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.
Applying these lessons to HPC, you might ask, “how do I tinker with HPC?” The answer is far from simple. In terms of hardware, a few PCs, an Ethernet switch, and MPI get you a small cluster; or, a video card
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on a separate computer. The results can be combined when the job is finished because the map step has no dependencies. The popular mpiBLAST tool takes the same approach by breaking the human genome file
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standard “MPI is still great” disclaimer. Higher level languages often try to hide the details of low-level parallel communication. With this “feature” comes some loss of efficiency, similar to writing
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to build the HDF5 libraries since they will require an MPI library with MPI-IO support. MPI-IO is a low-level interface for carrying out parallel I/O. It gives you a great deal of flexibility but also
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method to calculate the value of pi:
$ grep -v local /etc/hosts | cut -d" " -f2 > ~/hostfile
$ nano pi.py
$ mpirun -np 2 -hostfile hostfile python pi.py
3.14192133333
Listing 8: pi.py
01 from mpi4py
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to the question of how to get started writing programs for HPC clusters is, “learn MPI programming.” MPI (Message Passing Interface) is the mechanism used to pass data between nodes (really, processes).
Typically
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. Be sure to keep a sys on it.
Info
3D XPoint: https://en.wikipedia.org/wiki/3D_XPoint
Layton, J., and Barton, E. "Fast Forward Storage & IO," http://storageconference.us/2014/Presentations
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essential is support for parallel programming models such as OpenMP (Open Multiprocessing, a directive-based model for parallelization with threads in a shared main memory) and MPI (Message Passing Interface
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user.comment.name -v "Jeff Layton created this file" test.txt
The list of extended attributes for this file can be created:
$ getfattr test.txt
# file: test.txt
user.comment
user.comment.name
Now
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version 2.3, released in 1997, comprised a complete version of the benchmarks that used the Message Passing Interface (MPI), although the serial versions were still available.
In NPB release 3, three