To accounts for the number of processors in each dimension the values nprox_x and nproc_y. Mentioned in the Introduction i.e., compiler, number of processors, hardware year. After configuring and compiling, we submit a Slurm submit script with some combination of the options f90=$(SFC) and -cc=$(SCC), which ensures that distributed memory parallel processing is used rather than shared memory We then modify the configure.wrf file to change a few compiler flags, where most importantly we remove the flags,
Stages, we use the dmpar options for each compiler under Linux x86_64, using basic nesting. Instructions outlined on the WRF benchmarking page. WRF provides two example data sets intended specifically for benchmarking purposes. One of these plots shows the compiler performance for that year. Then, we show the inverse, where four plots correspond One of these plotsĭetails the scaling across every year's hardware for one compiler. GNU OpenMPI, Intel MVAPICH2, PGI OpenMPI, etc.), so we have six plots. First, we show each compiler combinationĬhanges across upgrades (e.g. We show two variants of the same data below in the benchmark results section. Hardware on the HPC, which is categorized according to the year that it was introduced (2012-2019). The scaling of each of these compilers is shown for all the available We coupled each of these with both OpenMPI or MVAPICH2, which gave us a total of six combinations.
In our tests, we looked at the GFlops for three different To determine the computational performance and scaling of systems available on the HPC, we used the benchmarking toolsĪvailable from WRF, the Weather Research and Forecasting model 1. Compute node benchmarks # WRF Compute node benchmarks # We are working on re-running them as of November 2022. These benchmarks were updated in mid-2021. Instructions for running the benchmark scripts ScanResults.py and generateGigures.m scripts FSU Research Computing Center Documentation
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