{ "cells": [ { "cell_type": "markdown", "id": "2fb2fc52", "metadata": {}, "source": [ "(tuto2)=\n", "# Your first workflow\n", "\n", "This tutorial will teach you how to chain functionalities to create a simple workflow.\n", "Based on a real CFD case, the objectives are to demonstrate how Maia simplifies distributed\n", "mesh operations and to provide a solid foundation for creating your own parallel CGNS workflows.\n", "\n", "Our use case involves a Rotor37 geometry mesh.\n", "The input mesh is available in different sizes; please download one from the table below.\n", "The illustrations on this page were created using the medium-sized mesh.\n", "\n", "\n", "[rotor_37_small.cgns]: https://github.com/onera/Maia/releases/download/v1.8/rotor37_small.cgns\n", "[rotor_37_medium.cgns]: https://github.com/onera/Maia/releases/download/v1.8/rotor37_medium.cgns\n", "[rotor_37_large.cgns]: https://github.com/onera/Maia/releases/download/v1.8/rotor37_large.cgns\n", "\n", "| | Light | Medium | Large |\n", "|-------------------|-----------------------|------------------------|-----------------------|\n", "| Initial cell size | ~60K | ~200K | ~21M |\n", "| File size | 6 MiB | 16 MiB | 1 GiB |\n", "| (Final cell size) | ~2M | ~7M | ~762M |\n", "| Download link | [rotor_37_small.cgns] | [rotor_37_medium.cgns] | [rotor_37_large.cgns] |" ] }, { "cell_type": "code", "execution_count": null, "id": "a675c53e", "metadata": { "tags": [ "remove-cell", "no-parallel" ] }, "outputs": [], "source": [ "# This create a symlink of the input file in current directory, for automatic execution\n", "SRC = '/stck/jcoulet/Public/maia_training/MESHES/rotor37_medium_elt.cgns'\n", "import os\n", "if not os.path.exists('rotor37_medium.cgns'):\n", " os.symlink(SRC, 'rotor37_medium.cgns')" ] }, { "cell_type": "markdown", "id": "0fa790ec", "metadata": {}, "source": [ "Then, ensure you have access to a valid installation of maia and create\n", "an empty file {file}`02_workflow.py`. \n", "You will complete this file as you work through the tutorial; look out for\n", "instructions preceded by the symbol ✏️.\n", "\n", "```{tip}\n", "During the experimentation phase, we advise you to use a light mesh and a low number of processes, especially if you are displaying data.\n", "Once the script is ready, you can use more processes and move to larger meshes.\n", "```\n", "\n", "## Objective and plan\n", "\n", "The aim of the workflow is to recombine a complete 360° mesh from the input angular section,\n", "with a data field initialized on it:\n", "\n", "```{glue:figure} target_fig\n", ":align: center\n", ":width: 85%\n", "\n", "Initial (left) and expected final (right) mesh, colored from `VelocityY` field. Black lines\n", "show the initial angular section position.\n", "```\n", "\n", "We precise that we expect to have a single `Zone_t` node on the 360° mesh.\n", "\n", "👉 Before starting to code, let's see what will be needed. Open the {ref}`user_manual`\n", "and answer the following questions:\n", "\n", "❓ Which function(s) you will need to use ?\n", "\n", "*Hint : try to search 360 or duplicate in the search bar* \n", "\n", "```{toggle}\n", "The main functions you are going to use are :\n", "- {func}`~maia.algo.dist.duplicate_from_rotation_jns_to_360` to duplicate the input mesh,\n", "- {func}`~maia.algo.dist.merge_zones` (or equivalent) to merge the resulting zones in a single one.\n", "\n", "In addition, you will need some `PT` functions to create and store the initial field, as well as\n", "the standard `maia.io` functions.\n", "```\n", "\n", "❓ Will you need to work on a partioned view of the input mesh ?\n", "\n", "```{toggle}\n", "All the functions mentionned above operate on a distributed tree. Thus, partioning the\n", "tree will not be necessary.\n", "```\n", "\n", "❓ When should you create the initial field ?\n", "\n", "```{toggle}\n", "You can create the initial field anytime in your workflow, but it is probably more\n", "efficient to do it before the duplication (since there will be more cells\n", "to compute after).\n", "```\n", "\n", "👉 Last step: it is a good practice to quickly check the input mesh, *eg* with `maia_print_tree`:" ] }, { "cell_type": "code", "execution_count": null, "id": "4cbe5d02", "metadata": { "tags": [ "remove-input", "no-parallel" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m\u001b[38;5;33mCGNSTree\u001b[0m \u001b[38;5;246mCGNSTree_t\u001b[0m \n", "├───CGNSLibraryVersion \u001b[38;5;246mCGNSLibraryVersion_t\u001b[0m R4 [4.2]\n", "└───\u001b[1m\u001b[38;5;33mBase\u001b[0m \u001b[38;5;246mCGNSBase_t\u001b[0m I4 [3 3]\n", " ├───\u001b[1m\u001b[38;5;220mAmont\u001b[0m \u001b[38;5;246mFamily_t\u001b[0m \n", " │ └───FamilyBC \u001b[38;5;246mFamilyBC_t\u001b[0m \"BCInflowSubsonic\"\n", " ├───\u001b[1m\u001b[38;5;220mAval\u001b[0m \u001b[38;5;246mFamily_t\u001b[0m \n", " │ └───FamilyBC \u001b[38;5;246mFamilyBC_t\u001b[0m \"BCOutflowSubsonic\"\n", " ├───\u001b[1m\u001b[38;5;220mCarter\u001b[0m \u001b[38;5;246mFamily_t\u001b[0m \n", " │ └───FamilyBC \u001b[38;5;246mFamilyBC_t\u001b[0m \"BCWallViscous\"\n", " ├───\u001b[1m\u001b[38;5;220mAubeMoyeu\u001b[0m \u001b[38;5;246mFamily_t\u001b[0m \n", " │ └───FamilyBC \u001b[38;5;246mFamilyBC_t\u001b[0m \"BCWallViscous\"\n", " ├───\u001b[1m\u001b[38;5;33mRotor\u001b[0m \u001b[38;5;246mZone_t\u001b[0m I4 [[211897 197568 0]]\n", " │ ├───\u001b[1m\u001b[38;5;183mZoneType\u001b[0m \u001b[38;5;246mZoneType_t\u001b[0m \"Unstructured\"\n", " │ ├───\u001b[1m\u001b[38;5;183mGridCoordinates\u001b[0m \u001b[38;5;246mGridCoordinates_t\u001b[0m \n", " │ │ ╵╴╴╴ (3 children masked)\n", " │ ├───\u001b[1m\u001b[38;5;183mZoneBC\u001b[0m \u001b[38;5;246mZoneBC_t\u001b[0m \n", " │ │ ╵╴╴╴ (6 children masked)\n", " │ ├───\u001b[1m\u001b[38;5;183mQUAD_4\u001b[0m \u001b[38;5;246mElements_t\u001b[0m I4 [7 0]\n", " │ │ ╵╴╴╴ (2 children masked)\n", " │ └───\u001b[1m\u001b[38;5;183mHEXA_8\u001b[0m \u001b[38;5;246mElements_t\u001b[0m I4 [17 0]\n", " │ ╵╴╴╴ (2 children masked)\n", " ├───\u001b[1m\u001b[38;5;220mperright\u001b[0m \u001b[38;5;246mFamily_t\u001b[0m \n", " │ └───FamilyBC \u001b[38;5;246mFamilyBC_t\u001b[0m \"UserDefined\"\n", " └───\u001b[1m\u001b[38;5;220mperleft\u001b[0m \u001b[38;5;246mFamily_t\u001b[0m \n", " └───FamilyBC \u001b[38;5;246mFamilyBC_t\u001b[0m \"UserDefined\"\n" ] } ], "source": [ "import subprocess\n", "_ = subprocess.run(\n", " [\"maia_print_tree\", \"rotor37_medium.cgns\", \"--depth=3\"],\n", ")" ] }, { "cell_type": "markdown", "id": "b8674343", "metadata": {}, "source": [ "❓ Is the input mesh suitable for the functions you plan to call ?\n", "\n", "```{toggle}\n", "❌️ No! The input mesh is described by standard elements, but the function\n", "{func}`~maia.algo.dist.merge_zones` only applies to polyedric elements.\n", "Thus, a mesh conversion into polyedric (`NGON_n`) elements will be needed.\n", "```\n", "\n", "👉 All these informations allow us to propose the following plan:\n", "\n", "```{toggle}\n", "1. Load mesh\n", "2. Convert into polyedric tree\n", "3. Initialize fields\n", "4. Duplicate \n", "5. Merge zones\n", "```\n", "\n", "## Development\n", "\n", "### Prelude\n", "\n", "✏️ As in {ref}`first tutorial `, get the communicator\n", "from `mpi4py` package and import the modules that we will use: `maia` and `maia.pytree`." ] }, { "cell_type": "code", "execution_count": null, "id": "81849a03", "metadata": { "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "from mpi4py import MPI\n", "comm = MPI.COMM_WORLD\n", "\n", "import maia\n", "import maia.pytree as PT" ] }, { "cell_type": "markdown", "id": "0c75de2d", "metadata": {}, "source": [ "### File reading\n", "\n", "\n", "\n", "✏️ Read the CGNS file of your choice from the disk using the main file reading function : \n", "{func}`maia.io.file_to_dist_tree`." ] }, { "cell_type": "code", "execution_count": null, "id": "a521a28b", "metadata": { "mystnb": { "code_prompt_hide": "Hide solution", "code_prompt_show": "Show solution" }, "tags": [ "hide-cell" ] }, "outputs": [ { "data": { "text/plain": [ "[stdout:0] Distributed read of file rotor37_medium.cgns...\n", "Read completed (0.13 s) -- Size of dist_tree for current rank is 2.9MiB (Σ=11.5MiB)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tree = maia.io.file_to_dist_tree('rotor37_medium.cgns', comm)" ] }, { "cell_type": "markdown", "id": "6fcbada7", "metadata": {}, "source": [ "### Connectivity conversion\n", "\n", "👉 Search in the {ref}`user_manual` the relevant function to convert the\n", "elements description from standard elements to polyedric (`NGON_n`) elements.\n", "\n", "✏️ Call the function and check it worked by printing the\n", "{func}`~maia.pytree.Element.Type` of `Elements_t` nodes." ] }, { "cell_type": "code", "execution_count": null, "id": "90807ef8", "metadata": { "tags": [ "hide-cell" ] }, "outputs": [ { "data": { "text/plain": [ "[stdout:0] Elements type was {'QUAD_4', 'HEXA_8'}\n", "Elements type are now {'NGON_n', 'NFACE_n'}\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "elt_kinds = set(PT.Element.Type(e) for e in PT.iter_nodes_from_label(tree, 'Elements_t'))\n", "if comm.Get_rank() == 0:\n", " print(f\"Elements type was {elt_kinds}\")\n", "\n", "maia.algo.dist.convert_elements_to_ngon(tree, comm)\n", "\n", "elt_kinds = set(PT.Element.Type(e) for e in PT.iter_nodes_from_label(tree, 'Elements_t'))\n", "if comm.Get_rank() == 0:\n", " print(f\"Elements type are now {elt_kinds}\")" ] }, { "cell_type": "markdown", "id": "75882724", "metadata": {}, "source": [ "```{tip}\n", "The {ref}`pt_inspect` page lists various functions to easily get\n", "data related to the most frequent node labels.\n", "```" ] }, { "cell_type": "code", "execution_count": null, "id": "7e87165f", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [ { "data": { "text/plain": [ "[stdout:0] Distributed write of a 6.6MiB dist_tree (Σ=26.5MiB)...\n", "Write completed [rotor37_medium_ng.cgns] (0.90 s)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Save for visualization\n", "maia.io.dist_tree_to_file(tree, 'rotor37_medium_ng.cgns', comm)" ] }, { "cell_type": "markdown", "id": "36d6349f", "metadata": {}, "source": [ "### Field initialization\n", "\n", "In order to illustrate the ability of the duplication to move the vectorial fields\n", "together with the mesh, we are going to create a vectorial initial field in the input zone.\n", "\n", "This will made you use some functions of {ref}`pytree_module`.\n", "\n", "✏️ Get the (cartesian) coordinates $x$,$y$, and $z$ of the input zone and use it to compute\n", "the following vertex located velocity :\n", "\n", "```python\n", "vx = α \n", "vy = -α*sin(Θ) where α = sqrt(2)/2\n", "vz = α*cos(Θ) Θ = atan(z/y)\n", "```\n", "\n", "which corresponds to the uniform field $(0, \\alpha, \\alpha)$ in the cylindric coordinates\n", "$(\\vec{e_{r}}, \\vec{e_{\\theta}}, \\vec{e_{x}})$ (since $\\vec{e_x}$ is the revolution axis of the mesh).