HP Scalable Visualization Array Software - Manual (Page 11)

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
PC 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 11