Hp Scalable Visualization Array Software Bedienungsanleitung PDF herunterladen (Seite 13)

1 Introduction

This chapter gives an overview of the HP Scalable Visualization Array (SVA). It describes how

the SVA works within the context of overall HP cluster solutions. It also discusses attributes of

the SVA that make it a powerful tool for running data intensive graphics applications.

The SVA is a scalable visualization solution that brings the power of parallel computing to bear

on many demanding visualization challenges.

The SVA leverages the advances made across the industry in workstation class systems, graphics

technology, processors, and networks by integrating the latest generations of these components

into its clustering architecture. This base of scalable hardware underlies powerful Linux clustering

software from HP. It is further enhanced by a set of utilities and support software developed by

HP and its partners to facilitate the use of the system by new and existing user applications.

1.1 Where SVA Fits in the High Performance Computing Environment

The SVA is an HP Cluster Platform system. It can be a specialized, standalone system consisting

entirely of visualization nodes, or it can be integrated into a larger HP Cluster Platform system

and share a single System Interconnect with the compute nodes and a storage system. Either

way, the SVA can integrate seamlessly into the complete computational, storage, and display

environment of customers as shown in Figure 1-1.

Figure 1-1 System View of a Computing Environment with Integrated SVA

Cluster System Interconnect

Compute Compute Compute Compute Compute

Visualization Visualization Visualization

HP SFS

Remote

Display Surface

High-speed networks make feasible the transfer of large amounts of data among the following:

• Individual users at their desktops, or logged into a cluster.

• The compute nodes, the visualization nodes, and local and remote display devices.

• Servers that are part of data storage farms.

A typical usage model for the type of system shown in Figure 1-1 has the following characteristics:

• A compute intensive application, for example, an automobile crash test simulation, runs on

the supercomputing compute nodes of the cluster.

• The large dataset generated on the compute nodes can be stored in the storage servers for

later retrieval, or directed in realtime for rendering on the SVA portion of the overall system.

• One or more users can log into the SVA concurrently, which allocates resources efficiently

to meet the rendering and display requirements of each user application.

• Users’ visualization applications use parallel programming techniques and visualization

middleware software to distribute their graphical rendering across the SVA nodes, each of

which in turn renders a portion of the output for the final image. Image data can be

apportioned by a master application to a set of visualization nodes for rendering.

• Each portion of the final image rendered by a visualization node is sent to a tile of a single

or multi-tile display. The complete image is available for display locally. The complete image

1.1 Where SVA Fits in the High Performance Computing Environment 13

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