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+# Dask
+
+[Dask](https://www.dask.org/) is a Python library for parallel and distributed computing. 
+A Dask cluster is composed by one scheduler that coordinates the job of many workers, which can have access to CPU or GPU resources. 
+Here we show how to install Dask in a conda environment on Aurora and how to start a cluster with GPU workers and run a simple example script. 
+
+
+## Install Dask on Aurora
+
+From one of Aurora's login nodes, use the following commands to create a conda environment and install Dask. 
+This will also install other libraries needed to run an example script, and create a Jupyter kernel that allows to work interactively from a notebook. 
+
+```bash
+module load frameworks
+conda create -y -n dask -c conda-forge python=3.11 pip dask ipykernel jupyterlab
+conda activate dask
+# install additional libraries
+conda install -y -c https://software.repos.intel.com/python/conda/ -c conda-forge dpnp
+# create the jupyter kernel
+python -m ipykernel install --prefix=${CONDA_PREFIX} --name dask
+```
+
+
+## Start a Dask cluster
+
+Copy the following script into a file called `start_dask_aurora.sh` and make it executable with 
+
+```bash
+chmod a+x ./start_dask_aurora.sh
+```
+
+### `start_dask_aurora.sh`
+
+```bash
+#!/bin/bash
+
+# start_dask_aurora.sh
+# Usage: 
+# mpiexec -n NNODES * NUM_WORKERS_PER_NODE --ppn NUM_WORKERS_PER_NODE ./start_dask_aurora.sh WORKER_TYPE NUM_WORKERS_PER_NODE
+# Examples on two nodes:
+# mpiexec -n 12 --ppn 6 ./start_dask_aurora.sh gpu 6
+# mpiexec -n 208 --ppn 104 ./start_dask_aurora.sh cpu 104
+
+WORKER_TYPE=$1
+NUM_WORKERS_PER_NODE=$2
+# if using 12 GPU workers, assign one worker per tile, otherwise uses one worker per GPU (2 tiles)
+if [ $NUM_WORKERS_PER_NODE = 12 ] && [ $WORKER_TYPE = 'gpu' ]; then
+    export ZE_FLAT_DEVICE_HIERARCHY=FLAT
+    export ZE_ENABLE_PCI_ID_DEVICE_ORDER=1
+else
+    export ZE_FLAT_DEVICE_HIERARCHY=COMPOSITE
+fi
+
+# Number of threads per worker (208 CPU threads per node divided by num workers)
+NTHREADS=$(( 208 / NUM_WORKERS_PER_NODE ))  # 208 / 12 ≈ 17
+# Memory limit per worker (1100GB RAM per node divided by num workers)
+MEMORY_PER_WORKER=$(( 1100 / NUM_WORKERS_PER_NODE ))GB  # 1100GB / 12 ≈ 91GB
+LOCAL_DIRECTORY=~/dask-local-directory
+DASK_DASHBOARD_PORT=${DASK_DASHBOARD_PORT:-8787}
+DASK_SCHEDULER_PORT=${DASK_SCHEDULER_PORT:-8786}
+
+# Start Dask scheduler on rank 0
+if [ $PALS_RANKID = 0 ]; then
+    # Purge Dask worker, log directories and config directories
+    rm -rf ${LOCAL_DIRECTORY}/* /tmp/dask-workers/*  ~/.config/dask
+    mkdir -p ${LOCAL_DIRECTORY}/logs /tmp/dask-workers
+    # Setup scheduler
+    nohup dask scheduler --port ${DASK_SCHEDULER_PORT} --dashboard-address $DASK_DASHBOARD_PORT \
+        --scheduler-file ${LOCAL_DIRECTORY}/scheduler.json > ${LOCAL_DIRECTORY}/logs/$HOSTNAME-scheduler.log 2>&1 &
+fi
+sleep 10
+# Setup workers
+if [ $WORKER_TYPE = 'gpu' ]; then
+    ZE_AFFINITY_MASK=$PALS_LOCAL_RANKID dask worker \
+        --resources "GPU=1" --memory-limit ${MEMORY_PER_WORKER} \
+        --nthreads ${NTHREADS}  --local-directory /tmp/dask-workers \
+        --scheduler-file ${LOCAL_DIRECTORY}/scheduler.json >> ${LOCAL_DIRECTORY}/logs/$HOSTNAME-worker.log 2>&1
+else
+    dask worker \
+        --nthreads ${NTHREADS} --local-directory /tmp/dask-workers \
+        --scheduler-file ${LOCAL_DIRECTORY}/scheduler.json >> ${LOCAL_DIRECTORY}/logs/$HOSTNAME-worker.log 2>&1
+fi
+```
+
+### Start a cluster with CPU workers
+
+Run the following commands from a compute node on Aurora to start a Dask cluster with 104 CPU workers per node:
+
+```bash
+module load frameworks
+conda activate dask
+NNODES=`wc -l < $PBS_NODEFILE`
+mpiexec -n $(( $NNODES * 104 )) --ppn 104 ./start_dask_aurora.sh cpu 104
+```
+
+### Start a cluster with GPU workers
+
+Run the following commands from a compute node on Aurora to start a Dask cluster with 6 GPU workers per node:
+
+```bash
+module load frameworks
+conda activate dask
+NNODES=`wc -l < $PBS_NODEFILE`
+mpiexec -n $(( $NNODES * 6 )) --ppn 6 ./start_dask_aurora.sh gpu 6
+```
+
+
+## Example
+
+In this example, we will [estimate Pi using a Monte Carlo method](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods). 
+
+Paste the following python script into a file called `pi_dask_gpu.py`. 
+Here is a breakdown of what the script does:
+1. It connects to the Dask cluster (that you should start beforehand) and prints some information including the number of workers and available memory.