\n", "\n", "```{attention}\n", "You need to create arrays locally sized as $x$, $y$ and $z$. Use one of the ndarray methods to get this size.\n", "```" ] }, { "cell_type": "code", "execution_count": null, "id": "644cc935", "metadata": { "mystnb": { "code_prompt_hide": "Hide solution", "code_prompt_show": "Show solution" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "zone = PT.find_node_from_label(tree, 'Zone_t') # OK because tree has only one zone\n", "cx, cy, cz = PT.Zone.coordinates(zone)\n", "\n", "import numpy as np\n", "α = np.sqrt(2)/2\n", "Θ = np.arctan(cz/cy)\n", "vx = np.full(len(cx), α)\n", "vy = -α * np.sin(Θ)\n", "vz = α * np.cos(Θ)" ] }, { "cell_type": "markdown", "id": "7d7cee6f", "metadata": {}, "source": [ "✏️ Add the fields in a FlowSolution container. Be carefull to end the names\n", "by X,Y and Z to have it considered as a vectorial field later." ] }, { "cell_type": "code", "execution_count": null, "id": "1264494a", "metadata": { "mystnb": { "code_prompt_hide": "Hide solution", "code_prompt_show": "Show solution" }, "tags": [ "hide-cell", "remove-output" ] }, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mOut[3:6]: \u001b[0m\n", "['FlowSolution',\n", " None,\n", " [['GridLocation',\n", " array([b'V', b'e', b'r', b't', b'e', b'x'], dtype='|S1'),\n", " [],\n", " 'GridLocation_t'],\n", " ['VelocityX',\n", " array([0.70710678, 0.70710678, 0.70710678, ..., 0.70710678, 0.70710678,\n", " 0.70710678]),\n", " [],\n", " 'DataArray_t'],\n", " ['VelocityY',\n", " array([-0.06427231, -0.06446999, -0.06461761, ..., -0.04800832,\n", " -0.05192331, -0.05530049]),\n", " [],\n", " 'DataArray_t'],\n", " ['VelocityZ',\n", " array([0.70417971, 0.70416164, 0.70414811, ..., 0.70547516, 0.70519782,\n", " 0.70494103]),\n", " [],\n", " 'DataArray_t']],\n", " 'FlowSolution_t']" ] }, "metadata": { "after": null, "completed": null, "data": {}, "engine_id": 3, "engine_uuid": "3d326cb8-7b7f7ab001cd53013cc41487", "error": null, "execute_input": "PT.new_FlowSolution('FlowSolution',\n loc='Vertex',\n fields={\n 'VelocityX' : vx,\n 'VelocityY' : vy,\n 'VelocityZ' : vz},\n parent=zone)\n", "execute_result": { "data": { "text/plain": "['FlowSolution',\n None,\n [['GridLocation',\n array([b'V', b'e', b'r', b't', b'e', b'x'], dtype='|S1'),\n [],\n 'GridLocation_t'],\n ['VelocityX',\n array([0.70710678, 0.70710678, 0.70710678, ..., 0.70710678, 0.70710678,\n 0.70710678]),\n [],\n 'DataArray_t'],\n ['VelocityY',\n array([-0.06427231, -0.06446999, -0.06461761, ..., -0.04800832,\n -0.05192331, -0.05530049]),\n [],\n 'DataArray_t'],\n ['VelocityZ',\n array([0.70417971, 0.70416164, 0.70414811, ..., 0.70547516, 0.70519782,\n 0.70494103]),\n [],\n 'DataArray_t']],\n 'FlowSolution_t']" }, "execution_count": 6, "metadata": {} }, "follow": null, "msg_id": null, "outputs": [], "received": null, "started": null, "status": null, "stderr": "", "stdout": "", "submitted": "2026-02-02T08:41:40.939242Z" }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[1:6]: \u001b[0m\n", "['FlowSolution',\n", " None,\n", " [['GridLocation',\n", " array([b'V', b'e', b'r', b't', b'e', b'x'], dtype='|S1'),\n", " [],\n", " 'GridLocation_t'],\n", " ['VelocityX',\n", " array([0.70710678, 0.70710678, 0.70710678, ..., 0.70710678, 0.70710678,\n", " 0.70710678]),\n", " [],\n", " 'DataArray_t'],\n", " ['VelocityY',\n", " array([0.10328153, 0.09921449, 0.09522865, ..., 0.0670758 , 0.0641403 ,\n", " 0.05937606]),\n", " [],\n", " 'DataArray_t'],\n", " ['VelocityZ',\n", " array([0.69952336, 0.70011177, 0.70066504, ..., 0.7039182 , 0.70419175,\n", " 0.70460945]),\n", " [],\n", " 'DataArray_t']],\n", " 'FlowSolution_t']" ] }, "metadata": { "after": null, "completed": null, "data": {}, "engine_id": 1, "engine_uuid": "8c3e8793-de070d2b2d4d9f174aaf7465", "error": null, "execute_input": "PT.new_FlowSolution('FlowSolution',\n loc='Vertex',\n fields={\n 'VelocityX' : vx,\n 'VelocityY' : vy,\n 'VelocityZ' : vz},\n parent=zone)\n", "execute_result": { "data": { "text/plain": "['FlowSolution',\n None,\n [['GridLocation',\n array([b'V', b'e', b'r', b't', b'e', b'x'], dtype='|S1'),\n [],\n 'GridLocation_t'],\n ['VelocityX',\n array([0.70710678, 0.70710678, 0.70710678, ..., 0.70710678, 0.70710678,\n 0.70710678]),\n [],\n 'DataArray_t'],\n ['VelocityY',\n array([0.10328153, 0.09921449, 0.09522865, ..., 0.0670758 , 0.0641403 ,\n 0.05937606]),\n [],\n 'DataArray_t'],\n ['VelocityZ',\n array([0.69952336, 0.70011177, 0.70066504, ..., 0.7039182 , 0.70419175,\n 0.70460945]),\n [],\n 'DataArray_t']],\n 'FlowSolution_t']" }, "execution_count": 6, "metadata": {} }, "follow": null, "msg_id": null, "outputs": [], "received": null, "started": null, "status": null, "stderr": "", "stdout": "", "submitted": 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"['FlowSolution',\n", " None,\n", " [['GridLocation',\n", " array([b'V', b'e', b'r', b't', b'e', b'x'], dtype='|S1'),\n", " [],\n", " 'GridLocation_t'],\n", " ['VelocityX',\n", " array([0.70710678, 0.70710678, 0.70710678, ..., 0.70710678, 0.70710678,\n", " 0.70710678]),\n", " [],\n", " 'DataArray_t'],\n", " ['VelocityY',\n", " array([-0.00649314, -0.00789719, -0.01000445, ..., 0.11526278,\n", " 0.11136508, 0.10735391]),\n", " [],\n", " 'DataArray_t'],\n", " ['VelocityZ',\n", " array([0.70707697, 0.70706268, 0.707036 , ..., 0.69764926, 0.69828205,\n", " 0.69890996]),\n", " [],\n", " 'DataArray_t']],\n", " 