Please enlighten a baffled newbie: Now there are motherboards with 8 sockets; quad-core processors; and clusters with as many nodes as you can shake a stick at. It seems there are at least 3 dimensions for expansion. What (in your opinion) is the right tradeoff between more cores, more processors and more individual compute nodes?
In particular, I am thinking of in-house parallel finite difference / finite element codes, parallel BLAS, and maybe some commercial Monte-Carlo codes (the last being an embarrassingly parallel problem). I have been set the task of building our first cluster for these applications. Our existing in-house codes run on an SGI machine with a parallelizing compiler. They would need to be ported to use MPI on a cluster. However, I do not understand what happens when you have multi-processor/multi-core nodes in a cluster. Do you just use MPI (with each thread using its own non-shared memory) or is there any way to do "mixed-mode" programming which takes advantage of shared memory within a node (like, an MPI/OpenMP hybrid?). Peter Wainwright
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