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<title>Tips for HPC users — swspy 1.0.3 documentation</title>
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<div class="section" id="tips-for-hpc-users">
<h1>Tips for HPC users<a class="headerlink" href="#tips-for-hpc-users" title="Permalink to this headline">¶</a></h1>
<p>Since SWSPy is parallelised, it may be beneficial to process data on High Performance Computing (HPC) infrastructure for large datasets. Here is a little information on the arcitecture of SWSPy and a brief example on how to structure a HPC job.</p>
<p>SWSPy is parallelised in an embarissingly parallel fashion for performing the phi-dt grid search. Each process is therefore independent during the grid search. However, the processes come together at the end of each process, and so the code should be treated as a shared memory model rather than a distributed memory model. Crucially, this means that for any job, one should only submit to a maximum of one node. SWSPy does not support parallelisation across mulitple nodes for one job.</p>
<p>A simple example of a possible submission script is shown below. This script is written for a SLURM submission management system with the Anaconda package manager installed.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Setup SBATCH params (example only):</span>
SBATCH<span class="w"> </span>--nodes<span class="o">=</span><span class="m">1</span><span class="w"> </span><span class="c1"># NOTE: swspy will only run on a single node</span>
SBATCH<span class="w"> </span>--ntasks-per-node<span class="o">=</span><span class="m">1</span>
SBATCH<span class="w"> </span>--cpus-per-task<span class="o">=</span><span class="m">48</span><span class="w"> </span><span class="c1"># NOTE: Make sure number of NUMBA threads specified in swspy!</span>
SBATCH<span class="w"> </span>--mem<span class="o">=</span><span class="m">64000</span>
SBATCH<span class="w"> </span>--time<span class="o">=</span><span class="m">12</span>:00:00
SBATCH<span class="w"> </span>--job-name<span class="o">=</span>swspy_run
<span class="c1"># Load python environment:</span>
module<span class="w"> </span>load<span class="w"> </span>Anaconda3
<span class="nb">source</span><span class="w"> </span>activate<span class="w"> </span><span class="nv">$DATA</span>/swspy_env<span class="w"> </span><span class="c1">#Path to anaconda environment with swspy and all dependencies installed</span>
<span class="c1"># Run SWSPy:</span>
<span class="c1"># NOTE: Very important that the number of processors specified above (--cpus-per-task parameter)</span>
python<span class="w"> </span>swspy_run_script.py<span class="w"> </span><span class="c1"># Python script detailing specific commands for running swspy (see examples in tutorials).</span>
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