'FlowSolution_t']" ] }, "metadata": { "after": null, "completed": null, "data": {}, "engine_id": 0, "engine_uuid": "7a177389-9494ed593e6c36e4016c6419", "error": null, "execute_input": "PT.new_FlowSolution('FlowSolution',\n loc='Vertex',\n fields={\n 'VelocityX' : vx,\n 'VelocityY' : vy,\n 'VelocityZ' : vz},\n parent=zone)\n", "execute_result": { "data": { "text/plain": "['FlowSolution',\n None,\n [['GridLocation',\n array([b'V', b'e', b'r', b't', b'e', b'x'], dtype='|S1'),\n [],\n 'GridLocation_t'],\n ['VelocityX',\n array([0.70710678, 0.70710678, 0.70710678, ..., 0.70710678, 0.70710678,\n 0.70710678]),\n [],\n 'DataArray_t'],\n ['VelocityY',\n array([-0.00649314, -0.00789719, -0.01000445, ..., 0.11526278,\n 0.11136508, 0.10735391]),\n [],\n 'DataArray_t'],\n ['VelocityZ',\n array([0.70707697, 0.70706268, 0.707036 , ..., 0.69764926, 0.69828205,\n 0.69890996]),\n [],\n 'DataArray_t']],\n 'FlowSolution_t']" }, "execution_count": 6, "metadata": {} }, "follow": null, "msg_id": null, "outputs": [], "received": null, "started": null, "status": null, "stderr": "", "stdout": "", "submitted": "2026-02-02T08:41:40.938857Z" }, "output_type": "display_data" } ], "source": [ "PT.new_FlowSolution('FlowSolution',\n", " loc='Vertex',\n", " fields={\n", " 'VelocityX' : vx,\n", " 'VelocityY' : vy,\n", " 'VelocityZ' : vz},\n", " parent=zone)" ] }, { "cell_type": "markdown", "id": "146e360a", "metadata": {}, "source": [ "```{note}\n", "An alternative approach would have be to convert the mesh into cylindrical coordinates,\n", "then to create the FlowSolution with the uniform field $(0, \\alpha, \\alpha)$ (using this time\n", "R, Theta, and Z suffixes) before moving back the mesh to cartesian coordinates.\n", "Try it if you want!\n", "```\n", "\n", "### 360° duplication\n", "\n", "👉 It is time to apply the duplication operation with {func}`~maia.algo.dist.duplicate_from_rotation_jns_to_360`.\n", "\n", "❓ This function takes as an input the paths of the `GridConnectivity_t` nodes that define\n", "the periodic transformation. What is wrong with this mesh ?\n", "\n", "```{toggle}\n", "Our mesh has no `GridConnectivity_t` nodes ! Apparently, the meshing tool did not store it and we need to\n", "recover it. Congratulations if you anticipated it at the begining of this tutorial 🏅️\n", "```\n", "\n", "#### Recovering GridConnectivity_t nodes\n", "\n", "In fact, the concerned boundary faces are stored in two `BC_t` nodes : `perleft` and `perright`; it is 'only'\n", "the 1-to-1 mapping which is missing.\n", "\n", "✏️ Read the documentation of {func}`~maia.algo.dist.connect_1to1_families` and call it to rebuild the 1to1 connection\n", "between these two subsets.\n", "\n", "```{tip}\n", "The rotation angle to go from perleft subset to perright subset is `2*pi/36`\n", "```" ] }, { "cell_type": "code", "execution_count": null, "id": "c813e654", "metadata": { "mystnb": { "code_prompt_hide": "Hide solution", "code_prompt_show": "Show solution" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "a = 2*np.pi / 36\n", "maia.algo.dist.connect_1to1_families(tree,\n", " ('perleft', 'perright'), \n", " comm,\n", " periodic={'rotation_angle' : np.array([a, 0, 0])})" ] }, { "cell_type": "markdown", "id": "e8fa09c9", "metadata": {}, "source": [ "✏️ Then check the presence of `GridConnectivity_t` nodes in the tree and note their path." ] }, { "cell_type": "code", "execution_count": null, "id": "512e6433", "metadata": { "mystnb": { "code_prompt_hide": "Hide solution", "code_prompt_show": "Show solution" }, "tags": [ "hide-cell" ] }, "outputs": [ { "data": { "text/plain": [ "[stdout:0] \n", " CGNSTree CGNSTree_t \n", " ├───CGNSLibraryVersion CGNSLibraryVersion_t R4 [4.2]\n", " └───Base CGNSBase_t I4 [3 3]\n", " ├───Amont Family_t \n", " │ └───FamilyBC FamilyBC_t \"BCInflowSubsonic\"\n", " ├───Aval Family_t \n", " │ └───FamilyBC FamilyBC_t \"BCOutflowSubsonic\"\n", " ├───Carter Family_t \n", " │ └───FamilyBC FamilyBC_t \"BCWallViscous\"\n", " ├───AubeMoyeu Family_t \n", " │ └───FamilyBC FamilyBC_t \"BCWallViscous\"\n", " ├───Rotor Zone_t I4 [[211897 197568 0]]\n", " │ ├───ZoneType ZoneType_t \"Unstructured\"\n", " │ ├───GridCoordinates GridCoordinates_t \n", " │ │ ├───CoordinateX DataArray_t R8 (52975,)\n", " │ │ ├───CoordinateY DataArray_t R8 (52975,)\n", " │ │ └───CoordinateZ DataArray_t R8 (52975,)\n", " │ ├───ZoneBC ZoneBC_t \n", " │ │ ├───bc_AubeMoyeu BC_t \"FamilySpecified\"\n", " │ │ │ ╵╴╴╴ (4 children masked)\n", " │ │ ├───bc_Carter BC_t \"FamilySpecified\"\n", " │ │ │ ╵╴╴╴ (4 children masked)\n", " │ │ ├───bc_Amont BC_t \"FamilySpecified\"\n", " │ │ │ ╵╴╴╴ (4 children masked)\n", " │ │ └───bc_Aval BC_t \"FamilySpecified\"\n", " │ │ ╵╴╴╴ (4 children masked)\n", " │ ├───:CGNS#Distribution UserDefinedData_t \n", " │ │ ├───Vertex DataArray_t I4 [ 0 52975 211897]\n", " │ │ └───Cell DataArray_t I4 [ 0 49392 197568]\n", " │ ├───NGonElements Elements_t I4 [22 0]\n", " │ │ ├───ElementRange IndexRange_t I4 [ 1 606880]\n", " │ │ ├───ElementStartOffset DataArray_t I4 (151793,)\n", " │ │ ├───ElementConnectivity DataArray_t I4 (607168,)\n", " │ │ ├───ParentElements DataArray_t I4 (151792, 2)\n", " │ │ └───:CGNS#Distribution UserDefinedData_t \n", " │ │ ╵╴╴╴ (2 children masked)\n", " │ ├───NFaceElements Elements_t I4 [23 0]\n", " │ │ ├───ElementRange IndexRange_t I4 [606881 804448]\n", " │ │ ├───ElementStartOffset DataArray_t I4 (49393,)\n", " │ │ ├───ElementConnectivity DataArray_t I4 (296352,)\n", " │ │ └───:CGNS#Distribution UserDefinedData_t \n", " │ │ ╵╴╴╴ (2 children masked)\n", " │ ├───FlowSolution FlowSolution_t \n", " │ │ ├───GridLocation GridLocation_t \"Vertex\"\n", " │ │ ├───VelocityX DataArray_t R8 (52975,)\n", " │ │ ├───VelocityY DataArray_t R8 (52975,)\n", " │ │ └───VelocityZ DataArray_t R8 (52975,)\n", " │ └───ZoneGridConnectivity ZoneGridConnectivity_t \n", " │ ├───perleft_0 GridConnectivity_t \"Base/Rotor\"\n", " │ │ ╵╴╴╴ (8 children masked)\n", " │ └───perright_0 GridConnectivity_t \"Base/Rotor\"\n", " │ ╵╴╴╴ (8 children masked)\n", " ├───perright Family_t \n", " │ └───FamilyBC FamilyBC_t \"UserDefined\"\n", " └───perleft Family_t \n", " └───FamilyBC FamilyBC_t \"UserDefined\"\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if comm.Get_rank() == 0:\n", " PT.print_tree(tree, max_depth=4)" ] }, { "cell_type": "markdown", "id": "a68b82af", "metadata": {}, "source": [ "#### Effective duplication\n", "\n", "Now that tree has periodic joins, we can call the duplication function.\n", "\n", "✏️ Use the paths of the `GridConnectivity_t` created nodes to call the duplication function." ] }, { "cell_type": "code", "execution_count": null, "id": "78ee6c09", "metadata": { "mystnb": { "code_prompt_hide": "Hide solution", "code_prompt_show": "Show solution" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "zone_paths = [\"Base/Rotor\"]\n", "left_jns = [\"Base/Rotor/ZoneGridConnectivity/perleft_0\"]\n", "right_jns = [\"Base/Rotor/ZoneGridConnectivity/perright_0\"]\n", "\n", "maia.algo.dist.duplicate_from_rotation_jns_to_360(tree,\n", " zone_paths,\n", " jn_paths_for_dupl = (left_jns, right_jns),\n", " comm = comm)" ] }, { "cell_type": "markdown", "id": "61e3b4df", "metadata": {}, "source": [ "❓ How many `Zone_t` nodes are registered in the tree after the duplication ?\n", "\n", "```{toggle}\n", "The tree has 36 zones after the duplication operation\n", "(since the periodicity angle was $\\frac{2\\pi}{36}$).\n", "You can check this with `len(PT.get_all_Zone_t(tree))`.\n", "```\n", "\n", "### Merging zones\n", "\n", "As you noticed, the tree has now several physical zones (due to the duplication).\n", "Sometimes it can be useful to merge these zones, which are still connected throught\n", "1to1 matching joins, into a single one, for example:\n", "- to call a tool that does not manage multiblock meshes (e.g. mesh adaptation)\n", "- to avoid interface management in the solver.\n", "\n", "Search in the {ref}`documentation ` the function to call to merge the zones.\n", "\n", "✏️ Call the function and check again the number of ``Zone_t`` nodes in the output tree." ] }, { "cell_type": "code", "execution_count": null, "id": "32b0b79f", "metadata": { "mystnb": { "code_prompt_hide": "Hide solution", "code_prompt_show": "Show solution" }, "tags": [ "hide-cell" ] }, "outputs": [ { "data": { "text/plain": [ "[stdout:0] The number zones is now 1\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "maia.algo.dist.merge_connected_zones(tree, comm)\n", "\n", "if comm.Get_rank() == 0:\n", " print(f\"The number zones is now {len(PT.get_all_Zone_t(tree))}\")" ] }, { "cell_type": "markdown", "id": "3dbd9e73", "metadata": {}, "source": [ "### Conclusion\n", "\n", "Our preprocessing workflow is completed 🚀!\n", "\n", "This is what the final tree looks like:" ] }, { "cell_type": "code", "execution_count": null, "id": "7c538480", "metadata": { "tags": [ "scroll_output", "hide-output" ] }, "outputs": [ { "data": { "text/plain": [ "[stdout:0] \n", " CGNSTree CGNSTree_t \n", " ├───CGNSLibraryVersion CGNSLibraryVersion_t R4 [4.2]\n", " └───Base CGNSBase_t I4 [3 3]\n", " ├───Amont Family_t \n", " │ └───FamilyBC FamilyBC_t \"BCInflowSubsonic\"\n", " ├───Aval Family_t \n", " │ └───FamilyBC FamilyBC_t \"BCOutflowSubsonic\"\n", " ├───Carter Family_t \n", " │ └───FamilyBC FamilyBC_t \"BCWallViscous\"\n", " ├───AubeMoyeu Family_t \n", " │ └───FamilyBC FamilyBC_t \"BCWallViscous\"\n", " ├───perright Family_t \n", " │ └───FamilyBC FamilyBC_t \"UserDefined\"\n", " ├───perleft Family_t \n", " │ └───FamilyBC FamilyBC_t \"UserDefined\"\n", " └───mergedZone0 Zone_t I4 [[7432272 7112448 0]]\n", " ├───ZoneType ZoneType_t \"Unstructured\"\n", " ├───NGonElements Elements_t I4 [22 0]\n", " │ ├───ElementRange IndexRange_t I4 [ 1 21657600]\n", " │ ├───ElementStartOffset DataArray_t I4 (5424961,)\n", " │ ├───ElementConnectivity DataArray_t I4 (21699840,)\n", " │ ├───ParentElements DataArray_t I4 (5424960, 2)\n", " │ └───:CGNS#Distribution UserDefinedData_t \n", " │ ├───Element DataArray_t I4 [ 0 5424960 21657600]\n", " │ └───ElementConnectivity DataArray_t I4 [ 0 21699840 86630400]\n", " ├───GridCoordinates GridCoordinates_t \n", " │ ├───CoordinateX DataArray_t R8 (1868958,)\n", " │ ├───CoordinateY DataArray_t R8 (1868958,)\n", " │ └───CoordinateZ DataArray_t R8 (1868958,)\n", " ├───FlowSolution FlowSolution_t \n", " │ ├───VelocityX DataArray_t R8 (1868958,)\n", " │ ├───VelocityY DataArray_t R8 (1868958,)\n", " │ ├───VelocityZ DataArray_t R8 (1868958,)\n", " │ └───GridLocation GridLocation_t \"Vertex\"\n", " ├───ZoneBC ZoneBC_t \n", " │ ├───bc_AubeMoyeu BC_t \"FamilySpecified\"\n", " │ │ ├───PointList IndexArray_t I4 (1, 87120)\n", " │ │ ├───GridLocation GridLocation_t \"FaceCenter\"\n", " │ │ ├───FamilyName FamilyName_t \"AubeMoyeu\"\n", " │ │ └───:CGNS#Distribution UserDefinedData_t \n", " │ │ └───Index DataArray_t I4 [ 0 87120 348480]\n", " │ ├───bc_Carter