diff --git a/docs/4-benchpark-setup.rst b/docs/4-benchpark-setup.rst index 22ed8b62a..80b0c58ca 100644 --- a/docs/4-benchpark-setup.rst +++ b/docs/4-benchpark-setup.rst @@ -15,7 +15,7 @@ Also choose a directory for your experiment:: where: - ``benchmark/ProgrammingModel``: amg2023/openmp | amg2023/cuda | saxpy/openmp (available choices in ``benchpark/experiments``) -- ``system``: x86_64 | HPECray-zen3-MI250X-Slingshot | AWS_PCluster_Hpc7a-zen4-EFA (available choices in ``benchpark/configs``) +- ``system``: x86_64 | HPECray-zen3-MI250X-Slingshot | AWS_PCluster_Hpc7a-zen4-EFA (available choices in :doc:`available-system-specs`) This command will assemble a Ramble workspace per experiment with a configuration for the specified benchmark and system diff --git a/docs/available-system-specs.rst b/docs/available-system-specs.rst index e9350713e..0be9ff871 100644 --- a/docs/available-system-specs.rst +++ b/docs/available-system-specs.rst @@ -7,10 +7,9 @@ Current System Configurations ============================= -.. note:: - - The column headers are the current options for the ``system`` name in - Benchpark setup. +The table below provides a directory of information for systems that have been +defined in Benchpark. The column headers in the table below are available for +use as the ``system`` parameter in Benchpark setup. .. csv-table:: Current system definitions in Benchpark. :file: current-system-definitions.csv diff --git a/docs/test.ipynb b/docs/test.ipynb deleted file mode 100644 index 86515bd79..000000000 --- a/docs/test.ipynb +++ /dev/null @@ -1,524 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "4e9d66ee", - "metadata": { - "execution": { - "iopub.execute_input": "2023-12-27T06:14:42.143338Z", - "iopub.status.busy": "2023-12-27T06:14:42.143026Z", - "iopub.status.idle": "2023-12-27T06:14:43.101780Z", - "shell.execute_reply": "2023-12-27T06:14:43.101503Z" - }, - "papermill": { - "duration": 0.967459, - "end_time": "2023-12-27T06:14:43.102440", - "exception": false, - "start_time": "2023-12-27T06:14:42.134981", - "status": "completed" - }, - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "import yaml\n", - "import glob\n", - "from IPython.display import display\n", - "from IPython.display import HTML\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "50232bcb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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ISAx86_64x86_64ppc64lex86_64x86_64
uArchzen4zen3power9icelakeAny
acceleratorvendorAMDNVIDIANoneNone
nameMI250XV100NoneNone
ISAGCNPTXNoneNone
uArchgfx90asm_70NoneNone
interconnectvendorAWSHPECrayMellanoxIntelAny
nameEFASlingshot11EDR-InfinibandOmniPathAny
system-testedsiteAWSLLNLLLNLLLNLAny
nametiogalassenmagmaAny
installation-year202220182019Any
descriptionhttps://aws.amazon.com/ec2/instance-types/hpc7ahttps://www.top500.org/system/180052https://www.top500.org/system/179398https://www.top500.org/system/179774Any
top500-system-instances[][Frontier (ORNL), Lumi (CSC), Tioga (LLNL)][Sierra (LLNL)][Magma (LLNL)][Many]