+1. It divides the total number of points to sample between the workers, and each worker uses its GPU to
+   - generate random points uniformly inside the unit square
+   - return the number of points that are inside the unit circle
+1. When the results from the workers are ready, they are aggregated to compute Pi.
+1. A total of 5 Pi calculations are performed and timed (the very first iterations will incur in initialization and warmup costs).
+1. At the end, the Dask cluster is shut down.
+
+### `pi_dask_gpu.py`
+
+```python
+import json
+import pathlib
+from dask.distributed import Client
+
+
+fname = f'{pathlib.Path.home().as_posix()}/dask-local-directory/scheduler.json'
+with open(fname, 'r') as f:
+    scheduler = json.load(f)
+client = Client(scheduler['address'])
+print(client.scheduler_info)
+
+
+import time
+import dpnp as np
+
+
+def count_points_inside_circle(N):
+    x = np.random.uniform(low=-1.0, high=1.0, size=(N, 2))
+    inside_circle = ((x * x).sum(axis=1) < 1.).sum()
+    return int(inside_circle)
+
+
+def compute_pi(inside_circle, N):
+    return 4 * inside_circle / N
+
+
+def run():
+    start = time.time()
+    num_workers = len(client.scheduler_info()['workers'])
+    N = 10_400_000_004
+
+    # number of points per worker
+    Neach_section, extras = divmod(N, num_workers)
+    points_per_worker = [Neach_section for _ in range(num_workers)]
+    points_per_worker[-1] += extras
+
+    futures = client.map(count_points_inside_circle, points_per_worker)
+    inside_circle = client.submit(sum, futures).result()
+    pi = compute_pi(inside_circle, N)
+    end = time.time()
+    return f"Num samples: {N:.2E}\t\tEstimate: {pi:.9f}\t\tTime taken: {end - start:.3f} s"
+
+
+def main(runs=5):
+    for i in range(runs):
+        print(f"Run {i}\t\t{run()}")
+
+
+main()
+client.shutdown()
+```
+
+### Run the `pi_dask_gpu.py` example
+
+- First, request an interactive job on 1 node.
+- Then, [start a Dask cluster with 6 GPU workers](#start-a-cluster-with-gpu-workers) and wait about 10 seconds for the cluster to start.
+- Press `Ctrl+Z` to send the process to the background, or open a new shell and ssh into the compute node. 
+- Run the example script:
+  ```bash
+  module load frameworks
+  conda activate dask
+  python pi_dask_gpu.py
+  ```
+- Output:
+  ```bash
+  <bound method Client.scheduler_info of <Client: 'tcp://10.168.0.10:8786' processes=6 threads=204, memory=1.00 TiB>>
+  Run 0           Num samples: 1.04E+10           Estimate: 3.141653798           Time taken: 1.596 s
+  Run 1           Num samples: 1.04E+10           Estimate: 3.141570887           Time taken: 1.354 s
+  Run 2           Num samples: 1.04E+10           Estimate: 3.141651954           Time taken: 1.451 s
+  Run 3           Num samples: 1.04E+10           Estimate: 3.141636617           Time taken: 0.518 s
+  Run 4           Num samples: 1.04E+10           Estimate: 3.141650108           Time taken: 0.511 s
+  ```
+
+## Connect to a Dask cluster from JupyterLab
+
+Here are the steps to start a Dask cluster and connect to it interactively from a Jupyter notebook:
+
+- First, request an interactive job on 1 node. Print the compute node's hostname (that you get with the command `hostname`), which will be used later.
+- Then, [start a Dask cluster](#start-a-cluster-with-gpu-workers) and wait about 10 seconds for the cluster to start.
+- **On your local machine**, open a ssh tunnel to the compute node (`COMPUTE_NODE` is the compute node's hostname and `YOUR_ALCF_USERNAME` is your ALCF username):
+  ```bash
+  ssh -t -L 23456:localhost:23456 -L 8787:localhost:8787 YOUR_ALCF_USERNAME@bastion.alcf.anl.gov ssh -t -L 23456:localhost:23456 -L 8787:localhost:8787 login.aurora.alcf.anl.gov ssh -t -L 23456:localhost:23456 -L 8787:localhost:8787 COMPUTE_NODE
+  ```
+- **On the compute node** where you land with the above ssh command, start JupyterLab:
+  ```bash
+  module load frameworks
+  conda activate dask
+  jupyter lab --no-browser --port=23456
+  ```
+- Copy the line starting with `http://localhost:23456/lab?token=<TOKEN>` at the end of the jupyter command's output.
+- **On your local machine**, open a browser window and go to that url.
+- **On the JupyterLab page**, select the `dask` kernel and use this script to connect to the Dask cluster:
+  ```python
+  import json
+  import pathlib
+  from dask.distributed import Client
+  
+  fname = f'{pathlib.Path.home().as_posix()}/dask-local-directory/scheduler.json'
+  with open(fname, 'r') as f:
+      scheduler = json.load(f)
+  client = Client(scheduler['address'])
+  client
+  ```
+- The [Dask dashboard](https://docs.dask.org/en/latest/dashboard.html) will be available at <http://localhost:8787>
+
diff --git a/mkdocs.yml b/mkdocs.yml
index bee5a11b0..2341db9f4 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -85,6 +85,7 @@ nav:
           - LibTorch: aurora/data-science/frameworks/libtorch.md
           - OpenVINO: aurora/data-science/frameworks/openvino.md
           - oneCCL: aurora/data-science/frameworks/oneCCL.md
+          - Dask: aurora/data-science/frameworks/dask.md
       - Programming Models:
         - Kokkos: aurora/programming-models/kokkos-aurora.md
         - Level Zero: aurora/programming-models/level-0.md