This feature can be used to host compute-intensive or HPC-type applications inside a WPAR environment in order to confine certain processes to a particular set of processors.
可以使用这个特性在WPAR环境中驻留计算密集型或hpc类型的应用程序，让某些进程只能在指定的一组处理器上运行。
To understand HPC hardware, it is useful to contrast the vector computing paradigm with the cluster paradigm.
要理解HPC硬件，对向量计算和集群计算进行一下比较是非常有用的。
The owner of the workstation establishes what privileges the HPC application will have when running.
工作站的所有者可以决定HPC应用程序在运行时会拥有什么权限。
When an HPC image is loaded, the VCL manager scheduler recognizes it and begins to assign it work.
当HPC映像被加载后，VCL管理器调度程序将识别它并开始为它分配工作。
Processes using large percentages of memory should be examined further, although remember that database and HPC applications can use the entire memory block as normal and could be ignored.
应该进一步检查使用的内存百分比高的进程，但是应该记住数据库和HPC应用程序在正常情况下可以使用大量内存，可以不理会它们。
In an HPC environment, VCL provides public access only through login nodes.
在HPC环境中，VCL只通过登录节点提供公共访问。
Most HPC systems use the concept of parallelism.
大部分HPC系统都使用了并行的概念。
Even then, this was really more of a stunt than a demonstration that the HPC Server system is ready to compete with the big boys.
尽管那样，这实际上不只是一次示范准备与大亨竞争的高性能服务器系统的特技表演。
There are mainly two implications for the High Performance Computing (HPC) level of machine utilization in universities.
大学中对高性能计算(HPC)级别机器的使用主要有两个含义。
The two common hardware platforms used in HPC are Shared memory systems and distributed memory systems.
HPC中使用的两种主要的硬件平台是共享内存系统和分布式内存系统。
The integration of HPC in VCL significantly increases resource utilization by the reuse of blade servers.
通过重用刀片服务器，VCL 与HPC的集成显著提高了资源利用率。
Two important HPC patterns have been incorporated into Compute Grid.
计算网格中合并了两种重要的HPC模式。
The master HPC login node image includes components of the HPC scheduler.
主hpc登录节点映像包括HPC调度程序的组件。
Also, using the VCL blades for both HPC and VCL desktops provides economical services with optimum use of resources.
同样，对HPC和VCL桌面计算机使用VCL刀片服务器则以最理想的资源利用率提供了经济的服务。
Part 1 of this series, clustering fundamentals, discusses the types of clusters, USES of clusters, HPC fundamentals, and reasons for the growth of clustering technology in High Performance Computing.
本系列的第1篇集群基础讨论了集群的类型、用途、HPC基础以及集群技术在HPC领域变得更加流行的原因。
Independently, each computing paradigm - HPC, XTP, grid, and utility computing - provides unique advantages for enterprise computing.
单独地来看，各种计算范例(HPC、XTP、网格和效用计算)都提供了独特的企业计算优势。
Many software platforms are oriented for HPC, but first let's look at the hardware aspects.
有很多软件平台都是面向HPC的，但是首先让我们先来了解一下硬件的知识。
The two largest challenges in using HPC are programming the HPC application itself and ensuring that you can get enough computing power to do the job.
利用HPC有两大挑战，一是HPC应用编程本身，二是确保你能获得足以完成工作的计算能力。
The basic working model of VCL on HPC services is fairly simple.
VCL中的HPC服务的基本工作模型非常简单。
The second HPC pattern, divide-and-conquer, is also known as highly parallel execution.
第二种HPC模式，即分治模式，也称为高并行执行。
It's mainly going to be used in the HPC space to improve computational speed.
这主要应用于改善HPC(高精度计算)领域的运算速度。
As you saw in Part 1, HPC is mostly about parallel programming.
正如在第1部分中可以看到的一样，HPC大部分都是有关并行编程的。
The basic working model of HPC in a VCL is as shown in Figure 7.
VCL中的HPC的基本工作模型如图7所示。
In short, Windows HPC isn't ready for prime-time.
总之，WindowsHPC还没有为黄金时间作好准备。
Support for HPC clusters and MPI, including debugging support
支持HPC集群和MPI，包括调试的支持
Each HPC client image has access to a large amount of storage (in terabytes), as well as user home directories and HPC backup storage.
每个HPC客户机映像可以访问大量的存储(以TB为单位)，以及用户主目录和HPC备份存储。
Beside numerical and symbolic computational support, Mathematica includes image processing, parallel High-Performance Computing (HPC), interactive documents, and others.
除了支持数值和符号计算，Mathematica还包括图像处理、并行的高性能计算(HPC)、交互式文档及其它内容。
Ganglia is a scalable distributed monitoring system for HPC clusters and grids that is based on a hierarchical design targeted at federations of clusters.
Ganglia是一种可伸缩的分布式监视系统，用于监视以集群联合体为目标的采用层次化设计的HPC集群和网格。
High Performance Computing (HPC) and Life Science developers will benefit from the availability of common applications, for example NCBI applications.
高性能计算(HPC)以及生命科学(Life Science)开发者将受益于可用的通用应用程序，例如NCBI应用程序。
Advances developed by the HPC program will complement DARPA's Ubiquitous High Performance Computing program.
无所不在(omnipresent)高性能计算项目进展将补充DARPA的Ubiquitous高性能计算项目。
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