种混合计算模型。相较于常规的MPI计算模型,MPI+OpenMP计算模型通过使用MPI进行多节点之间的粗粒度通信,使用OpenMP实现进程内部的细粒度并行计算,并结合单指令多数据、非一致内存访问、数据预取、数据分块等技术,提高模板计算过程中的缓存命中率与计算并行能力,加快计算速度。在只采用CUDA进行模板计算时,CPU的计算资源没有得到充分利用,浪费了大量计算资源,CUDA+OpenMP计算模型通过对计算任务的负载划分让CPU也参与到计算中,以减少通信开销及充分利用CPU的多核并行计算能力。实验结果表明,OpenMP+MPI计算模型相较于MPI计算模型的平均加速比为3.67,CUDA+OpenMP计算模型相较于CUDA计算模型的平均加速比为1.26,OpenMP+MPI和CUDA+OpenMP两种计算模型的性能均得到了显著提升。

Scaling out NUMA-Aware Applications with RDMA-Based Distributed Shared Memory

The multicore evolution has stimulated renewed interests in scaling up applications on shared-memory multiprocessors,significantly improving the scalability of many applications.But the scalability is limited within a single node;therefore programmers still have to redesign applications to scale out over multiple nodes.This paper revisits the design and implementation of distributed shared memory (DSM)as a way to scale out applications optimized for non-uniform memory access (NUMA)architecture over a well-connected cluster.This paper presents MAGI,an efficient DSM system that provides a transparent shared address space with scalable performance on a cluster with fast network interfaces.MAGI is unique in that it presents a NUMA abstraction to fully harness the multicore resources in each node through hierarchical synchronization and memory management.MAGI also exploits the memory access patterns of big-data applications and leverages a set of optimizations for remote direct memory access (RDMA)to reduce the number of page faults and the cost of the coherence protocol.MAGI has been implemented as a user-space library with pthread-compatible interfaces and can run existing multithreaded applications with minimized modifications.We deployed MAGI over an 8-node RDMA-enabled cluster.Experimental evaluation shows that MAGI achieves up to 9.25:4 speedup compared with an unoptimized implementation,leading to a sealable performance for large-scale data-intensive applications.

PsmArena:Partitioned Shared Memory for NUMA-Awareness in Multithreaded Scientific Applications

The Distributed Shared Memory(DSM)architecture is widely used in today’s computer design to mitigate the ever-widening processing-memory gap,and it inevitably exhibits Non-Uniform Memory Access(NUMA)to shared-memory parallel applications.Failure to adapt to the NUMA effect can significantly downgrade application performance,especially on today’s manycore platforms with tens to hundreds of cores.However,traditional approaches such as first-touch and memory policy fall short in false page-sharing,fragmentation,or ease of use.In this paper,we propose a partitioned shared-memory approach that allows multithreaded applications to achieve full NUMA-awareness with only minor code changes and develop an accompanying NUMA-aware heap manager which eliminates false page-sharing and minimizes fragmentation.Experiments on a 256-core cc-NUMA computing node show that the proposed approach helps applications to adapt to NUMA with only minor code changes and improves the performance of typical multithreaded scientific applications by up to 4.3 folds with the increased use of cores.

PC服务器新发展

本文叙述PC服务器新发展动向和各个不同厂家的新PC服务器产品的特点,对先进的对称多处理机/缓存相干非均匀存储器访问体系结构作出较详细的介绍。