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TomNicholas/quadratic_means_comparison.ipynb

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Cubed vs dask on quadratic means problem
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quadratic_means_comparison.ipynb
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"cell_type": "code",
"execution_count": 1,
"id": "144e8663-f783-496b-99a4-07de3e92f172",
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"tags": []
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"import xarray as xr"
]
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"cell_type": "markdown",
"id": "a0b0b052-ea72-4b0c-885f-e6ccf39ebfac",
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"## Means of quadratic quantities\n",
"\n",
"**Problem:** We have 2 time-varying scalar fields $U(t)$ and $V(t)$. \n",
"We want to calculate the time-averages of all the quadratic combinations of these fields, i.e. $<U^2>_t$, $<V^2>_t$, and $<UV>_t$ all simultaneously.\n",
"\n",
"If these fields are chunked along time, then computing the mean of any one quadratic field should process only require loading one time chunk at a time.\n",
"\n",
"(From https://github.com/pangeo-data/distributed-array-examples/issues/2)"
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"cell_type": "markdown",
"id": "b44b1db8-2836-469b-b6ee-dab8e5049111",
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"source": [
"# Using dask"
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"cell_type": "markdown",
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"We're going to define the problem using dask, then execute the workloads first without using P2P rechunking (by using a LocalCluster) and then again with the new P2P rechunking (using a Coiled Cluster running the latest version of distributed)."
]
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{
"cell_type": "code",
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"id": "51e27bf4-ccee-442a-ab76-24c1d96a69c4",
"metadata": {
"tags": []
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"outputs": [],
"source": [
"import dask\n",
"import dask.array as da"
]
},
{
"cell_type": "markdown",
"id": "815668f5-96c6-4b00-adc5-d5d988d2a135",
"metadata": {},
"source": [
"## Define problem"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fa136aa6-6d6f-4641-891d-2119dbd2897c",
"metadata": {
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"outputs": [],
"source": [
"def create_dask_data(t_length):\n",
" return xr.Dataset(\n",
" dict(\n",
" anom_u=([\"time\", \"face\", \"j\", \"i\"], da.random.random((t_length, 1, 987, 1920), chunks=(10, 1, -1, -1))),\n",
" anom_v=([\"time\", \"face\", \"j\", \"i\"], da.random.random((t_length, 1, 987, 1920), chunks=(10, 1, -1, -1))),\n",
" )\n",
" )"
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},
{
"cell_type": "code",
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"id": "f89665df-e603-4911-8bf8-c47440e23831",
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"source": [
"datasets = {\n",
" '1.5GB': create_dask_data(50),\n",
" '15GB': create_dask_data(500),\n",
" '150GB': create_dask_data(5000),\n",
" '1.5TB': create_dask_data(50000),\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "366eed03-67d1-49b4-b165-9fc1d930af45",
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"name": "stdout",
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"text": [
"1.5 GB dataset, \n",
"1.5e+01 GB dataset, \n",
"1.5e+02 GB dataset, \n",
"1.5e+03 GB dataset, \n"
]
}
],
"source": [
"for scale, ds in datasets.items():\n",
" print(f'{ds.nbytes / 1e9:.2} GB dataset, ')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5eb076a4-77b7-4735-851b-a35a50616e54",
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"dask.array&lt;random_sample, shape=(50, 1, 987, 1920), dtype=float64, chunksize=(10, 1, 987, 1920), chunktype=numpy.ndarray&gt;\n",
"Dimensions without coordinates: time, face, j, i</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'anom_u'</div><ul class='xr-dim-list'><li><span>time</span>: 50</li><li><span>face</span>: 1</li><li><span>j</span>: 987</li><li><span>i</span>: 1920</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b9ac65d6-d6dd-485f-9afe-23a2af484195' class='xr-array-in' type='checkbox' checked><label for='section-b9ac65d6-d6dd-485f-9afe-23a2af484195' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(10, 1, 987, 1920), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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],
"text/plain": [
"<xarray.DataArray 'anom_u' (time: 50, face: 1, j: 987, i: 1920)>\n",
"dask.array<random_sample, shape=(50, 1, 987, 1920), dtype=float64, chunksize=(10, 1, 987, 1920), chunktype=numpy.ndarray>\n",
"Dimensions without coordinates: time, face, j, i"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datasets['1.5GB']['anom_u']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "20ae7567-6a6e-4c1a-bc68-a5b116926fa5",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def quadratic_means(ds):\n",
" quadratic_products = ds**2\n",
" quadratic_products[\"uv\"] = ds.anom_u * ds.anom_v\n",
" mean = quadratic_products.mean(\"time\")\n",
" return mean"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "18cb5bfc-17cb-4c4d-906f-855e5fb01841",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"workloads = {scale: quadratic_means(ds) for scale, ds in datasets.items()}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5e7fc042-fd2f-4a74-9fdc-6c4a371040dc",
"metadata": {
"tags": []
},
"outputs": [
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