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Full Discussion: High Performance Computing
Special Forums UNIX and Linux Applications High Performance Computing High Performance Computing Post 302278833 by otheus on Wednesday 21st of January 2009 09:30:23 AM
Old 01-21-2009
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Originally Posted by humbletech99
1. CPU intensive computation of a single task
Q1: What percentage of the operations are floating point? Do you need double-precision? (Usually the answer is yes).

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2. Parallel computation of a task broken down into pieces
What's the expected ratio between computation time and communication time between the pieces. Medium ratio: do some computation, then send intermediate results to all neighbors, then do some more computation. Low ratio: compute, send a result, wait for a message, compute, send a result, and so on. High ratio: the CPUs crunch, crunch, crunch, then finally send results to a central task which does a final computation.

This is important in deciding what kind of network capacity you will need.
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3. Storage across many commodity nodes with scalability and i/o performance
How about reliability? Commodity nodes means high rate of disk failures and/or node failures. Can you bear with frequent filesystem downtime? Or will you need high availability on this filesystem?

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4. The solutions do not need to be geographically dispersed, same server room is fine.
Does your budget include life operating costs? Does your server room have specifications for lb/ft^2 ? One institution I worked at discovered that the building was designed for a certain amount of weight density -- even in the server room. It turns out that putting more than about 8 computer racks in the room exceeded this density! So we had the room, but adding more racks might make the floor unstable, especially given that this building was in a geographically active area (about 1 4+ quake every 2 to 3 years).
 

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CLUSTER(1)						      General Commands Manual							CLUSTER(1)

NAME
cluster - find clusters in a graph and augment the graph with this information. SYNOPSIS
cluster [-v?] [ -Ck ] [ -ck ] [ -o outfile ] [ files ] DESCRIPTION
cluster takes as input a graph in DOT format, finds node clusters and augments the graph with this information. The clusters are specified by the "cluster" attribute attached to nodes; cluster values are non-negative integers. cluster attempts to maximize the modularity of the clustering. If the edge attribute "weight" is defined, this will be used in computing the clustering. OPTIONS
The following options are supported: -Ck specifies a targeted number of clusters that should be generated. The specified number k is only a suggestion and may not be real- isable. If k == 0, the default, the number of clusters that approximately optimizes the modularity is returned. -ck specifies clustering method. If k == 0, the default, the modularity will be used. If k == 1 modularity quality will be used. -ooutfile Specifies that output should go into the file outfile. By default, stdout is used. -v Verbose mode. EXAMPLES
Applying cluster to the following graph, graph { 1--2 [weight=10.] 2--3 [weight=1] 3--4 [weight=10.] 4--5 [weight=10] 5--6 [weight=10] 3--6 [weight=0.1] 4--6 [weight=10.] } gives graph { node [cluster="-1"]; 1 [cluster=1]; 2 [cluster=1]; 3 [cluster=2]; 4 [cluster=2]; 5 [cluster=2]; 6 [cluster=2]; 1 -- 2 [weight="10."]; 2 -- 3 [weight=1]; 3 -- 4 [weight="10."]; 4 -- 5 [weight=10]; 5 -- 6 [weight=10]; 3 -- 6 [weight="0.1"]; 4 -- 6 [weight="10."]; } AUTHOR
Yifan Hu <yifanhu@research.att.com> SEE ALSO
gvmap(1) Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment (2008), P10008. 3 March 2011 CLUSTER(1)
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