A number of development tools, libraries, and applications that are requested by multiple HPC users are also installed/compiled and managed by the IT HPC staff. IT is solely responsible for maintaining the OS on the HPC systems. |**applications**|In HPC, a program that facilitates computation or data analysis|Matlab, Gaussian|
|**libraries**|Reusable program fragments that provide additional functionality to programs|OpenMPI, ScaLAPACK|
|**development**|Programs that are used to create and refine other programs|Portland Compiler Suite, GNU Debugger (gdb)| |**operating system (OS)**|Programs that collectively provide a working environment for computation|RedHat Enterprise Linux (RHEL)| There are several categories of software involved: Then you have a sufficient number of time steps to compute the RMS without transient data.IT maintains a wide variety of software on the clusters it supports. Continue to run the software until the window averaging of the FFTs converge to a constant signature. As you add each window and then average, if the frequency signature changes considerably, then you have not eliminated the transients. Perform an FFT on each window, then sum and average each FFT together to get one window. If you have 100,000 time steps, try 25,000 time steps each, where the first window is 1-25k, the second is 12.5k-37.5k, the third is 25k-50k, the fourth is 37.5k-62.5k, etc. Simply take your time accurate data set (preferably second order accurate in time) and divide it into windows of equal size. I recommend looking at Welch's method for windowing.
In theory, if the window size is sufficiently sized, there will be very little variance between each window. To do the FFT, you'll want to use multiple segments of the data that over lap. The second is to perform a Fast Fourier Transform (FFT) analysis of the data. The first is an RMS (root mean square) of whatever parameter you are targeting. The best way I've seen this done is to compute two quantities. One of the more perplexing issues is determining if the transient data is eliminated from your data when computing the mean.
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