HP Scalable Visualization Array Software - Manual (Page 17)

2 SVA Architecture
This chapter gives a detailed look at the architecture of the HP Scalable Visualization Array (SVA). It compares
the SVA to other clusters and describes the flow of data within the cluster.
SVA as a Cluster
It is important to understand the cluster characteristics of the SVA. These characteristics have implications
for how SVA functions. They also affect how applications take advantage of cluster features to achieve
graphical performance and display goals.
Background on Linux Clusters
In the taxonomy of parallel computers, the SVA is most similar to a Beowulf class Linux cluster. Beowulf
clusters have many servers of the same type that communicate on high speed connections such as channel
bonded Ethernet. In this way, the cluster provides high performance for applications capable of using parallel
processing. This type of cluster can provide exceptional computational performance.
A Beowulf cluster falls somewhere between the class of systems known as Massively Parallel Processors
(MPP) and a network of workstations (NOW). Examples of MPP systems include the nCube, CM5, Convex
SPP, Cray T3D, and Cray T3E. Beowulf clusters benefit from developments in both these classes of architecture.
MPPs are typically larger and have a lower latency interconnect than a Beowulf cluster. However, programmers
on MPPs must take into account locality, load balancing, granularity, and communication overheads to
obtain the best performance. Even on shared memory machines, many programmers develop programs that
use message passing. Programs that do not require fine-grain computation and communication can usually
be ported and run effectively on a Linux cluster.
Programming a NOW is usually an attempt to harvest unused cycles on an already-installed base of
workstations in a lab or on a campus. Programming in this environment requires algorithms that are extremely
tolerant of load balancing problems and large communication latency. Any program that runs on a NOW
runs at least as well on a cluster.
A Beowulf cluster is distinguished from a NOW by several subtle but significant characteristics. These
characteristics are shared by the SVA.
•
Nodes in the cluster are dedicated to the cluster. This helps ease load balancing problems because the
performance of individual nodes is not subject to external factors.
•
Because the System Interconnect (SI) is isolated from the external network, the network load is determined
only by the applications being run on the cluster. This eases problems associated with unpredictable
latency in NOWs.
•
All nodes in the cluster are within the administrative jurisdiction of the cluster. For example, the SI for
the cluster is less visible to the outside world. Often, the only authentication needed between processors
is for system integrity. On a NOW, network security is an issue.
Architectural Design
The SVA derives its most powerful attributes from its architectural design, which consists of a cluster of
visualization nodes, high-speed interconnects, and advanced graphics cards.
SVA runs parallel visualization applications efficiently. The SVA also is an integral part of the HP Cluster
Platform and storage (HP Scalable File Share) solutions. To accomplish this, the SVA architecture extends
the HP Cluster Platform architecture with the addition of visualization nodes, which you can use as specialized
compute nodes. Further, an SVA can be made up entirely of visualization nodes, or it can share an
interconnect with compute nodes and a storage system. Thus, the SVA provides the HP Cluster Platform with
a visualization component for those applications that require visualization in addition to computation.
The following sections describe the components that make up an HP Cluster Platform, followed by those tasks
and components that are unique to an SVA.
SVA as a Cluster 17