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Recent trends in computing are toward more cores doing more tasks at once. These days, you are likely to have a dual- or quad-core CPU in your laptop, and perhaps 4, 6, 12, or 16 cores in your
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07.11.2011
to the created subprocesses in a few different ways. To see these concepts in action, run the code shown in Listing 4 (miscellaneous.py
).
Listing 4: miscellaneous.py
01 #! /usr/bin/env python2.7
02
03 import
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. The speed-up increases from 1.00 with one process to 4.71 using 64 processes. Of course, the wall clock time for the serial portion of the application does not change; it stays at 200 seconds regardless
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that executed on the GPUs and produced massive speedups for many applications.
Starting with OpenMP 4.0, the OpenMP standard added directives that targeted GPUs and has expanded on this capability
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Enterprise Linux Installation Guide [3] and the section on Kickstart installations [4] are important reading.
The basic steps for getting your HPC cluster up and running are as follows:
Create the admin
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your code with auto-vectorization (so you don’t have to vectorize by hand).
Starting in OpenMP 4.0, the directive omp simd
was introduced. It basically gives a hint to the compiler to look for places
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add_ufunc(x, y):
return x + y
The decorator line defines the data types (i.e., int64
here) and the target for the decorator cuda
. A simple test for the add_ufunc
Numba function is:
a = np.array([1, 2, 3, 4])
b
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(32-bit).
The examples used in this column were built and run on a Limulus personal cluster running Scientific Linux 6.2 on an Intel i5-2400S with 4GB of memory. If don't want to bother building
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in Figure 4 shows the example of a case where the user has configured two compression logical instances for a process called SSL. The userspace process may then access these logical instances by calling cpa
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04.11.2011
cases, administrators turn to projects such as Parallel SSH [4] to handle SSH automation for cluster administration.
Another important part of administration is hardware monitoring to identify