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def main():\n", - " sysconfig_yaml_files = glob.glob(\"../configs/**/system_definition.yaml\", recursive=True)\n", - "\n", - " df_list = []\n", - " for f in sysconfig_yaml_files:\n", - " with open(f, \"r\") as stream:\n", - " try:\n", - " data = yaml.safe_load(stream)\n", - " except yaml.YAMLError as exc:\n", - " print(exc)\n", - " \n", - " df = pd.json_normalize(data, max_level=2)\n", - " df_list.append(df)\n", - "\n", - " mega_df = pd.concat(df_list)\n", - " mega_df.set_index(\"system_definition.name\", inplace=True)\n", - " mega_df.index.name = \"\"\n", - " mega_df_transpose = mega_df.T\n", - "\n", - " mega_df_transpose[\"myname\"] = mega_df_transpose.index\n", - " mega_df_transpose[[\"first\", \"second\", \"third\"]] = mega_df_transpose[\"myname\"].str.split(\".\", n=2, expand=True)\n", - " mega_df_transpose.fillna(\"\", inplace=True) \n", - " mega_df_transpose.drop([\"first\", \"myname\"], axis=1, inplace=True)\n", - " mega_df_transpose.set_index([\"second\", \"third\"], inplace=True)\n", - " \n", - " display(HTML(mega_df_transpose.to_html()))\n", - "\n", - " mega_df_transpose.to_csv(\"example.csv\")\n", - " \n", - "if __name__ == \"__main__\":\n", - " main()" - ] - }, - { - "cell_type": "markdown", - "id": "4e937506", - "metadata": {}, - "source": [ - "## Experimenting with different row names" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "99e4b7e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
AWS_PCluster_Hpc7a-zen4-EFAHPECray-zen3-MI250X-SlingshotIBM-power9-V100-InfinibandPenguin-icelake-OmniPathx86_64
secondthird
integratorvendorAWSHPECrayIBMPenguinComputingAny
nameParallelCluster3.7.2-Hpc7aEX235aAC922RelionClusterAny
processorvendorAMDAMDIBMIntelAny
nameEPYC-Zen4EPYC-Zen3POWER9XeonPlatinum924248CAny
ISAx86_64x86_64ppc64lex86_64x86_64
uArchzen4zen3power9icelakeAny
acceleratorvendorAMDNVIDIA
nameMI250XV100
ISAGCNPTX
uArchgfx90asm_70
interconnectvendorAWSHPECrayMellanoxIntelAny
nameEFASlingshot11EDR-InfinibandOmniPathAny
system-testedsiteAWSLLNLLLNLLLNLAny
nametiogalassenmagmaAny
installation-year202220182019Any
descriptionhttps://aws.amazon.com/ec2/instance-types/hpc7ahttps://www.top500.org/system/180052https://www.top500.org/system/179398https://www.top500.org/system/179774Any
top500-system-instances['']['Frontier (ORNL)', 'Lumi (CSC)', 'Tioga (LLNL)']['Sierra (LLNL)']['Magma (LLNL)']['Many']
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df = pd.read_csv(\"test.csv\")\n", - "df.rename(columns=df.iloc[0,3:], inplace=True)\n", - "df.drop([\"first\"], axis=1, inplace=True) # drop \"system_definition\" index level\n", - "df.drop([0], inplace=True) # drop name column (moved to column headers)\n", - "df.fillna(\"\", inplace=True) # replaces NaN with empty string, NaN occurs if value in key:value is left empty or if key does not exist\n", - "df.set_index([\"second\", \"third\"], inplace=True)\n", - "display(HTML(df.to_html()))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "569894f4", - "metadata": {}, - "outputs": [], - "source": [ - "#df2 = df.copy()\n", - "#df2.reset_index(inplace=True)\n", - "#df2.drop([\"first\"], axis=1, inplace=True)\n", - "#df2.fillna(\"\", inplace=True) # replaces NaN with empty string, NaN occurs if value in key:value is left empty or if key does not exist\n", - "#df2.set_index([\"second\", \"third\"], inplace=True)\n", - "#display(HTML(df2.to_html()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "947796c8", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "papermill": { - "default_parameters": {}, - "duration": 3.926654, - "end_time": "2023-12-27T06:14:45.334123", - "environment_variables": {}, - "exception": null, - "input_path": "01_thicket_tutorial.ipynb", - "output_path": "01_thicket_tutorial.ipynb", - "parameters": {}, - "start_time": "2023-12-27T06:14:41.407469", - "version": "2.5.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}