numa

Support and Optimization of Memory Management System for NUMA

内存管理系统对NUMA的支持及优化

NUMA has been floating around in research laboratories for years .

NUMA在研究实验室中已存在多年。

The Research and Implementation of Support and Optimization of Kylin Memory Management System for NUMA

Kylin存储管理系统的NUMA支持和优化技术研究与实现

Based on Kylin , we focus on the technology of memory management to support and optimize NUMA .

本文在Kylin操作系统基础上研究和实现其存储管理子系统对NUMA的支持和优化技术。

The result shows that this algorithm realizes NUMA affinity scheduling , improves data access locality and system performance .

实验证明，该算法较好地实现了具有节点亲近能力的NUMA调度，提高了数据访问的局部性，优化了系统性能。

In NUMA systems , each processor is close to some parts of memory and further from others .

在NUMA系统中，每个处理器距某部分内存较近而距其他内存较远。

NUMA Scheduling Algorithm Based on Affinitive Node

具有节点亲近能力的NUMA调度算法

Expression of NuMA in human glioma

核有丝分裂器蛋白NuMA在人脑胶质瘤中的表达

NUMA : Non-Uniform Memory Access Architecture

NUMA：非均匀存储器存取结构

On modern hardware , it makes no sense to work against the NUMA architecture that is designed for speed and scalability .

在现代硬件上，与专为高速和可伸缩性而设计的NUMA架构背道而驰是毫无意义的。

However , both of them cannot fully fit scalable parallel computers , especially MPP systems with NUMA architecture .

通过分析得出结论认为，这两种结构都不能很好地适应可扩展并行机尤其是NUMA结构并行机的需求。

NUMA servers today support 32 processors and eventually will be able to support up to 256 processors .

今天，NUMA支持32个处理器，最终将能支持多达256个处理器。

At the same time , the performance requirements of industry for virtualized environments multicore NUMA architecture servers are increasingly high .

与此同时，业界对于虚拟化环境下的多核NUMA架构服务器的性能要求也越来越高。

NUMA support ( Non-Uniform Memory Access , memory design for multiprocessors ) allows virtual machines to efficiently access large amounts of memory .

NUMA支持（非一致性内存访问，针对多处理器的内存设计）允许虚拟机有效地访问大量内存。

January and February were added around 700 BCE by Numa Pompilius to reflect a lunar calendar ;

一月和二月是在公元前700年左右，由第二代罗马国王怒马-庞皮利乌斯加上去的，是为了与阴历相匹配。

But the subsequent studies showed that the most of the SCNT embryos in monkey had normal distribution of NuMA .

后来的研究显示，大部分猴子体细胞核移植胚胎NuMA分布基本正常。

The main thing done in the relational database was to use " soft NUMA " and port mapping to get a good distribution of work within the system .

我们在关系型数据库中完成了被称为“SoftNUMA”的技术，它通过端口映射在系统内部得以获得良好的分布式工作效果。

NUMA reduces the contention for a system 's shared memory bus by having more memory buses and fewer processors on each bus .

NUMA通过在每个总线使用更多内存总线和更少处理器来减少系统共享内存总线的争用。

The scheduler was reported to have better performance on NUMA systems as well as mobile devices and was introduced into a derivative of the Android operating system .

该调度器据说在NUMA系统以及移动设备上具有更好的性能，并且被引入了Android操作系统的一款衍生产品中。

On a mail-server benchmark , we show a39 % improvement in performance by automatically splitting the application among multiple NUMA domains .

在邮件服务器的测试评分中，通过自动在多个NUMA域中切分应用程序，我们的性能得到了39%的提升。

Enabling memory location optimizations for NUMA multi-CPU systems ( - XX : + UseNUMA ) .

为NUMA多CPU系统启用内存位置优化（-XX：+UseNUMA）。

Applications that are processor intensive and access memory frequently will benefit the most since NUMA decreases the time it takes a processor to access a region in memory .

处理器密集型和内存访问频繁的应用程序将获得最大收益，因为NUMA减少了处理器访问内存区域的时间。

Modern hardware makes use of nonuniform memory access ( NUMA ), whereby a processor can access local memory faster than memory located remotely , on another bus .

现代硬件利用了非一致内存访问（NUMA），因此，处理器访问本地内存的速度比访问位于另一个总线上的远程内存的速度更快。

While applications in general will see performance gains from the kernel level NUMA support , additional gains may be achieved by compiling against these user-land APIs .

尽管从总体上看应用程序都能够从内核级别NUMA支持获得性能改进，但是重新编译这些user-landAPI还可以获得进一步的性能改进。

The team has developed kernels for XScale programmable IO cards , using an ARM architecture , and cache-coherent NUMA architectures .

该团队已经为XScale可编程IO卡（使用ARM架构）和具有缓冲一致性（cache-coherent）的NUMA架构编写了内核。

With the development and popularity of virtualization technology and multi-core NUMA architecture , the multi-core NUMA architecture commercial server based on virtualization has increasingly been widespread used .

随着虚拟化技术和多核NUMA架构发展和普及，基于虚拟化环境的多核NUMA架构商用服务器越来越受到广泛的重视以及使用。

Be wary of the options and settings that your virtualization software might provide , because it 's often easy to counteract the inherently efficient NUMA architecture .

请提防您的虚拟化软件可能提供的选项和设置，因为它常常很容易与本来高效的NUMA架构相抵触。

In this paper , we design and implement a comprehensive testing program to verify the CNA system to a virtual machine environment of multicore NUMA systems have a significant performance boost .

本文设计和实施了详尽的测试方案验证了CNA系统对虚拟机环境下多核NUMA系统有显著的性能提升。

The scalability of system is a puzzle to design high performance computers . NUMA ( non - uniform memory architecture ) architecture is proposed for scalable system underlying a shared memory pattern .

高性能计算机系统的可扩展性是系统设计的一大难题，NUMA（non-uniformmemoryarchitecture）结构正是为了解决共享存储体系的可扩展性问题而提出来的。

Through the analysis of both directions , we conclude that in the near future , the research on locality exploitation on NUMA will become one of the key points in the thread environment research .

通过对两个方向上的研究动态的分析，本文预计基于NIMA的局部性开发将成为线程环境研究的热点。