BC_t \"FamilySpecified\"\n", " │ │ ├───PointList IndexArray_t I4 (1, 57168)\n", " │ │ ├───GridLocation GridLocation_t \"FaceCenter\"\n", " │ │ ├───FamilyName FamilyName_t \"Carter\"\n", " │ │ └───:CGNS#Distribution UserDefinedData_t \n", " │ │ └───Index DataArray_t I4 [ 0 57168 228672]\n", " │ ├───bc_Amont BC_t \"FamilySpecified\"\n", " │ │ ├───PointList IndexArray_t I4 (1, 7920)\n", " │ │ ├───GridLocation GridLocation_t \"FaceCenter\"\n", " │ │ ├───FamilyName FamilyName_t \"Amont\"\n", " │ │ └───:CGNS#Distribution UserDefinedData_t \n", " │ │ └───Index DataArray_t I4 [ 0 7920 31680]\n", " │ └───bc_Aval BC_t \"FamilySpecified\"\n", " │ ├───PointList IndexArray_t I4 (1, 7920)\n", " │ ├───GridLocation GridLocation_t \"FaceCenter\"\n", " │ ├───FamilyName FamilyName_t \"Aval\"\n", " │ └───:CGNS#Distribution UserDefinedData_t \n", " │ └───Index DataArray_t I4 [ 0 7920 31680]\n", " └───:CGNS#Distribution UserDefinedData_t \n", " ├───Vertex DataArray_t I4 [ 0 1868958 7432272]\n", " └───Cell DataArray_t I4 [ 0 1778112 7112448]\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if comm.Get_rank() == 0:\n", " PT.print_tree(tree)" ] }, { "cell_type": "markdown", "id": "6777ee88", "metadata": {}, "source": [ "👉 If you want to create a visualisation, you can save it using {func}`~maia.io.dist_tree_to_file`.\n", "Be careful with the large mesh, as it requires more than 70 GiB of disk space.\n", "\n", "You can also download the final script {download}`02_pre.py`." ] }, { "cell_type": "code", "execution_count": null, "id": "bd3a8871", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [ { "data": { "text/plain": [ "[stdout:0] Partitioning tree of 1 initial block...\n", "Partitioning completed (7.99 s) -- Nb of cells for current rank is 1.8M (Σ=7.1M)\n", "Extraction from Family \"ALLBCS\" completed (0.66 s) -- Extracted tree has locally 160.1K faces (Σ=640.5K)\n", "Distributed write of a 9.8MiB dist_tree (Σ=39.1MiB)...\n", "Write completed [output_rotor37_med.cgns] (0.82 s)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# This snipper prepares tree for illustration (write only surfaces)\n", "ptree = maia.factory.partition_dist_tree(tree, comm, data_transfer='ALL')\n", "\n", "for bc in PT.get_nodes_from_label(ptree, 'BC_t'):\n", " PT.new_FamilyName('ALLBCS', as_additional='AdditionalFamilyName', parent=bc)\n", "\n", "ext = maia.algo.part.extract_part_from_family(ptree, 'ALLBCS', comm, containers_name='ALL')\n", "dext = maia.factory.recover_dist_tree(ext, comm, data_transfer='ALL')\n", "PT.rm_nodes_from_name(dext, 'ZoneBC')\n", "maia.algo.dist.convert_ngon_to_elements(dext, comm)\n", "maia.io.dist_tree_to_file(dext, 'output_rotor37_med.cgns', comm)" ] }, { "cell_type": "code", "execution_count": null, "id": "a02837f9", "metadata": { "tags": [ "remove-cell", "no-parallel" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "target_fig" } }, "output_type": "display_data" } ], "source": [ "# This snippet generate the illustration\n", "from utils import PIL_hstack\n", "\n", "\n", "import pyvista as pv\n", "pv.set_jupyter_backend('static')\n", "\n", "# Plot initial mesh\n", "plotter = pv.Plotter(off_screen=True)\n", "plotter.camera_position = [\n", " (-0.367, -0.036, 0.164), # Camera Position\n", " (-0.0635, 0.15867, 0.023), # Focal Point\n", " (0.268, 0.254, 0.929) # View Up\n", "]\n", "plotter.camera.view_angle = 30.0 # View Angle\n", "\n", "mesh_in = pv.read(\"rotor37_medium_ng.cgns\")\n", "plotter.add_mesh(mesh_in, show_edges=False, edge_color='#808080')\n", "plotter.add_axes()\n", "\n", "img_2d = plotter.screenshot(return_img=True, window_size=[600,600])\n", "\n", "# Plot final mesh (with field) + initial mesh (edges only)\n", "plotter = pv.Plotter(off_screen=True)\n", "plotter.camera_position = [\n", " (-0.780, -0.478, 0.360), # Camera Position\n", " (0.0073, 0.0272, -0.00576), # Focal Point\n", " (0.268, 0.254, 0.929) # View Up\n", "]\n", "plotter.camera.view_angle = 30.0 # View Angle\n", "\n", "\n", "# Plot 3D img\n", "reader = pv.CGNSReader(\"output_rotor37_med.cgns\")\n", "mesh_out = reader.read()['Base']['Zone']#['Internal']\n", "\n", "\n", "#sargs = dict(title='TurbulentDistance\\n', n_labels=3, position_x=0.25, fmt='%.2g', title_font_size=20)\n", "plotter.add_mesh(mesh_out)\n", "plotter.add_mesh(mesh_out, scalars='Velocity', component=1, cmap='coolwarm', show_scalar_bar=False)\n", " #scalars='TurbulentDistance', cmap='coolwarm', clim=[0,.5], scalar_bar_args=sargs)\n", "# Hack because SetVerticalTitleSeparation only works for vertical cb,\n", "# so we add line break and set linespacing https://github.com/pyvista/pyvista/discussions/4668\n", "#plotter.scalar_bar.GetTitleTextProperty().SetLineSpacing(0.5)\n", "\n", "\n", "actor = plotter.add_mesh(mesh_in['Base']['Rotor']['Internal'].extract_feature_edges(), \n", " line_width=1.5, color='black')\n", "# Fixup edge display (better rendering)\n", "actor.mapper.SetResolveCoincidentTopologyToPolygonOffset()\n", "\n", "\n", "img_3d = plotter.screenshot(return_img=True, window_size=[600,600])\n", "\n", "\n", "from PIL import Image\n", "new_im = PIL_hstack([Image.fromarray(img) for img in [img_2d, img_3d]],\n", " margin=5)\n", "\n", "#new_im.save('test.png')\n", "#display(new_im)\n", "from myst_nb import glue\n", "glue(\"target_fig\", new_im)" ] }, { "cell_type": "markdown", "id": "cd516aa1", "metadata": {}, "source": [ "## Performance overview" ] }, { "cell_type": "code", "execution_count": null, "id": "3b619f7e", "metadata": { "tags": [ "remove-cell", "no-parallel" ] }, "outputs": [], "source": [ "import numpy as np\n", "import plotly.graph_objects as go\n", "\n", "import utils\n", "\n", "fig = go.Figure()\n", "\n", "x = [1,2,3,4]#5,6,7,8]\n", "durations = [25.36, 12.84, 9.254, 7.712]#] 9.901, 8.768, 8.236, 8.068]\n", "speedup = [durations[0] / d for d in durations]\n", "\n", "fig.add_trace(\n", " go.Scatter(\n", " x=x, y=x,\n", " mode='lines',\n", " line=dict(color=utils.rtd_warning_title),\n", " name='ideal',\n", " )\n", ")\n", "fig.add_trace(\n", " go.Scatter(\n", " x=x, y=speedup,\n", " mode='markers',\n", " name='result',\n", " marker=dict(color=utils.rtd_note_title),\n", " )\n", ")\n", "\n", "\n", "# 5. finalize figure\n", "fig.update_layout(\n", "xaxis=dict(\n", " title=dict(\n", " text='Number of processes',\n", " ),\n", " showline=True,\n", " linecolor='black',\n", " gridcolor='lightgrey'\n", "),\n", "yaxis=dict(\n", " title=dict(\n", " text='Speedup',\n", " ),\n", " rangemode=\"tozero\",\n", " showline=True,\n", " linecolor='black',\n", " gridcolor='lightgrey'\n", "),\n", "font=dict(\n", " family='Courier New, monospace',\n", " size=14,\n", "),\n", "plot_bgcolor='white',\n", "showlegend=False,\n", ")\n", "\n", "\n", "fig.write_html('../_static/tuto02_perf_med.html')" ] }, { "cell_type": "code", "execution_count": null, "id": "2160a7d5", "metadata": { "tags": [ "remove-cell", "no-parallel" ] }, "outputs": [], "source": [ "import numpy as np\n", "import plotly.graph_objects as go\n", "from plotly.subplots import make_subplots\n", "\n", "import utils\n", "\n", "fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.2)\n", "\n", "n_cell_tot = 762100488\n", "pp = 48\n", "x = [4,5,6,7,8,9,10,11,12,13,14,15]\n", "durations = [37.26, 29.36, 24.55, 21.92, 19.79, 17.74, 17.47, 15.86, 14.77, 14.24, 13.47, 14.03]\n", "speedup = [4*durations[0] / d for d in durations]\n", "eff = [d*(p*pp)/n_cell_tot for (p,d) in zip(x,durations)]\n", "\n", "\n", "#x = [2,3,4,5]\n", "#pp = 96\n", "#durations = [32.29, 22.44, 18.74, 16.9]\n", "#eff = [d*(p*pp)/n_cell_tot for (p,d) in zip(x,durations)]\n", "\n", "fig.add_trace(\n", " go.Scatter(\n", " x=x, y=x,\n", " mode='lines',\n", " line=dict(color=utils.rtd_warning_title),\n", " name='ideal',\n", " ),\n", " row=1,col=1\n", ")\n", "fig.add_trace(\n", " go.Scatter(\n", " x=x, y=speedup,\n", " mode='markers',\n", " name='result',\n", " line=dict(color=utils.rtd_note_title),\n", " ),\n", " row=1,col=1\n", ")\n", "\n", "\n", "fig.add_trace(\n", " go.Scatter(\n", " x=x, y=[durations[0]*x[0]*pp/n_cell_tot for _ in x],\n", " mode='lines',\n", " line=dict(color=utils.rtd_warning_title),\n", " name='ideal',\n", " ),\n", " row=1,col=2\n", ")\n", "fig.add_trace(\n", " go.Scatter(\n", " x=x, y=eff,\n", " mode='markers',\n", " name='result',\n", " line=dict(color=utils.rtd_note_title),\n", " ),\n", " row=1,col=2\n", ")\n", "\n", "\n", "# 5. finalize figure\n", "fig.update_xaxes(\n", " title=dict(\n", " text='Number of nodes',\n", " ),\n", " showline=True,\n", " linecolor='black',\n", " gridcolor='lightgrey'\n", ")\n", "fig.update_yaxes(\n", " title=dict(\n", " text='Speedup',\n", " ),\n", " rangemode=\"tozero\",\n", " showline=True,\n", " linecolor='black',\n", " gridcolor='lightgrey',\n", " title_standoff=2,\n", " row=1, col=1\n", ")\n", "fig.update_yaxes(\n", " title=dict(\n", " text='Duration / dof (s)',\n", " ),\n", " rangemode=\"tozero\",\n", " showline=True,\n", " linecolor='black',\n", " gridcolor='lightgrey',\n", " title_standoff=2,\n", " row=1, col=2\n", ")\n", "\n", "fig.update_layout(\n", "font=dict(\n", " family='Courier New, monospace',\n", " size=14,\n", "),\n", "plot_bgcolor='white',\n", "showlegend=False,\n", ")\n", "\n", "fig.write_html('../_static/tuto02_perf_large.html')" ] }, { "cell_type": "markdown", "id": "5091dcce", "metadata": {}, "source": [ "This mini workflow provides an opportunity to take a quick look at performance.\n", "\n", "We slightly modify the input script to apply the following methodology: imports and file\n", "reading are performed once, after which the workflow is run five times, measuring elapsed time.\n", "The mean value of these five runs is then computed, and we repeat the operation for different\n", "number of processes.\n", "\n", "The medium sized mesh is well suited for this study if we only have access to a laptop or a workstation.\n", "This is what we get on a Intel Xeon (4 cores) CPU:\n", "\n", "```{raw} html\n", "\n", "```\n", "\n", "The quantity plotted is the parallel speedup defined as {math}`T_1/T_p` as a fonction\n", "of {math}`p`, where {math}`T_1` is the serial execution time and {math}`T_p` is the\n", "parallel execution time on {math}`p` processes.\n", "\n", "The orange line represents the ideal speedup; we can see that the scaling is quite good.\n", "In addition, it is worth noting that:\n", "\n", "```{important}\n", "- No changes to the script are required for parallel execution,\n", "- The output file is exactly the same, whatever the number of processes.\n", "```\n", "\n", "In order to conduct the study on the large mesh, we will need to run the cases on a supercomputer.\n", "The material configuration is a cluster of Cascade Lake CPUs, each node of the cluster\n", "having 2 CPUs of 24 cores (and thus 48 processes).