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Chapter 1
1.
SGI Altix XE1300 Cluster Quick-reference
Overview
Your SGI
®
Altix
®
XE1300 cluster system ships with a variety of hardware and software
documents in both hard copy and soft copy formats. Hard copy documents are in the packing box
and soft copy documents are located on your system hard disk in both
/usr/local/Factory-Install/Docs
and
/usr/local/Factory-Install/CFG
Additional third-party documentation may be shipped on removable media (CD/DVD) included
with your shipment.
This document is intended as an overview of some of the common operations that system
administrators may have to perform to set-up, boot, re-configure (upgrade) or troubleshoot the
SGI Altix XE1300 cluster.
The SGI Altix XE1300 cluster is a set of SGI Altix 1U-high servers (compute nodes), and one or
more SGI Altix 2U-high servers (head nodes) networked together, that can run parallel programs
using a message passing tool like the Message Passing Interface (MPI). Systems ordered prior to
the second quarter of 2008 generally use SGI Altix 310 servers as compute nodes and SGI Altix
XE240 servers as administrative head nodes. Most XE1300 clusters ordered in 2008 use SGI Altix
XE320 servers as compute nodes and SGI Altix XE250 servers as administrative head nodes.
Consult with your SGI support representative before swapping nodes between pre-existing and
newer clusters.
The XE1300 cluster is a distributed memory system as opposed to a shared memory system like
that used in the SGI Altix 450 or SGI Altix 4700 high-performance compute servers. Instead of
passing pointers into a shared virtual address space, parallel processes in an application pass
messages and each process has its own dedicated processor and address space. Just like a
multi-processor shared memory system, a cluster can be shared among multiple applications. For
instance, one application may run on 16 processors in the cluster while another application runs
on a different set of 8 processors. Very large clusters may run dozens of separate, independent
applications at the same time.
Typically, each process of an MPI job runs exclusively on a processor. Multiple processes can
share a single processor, through standard Linux context switching, but this can have a significant
effect on application performance. A parallel program can only finish when all of its sub-processes
have finished. If one process is delayed because it is sharing a processor and memory with another
application, then the entire parallel program is delayed. This gets slightly more complicated when
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