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chaoming0625chaoming0625
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update doc
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‎docs/tutorial_simulation/parallel_computing.ipynb‎

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},
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"cell_type": "code",
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"execution_count": 9,
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"outputs": [],
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"source": [
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"current = bm.linspace(1, 10.1, 10) # here only one parameter"
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"cell_type": "code",
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"def hh_spike_num2(bg_current): # \"input\" is the bg_current\n",
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" import brainpy as bp # needed to reimport packages when\n",
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" # run the function in Jupyter\n",
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"\n",
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" bg_current = bm.as_jax(bg_current)\n",
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" bg_current = bp.math.as_jax(bg_current)\n",
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" model = bp.neurons.HH(1)\n",
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" runner = bp.dyn.DSRunner(model, monitors=['spike'], inputs=['input', bg_current])\n",
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" runner.run(1000.)\n",
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"If you think that the order of the running results does not matter, you can also use ``cpu_unordered_parallel()`` function. This can maximize the running efficiency of all processors, since all workers run with a non-blocking and unordered manner."
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"def hh_spike_num3(bg_current): # \"input\" is the bg_current\n",
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"source": [
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"bp.math.set_host_device_count(10)\n",
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"bp.math.set_host_device_count(10) # this should place on the top of the file\n",
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"current = bm.linspace(1., 10.1, 20)\n",
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"r = bp.running.jax_vectorize_map(hh_spike_num3, [current], num_parallel=10, clear_buffer=True)\n",
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"r = bp.running.jax_parallelize_map(hh_spike_num3, [current], num_parallel=10, clear_buffer=True)\n",
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"r"
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"cell_type": "code",
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"outputs": [],
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"cell_type": "markdown",
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"source": [
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"```python\n",
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"\n",
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"#!/bin/bash\n",
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"#SBATCH -J <name>\n",
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"#SBATCH -o <file name>\n",
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"#SBATCH -N <int>\n",
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"python your_script.py"
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"python your_script.py\n",
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"```"
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"metadata": {
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"collapsed": false

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