\n", "\n", "Firstly, we observe that the run fails if the number of nodes is less than four\n", "because the requested memory is too high. Consequently, we use the elapsed time on four nodes\n", "as the basis for calculating the speed-up, represented on the left of the figure below.\n", "On the right, we plot the parallel execution time divided by the number of cells per process.\n", "Once again, the orange line represents the ideal value (this ratio should remain constant).\n", "\n", "```{raw} html\n", "\n", "```\n", "\n", "On both figures, we can see that the scaling is good up to 10 nodes (480p)\n", "but decreases slightly thereafter.\n", "This number of processes corresponds to approximately 1.6 million cells per rank,\n", "which is roughly the value we recommend for pre-processing workflows.\n", "\n", "\n", "The time taken for each cell remains at around 10 µs for all runs, meaning that the cost\n", "of this workflow is roughly equivalent to ten solver iterations.\n", "To conclude, we can state that, from user point of view,\n", "\n", "```{important}\n", "- The MPI parallelisation allows to use more nodes when the requested memory is too important,\n", "- On 10 nodes, the excution time of this exemple workflow is lower than **20 seconds**.\n", "```" ] }, { "cell_type": "code", "execution_count": null, "id": "4de53fba", "metadata": { "tags": [ "remove-cell", "no-parallel" ] }, "outputs": [ { "data": { "text/plain": [ "'\\nMaillage medium\\nLD8\\nx = [1,2,3,4,5,6,7,8]\\ndurations = [25.36, 12.84, 9.254, 7.712, 9.901, 8.768, 8.236, 8.068]\\n\\nSator, noeud cascade lake (48p) 826\\nx = [1,2,3,4,5,6,7,8,9,10,11,12,14,16,18,20,24,28,32,36,40,44,48]\\nRemplissage par groupe (0,1,2,3, ...)\\ndurations = [29.21, 15.09, 10.28, 8.132, 6.916, 6.023, 5.514, 5.168, 4.818, 4.565, 4.382, 4.183,\\n 3.664, 3.351, 3.039, 2.842, 2.522, 2.254, 2.050, 1.881, 1.815, 1.741, 1.578]\\n# Remplissage par package : 0, 24, 1, 25, 2, 26, ...\\ndurations = [29.16, 14.66, 9.945, 7.633, 6.400, 5.493, 4.956, 4.472, 4.109, 3.796, 3.578, 3.291,\\n 3.006, 2.811, 2.610, 2.491, 2.307, 2.061, 1.908, 1.766, 1.749, 1.707, 1.566]\\nRemplissage par Numa : 0, 12, 24, 36, 1, 13, 25, 37, ...\\nNote : semble être le comportement de slurm\\ndurations = [29.16, 14.66, 9.717, 7.461, 6.277, 5.417, 4.784, 4.339, 3.930, 3.619, 3.349, 3.050,\\n 2.756, 2.504, 2.291, 2.127, 1.883, 1.744, 1.700, 1.630, 1.674, 1.686, 1.577]\\n\\nJuno, noeud n016 saphire rapids (96p)\\nx = [1, 2, 4, 8, 16, 32, 48, 64, 72, 80, 96]\\nt = [19.36, 10.26, 5.02, 2.89, 1.53, 0.95, 0.778, 0.717, 0.723, 0.731, 0.727]\\n\\n\\n\\nMaillage large \\n\\nJuno saphire 96p \\nNB : 1er calcul dépeuplé sur 2 noeuds. Pour les autres le noeud est plein\\nx = [96, 192, 288, 384, 480]\\nt = [41.67, 31.93, 22.46, 18.15, 15.65]\\n\\nSator saphire 96p \\nNB : 1er calcul dépeuplé sur 2 noeuds. Pour les autres le noeud est plein\\nx = [96, 192, 288, 384, 480]\\nt = [40.96, 32.29, 22.44, 18.74, 16.90]\\nMeme noeud, mais en dépeuplant tjrs (48 procs / noeud du coup)\\nx = [2,3,4,5,6,7,8,9,10] #Node\\nt = [40.96, 29.68, 23.57, 18.65, 15.41, 13.85, 12.43, 13.33, 11.04]\\n\\n\\nSator cascade lake 48p\\nx = [4,5,6,7,8,9,10,11,12,13,14,15] #nb node\\ndurations = [37.26, 29.36, 24.55, 21.92, 19.79, 17.74, 17.47, 15.86, 14.77, 14.24, 13.47, 14.03]\\n\\n'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "Maillage medium\n", "LD8\n", "x = [1,2,3,4,5,6,7,8]\n", "durations = [25.36, 12.84, 9.254, 7.712, 9.901, 8.768, 8.236, 8.068]\n", "\n", "Sator, noeud cascade lake (48p) 826\n", "x = [1,2,3,4,5,6,7,8,9,10,11,12,14,16,18,20,24,28,32,36,40,44,48]\n", "Remplissage par groupe (0,1,2,3, ...)\n", "durations = [29.21, 15.09, 10.28, 8.132, 6.916, 6.023, 5.514, 5.168, 4.818, 4.565, 4.382, 4.183,\n", " 3.664, 3.351, 3.039, 2.842, 2.522, 2.254, 2.050, 1.881, 1.815, 1.741, 1.578]\n", "# Remplissage par package : 0, 24, 1, 25, 2, 26, ...\n", "durations = [29.16, 14.66, 9.945, 7.633, 6.400, 5.493, 4.956, 4.472, 4.109, 3.796, 3.578, 3.291,\n", " 3.006, 2.811, 2.610, 2.491, 2.307, 2.061, 1.908, 1.766, 1.749, 1.707, 1.566]\n", "Remplissage par Numa : 0, 12, 24, 36, 1, 13, 25, 37, ...\n", "Note : semble être le comportement de slurm\n", "durations = [29.16, 14.66, 9.717, 7.461, 6.277, 5.417, 4.784, 4.339, 3.930, 3.619, 3.349, 3.050,\n", " 2.756, 2.504, 2.291, 2.127, 1.883, 1.744, 1.700, 1.630, 1.674, 1.686, 1.577]\n", "\n", "Juno, noeud n016 saphire rapids (96p)\n", "x = [1, 2, 4, 8, 16, 32, 48, 64, 72, 80, 96]\n", "t = [19.36, 10.26, 5.02, 2.89, 1.53, 0.95, 0.778, 0.717, 0.723, 0.731, 0.727]\n", "\n", "\n", "\n", "Maillage large \n", "\n", "Juno saphire 96p \n", "NB : 1er calcul dépeuplé sur 2 noeuds. Pour les autres le noeud est plein\n", "x = [96, 192, 288, 384, 480]\n", "t = [41.67, 31.93, 22.46, 18.15, 15.65]\n", "\n", "Sator saphire 96p \n", "NB : 1er calcul dépeuplé sur 2 noeuds. Pour les autres le noeud est plein\n", "x = [96, 192, 288, 384, 480]\n", "t = [40.96, 32.29, 22.44, 18.74, 16.90]\n", "Meme noeud, mais en dépeuplant tjrs (48 procs / noeud du coup)\n", "x = [2,3,4,5,6,7,8,9,10] #Node\n", "t = [40.96, 29.68, 23.57, 18.65, 15.41, 13.85, 12.43, 13.33, 11.04]\n", "\n", "\n", "Sator cascade lake 48p\n", "x = [4,5,6,7,8,9,10,11,12,13,14,15] #nb node\n", "durations = [37.26, 29.36, 24.55, 21.92, 19.79, 17.74, 17.47, 15.86, 14.77, 14.24, 13.47, 14.03]\n", "\n", "\"\"\"" ] } ], "metadata": { "jupytext": { "text_representation": { "format_name": "myst" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "myst_html_secnum_depth": 0 }, "nbformat": 4, "nbformat_minor": 5 }