New parallel python tutotials

docs/Documentation/Development/Languages/Python/KestrelParallelPythonJupyter/exampleNotebooks/cupyAndIpyparallel.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a880d8b0-d919-463e-b4dc-402c23493efc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numberOfNodes=2\n",
+    "import mpi4py\n",
+    "import os\n",
+    "import ipyparallel as ipp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "127dc93e-1745-49d8-a9e1-2351ed0092b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting 2 engines with <class 'ipyparallel.cluster.launcher.MPIEngineSetLauncher'>\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c038b444550464b9ae0ccf07a6307b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?engine/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cluster = ipp.Cluster(engines=\"mpi\", n=numberOfNodes,controller_ip='*')\n",
+    "rc = cluster.start_and_connect_sync()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5ee27e14-31e3-46ed-a083-f99ad32410da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rc.wait_for_engines(n=numberOfNodes)\n",
+    "dview=rc[:]\n",
+    "rc.ids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e1216bef-49eb-4c6f-8ef5-2ae3a6eb499d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[stdout:0] x3100c0s21b0n0\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[stdout:1] x3101c0s13b0n0\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%px --target 0:2\n",
+    "#%%px --target 0:1\n",
+    "import os, socket\n",
+    "#print(os.getpid())\n",
+    "print(socket.gethostname())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3247f8c5-dea9-4bfa-8ba7-e14f920b2fa0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[stdout:0] Process 0 sent data: [[[[-0.00705688  0.1574703 ]\n",
+       "   [-0.11614565 -1.36530107]]\n",
+       "\n",
+       "  [[ 1.9377235   0.19872024]\n",
+       "   [ 0.20897026  1.86760844]]]\n",
+       "\n",
+       "\n",
+       " [[[-1.19135132  1.44246809]\n",
+       "   [-1.49924247  0.45127102]]\n",
+       "\n",
+       "  [[ 1.77763836  0.49243783]\n",
+       "   [-0.4570443   0.96459175]]]]\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[stdout:1] Process 1 received data: [[[[-0.00705688  0.1574703 ]\n",
+       "   [-0.11614565 -1.36530107]]\n",
+       "\n",
+       "  [[ 1.9377235   0.19872024]\n",
+       "   [ 0.20897026  1.86760844]]]\n",
+       "\n",
+       "\n",
+       " [[[-1.19135132  1.44246809]\n",
+       "   [-1.49924247  0.45127102]]\n",
+       "\n",
+       "  [[ 1.77763836  0.49243783]\n",
+       "   [-0.4570443   0.96459175]]]]\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%px\n",
+    "from mpi4py import MPI\n",
+    "\n",
+    "useGPU=False\n",
+    "\n",
+    "if useGPU:\n",
+    "    import cupy as cp\n",
+    "else:\n",
+    "    import numpy as cp\n",
+    "\n",
+    "comm = MPI.COMM_WORLD\n",
+    "size = comm.Get_size()\n",
+    "rank = comm.Get_rank()\n",
+    "\n",
+    "if rank == 0:\n",
+    "    N=2\n",
+    "    data = cp.random.randn(N,N,N,N)\n",
+    "    comm.send(data, dest=1, tag=42)\n",
+    "    print('Process {} sent data:'.format(rank), data)\n",
+    "    \n",
+    "elif rank == 1:\n",
+    "    data = comm.recv(source=0, tag=42)\n",
+    "    print('Process {} received data:'.format(rank), data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d47a47d6-b7f0-4e5d-a8ba-37649d805a10",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myEnvJupyter",
+   "language": "python",
+   "name": "myenvjupyter"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/Documentation/Development/Languages/Python/KestrelParallelPythonJupyter/exampleNotebooks/cupyOnly.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b173f1c1-467b-415b-8ee4-99257830d74b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import cupy as cp\n",
+    "from cupyx.profiler import benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9282ca52-03a7-41f9-8074-15fb457b6894",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(a, b):\n",
+    "    return cp.pi * cp.sin(-a) * cp.exp(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "be231d9e-90f5-4a5a-b49c-58de8c9271c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "my_func             :    CPU:    36.410 us   +/-  5.270 (min:    34.873 / max:  1140.029) us     GPU-0:    94.612 us   +/-  4.973 (min:    91.424 / max:  1172.864) us\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = cp.random.random((2560, 1024))\n",
+    "b = cp.random.random((2560, 1024))\n",
+    "print(benchmark(my_func, (a,b), n_repeat=100000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "798d1913-b406-4629-aecf-988134d3b650",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np_rng = np.random.default_rng()\n",
+    "\n",
+    "A_cpu = np_rng.random((1000, 1000))\n",
+    "b_cpu = np_rng.random(1000)\n",
+    "\n",
+    "A_gpu = cp.array(A_cpu)\n",
+    "b_gpu = cp.array(b_cpu)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cad205a2-9ba1-4740-9182-57b82af9baf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "17.6 ms ± 62.1 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "np.linalg.solve(A_cpu, b_cpu)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a8d96215-9b1f-4999-ab3e-cc926bb3284c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.49 ms ± 268 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "cp.linalg.solve(A_gpu, b_gpu)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "042864d2-c29c-4be0-8779-935d25759072",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Type of CPU array is: <class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_gpu = cp.ones((1000, 1000))\n",
+    "x_cpu = x_gpu.get()\n",
+    "print(f'Type of CPU array is: {type(x_cpu)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5beba59f-8d17-437d-a2f6-f8bbab28c6fd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myEnvJupyter",
+   "language": "python",
+   "name": "myenvjupyter"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}