Compare commits
9 Commits
yx-fmisc
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cjy-oneapi
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3
.gitignore
vendored
3
.gitignore
vendored
@@ -1,3 +1,6 @@
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__pycache__
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GW150914
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GW150914-origin
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docs
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*.tmp
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@@ -1,445 +0,0 @@
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##################################################################
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##
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## AMSS-NCKU ABE Test Program (Skip TwoPuncture if data exists)
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## Modified from AMSS_NCKU_Program.py
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## Author: Xiaoqu
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## Modified: 2026/02/01
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##
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##################################################################
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##################################################################
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## Print program introduction
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import print_information
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print_information.print_program_introduction()
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##################################################################
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import AMSS_NCKU_Input as input_data
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##################################################################
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## Create directories to store program run data
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import os
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import shutil
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import sys
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import time
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## Set the output directory according to the input file
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File_directory = os.path.join(input_data.File_directory)
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## Check if output directory exists and if TwoPuncture data is available
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skip_twopuncture = False
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output_directory = os.path.join(File_directory, "AMSS_NCKU_output")
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binary_results_directory = os.path.join(output_directory, input_data.Output_directory)
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if os.path.exists(File_directory):
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print( " Output directory already exists." )
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print()
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# Check if TwoPuncture initial data files exist
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if (input_data.Initial_Data_Method == "Ansorg-TwoPuncture"):
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twopuncture_output = os.path.join(output_directory, "TwoPunctureABE")
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input_par = os.path.join(output_directory, "input.par")
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if os.path.exists(twopuncture_output) and os.path.exists(input_par):
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print( " Found existing TwoPuncture initial data." )
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print( " Do you want to skip TwoPuncture phase and reuse existing data?" )
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print( " Input 'skip' to skip TwoPuncture and start ABE directly" )
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print( " Input 'regenerate' to regenerate everything from scratch" )
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print()
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while True:
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try:
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inputvalue = input()
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if ( inputvalue == "skip" ):
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print( " Skipping TwoPuncture phase, will reuse existing initial data." )
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print()
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skip_twopuncture = True
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break
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elif ( inputvalue == "regenerate" ):
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print( " Regenerating everything from scratch." )
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print()
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skip_twopuncture = False
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break
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else:
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print( " Please input 'skip' or 'regenerate'." )
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except ValueError:
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print( " Please input 'skip' or 'regenerate'." )
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else:
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print( " TwoPuncture initial data not found, will regenerate everything." )
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print()
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# If not skipping, remove and recreate directory
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if not skip_twopuncture:
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shutil.rmtree(File_directory, ignore_errors=True)
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os.mkdir(File_directory)
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os.mkdir(output_directory)
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os.mkdir(binary_results_directory)
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figure_directory = os.path.join(File_directory, "figure")
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os.mkdir(figure_directory)
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shutil.copy("AMSS_NCKU_Input.py", File_directory)
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print( " Output directory has been regenerated." )
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print()
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else:
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# Create fresh directory structure
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os.mkdir(File_directory)
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shutil.copy("AMSS_NCKU_Input.py", File_directory)
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os.mkdir(output_directory)
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os.mkdir(binary_results_directory)
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figure_directory = os.path.join(File_directory, "figure")
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os.mkdir(figure_directory)
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print( " Output directory has been generated." )
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print()
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# Ensure figure directory exists
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figure_directory = os.path.join(File_directory, "figure")
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if not os.path.exists(figure_directory):
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os.mkdir(figure_directory)
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##################################################################
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## Output related parameter information
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import setup
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## Print and save input parameter information
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setup.print_input_data( File_directory )
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if not skip_twopuncture:
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setup.generate_AMSSNCKU_input()
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setup.print_puncture_information()
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##################################################################
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## Generate AMSS-NCKU program input files based on the configured parameters
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if not skip_twopuncture:
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print()
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print( " Generating the AMSS-NCKU input parfile for the ABE executable." )
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print()
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## Generate cgh-related input files from the grid information
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import numerical_grid
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numerical_grid.append_AMSSNCKU_cgh_input()
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print()
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print( " The input parfile for AMSS-NCKU C++ executable file ABE has been generated." )
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print( " However, the input relevant to TwoPuncture need to be appended later." )
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print()
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##################################################################
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## Plot the initial grid configuration
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if not skip_twopuncture:
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print()
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print( " Schematically plot the numerical grid structure." )
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print()
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import numerical_grid
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numerical_grid.plot_initial_grid()
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##################################################################
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## Generate AMSS-NCKU macro files according to the numerical scheme and parameters
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if not skip_twopuncture:
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print()
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print( " Automatically generating the macro file for AMSS-NCKU C++ executable file ABE " )
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print( " (Based on the finite-difference numerical scheme) " )
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print()
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import generate_macrodef
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generate_macrodef.generate_macrodef_h()
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print( " AMSS-NCKU macro file macrodef.h has been generated. " )
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generate_macrodef.generate_macrodef_fh()
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print( " AMSS-NCKU macro file macrodef.fh has been generated. " )
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##################################################################
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# Compile the AMSS-NCKU program according to user requirements
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# NOTE: ABE compilation is always performed, even when skipping TwoPuncture
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print()
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print( " Preparing to compile and run the AMSS-NCKU code as requested " )
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print( " Compiling the AMSS-NCKU code based on the generated macro files " )
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print()
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AMSS_NCKU_source_path = "AMSS_NCKU_source"
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AMSS_NCKU_source_copy = os.path.join(File_directory, "AMSS_NCKU_source_copy")
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## If AMSS_NCKU source folder is missing, create it and prompt the user
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if not os.path.exists(AMSS_NCKU_source_path):
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os.makedirs(AMSS_NCKU_source_path)
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print( " The AMSS-NCKU source files are incomplete; copy all source files into ./AMSS_NCKU_source. " )
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print( " Press Enter to continue. " )
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inputvalue = input()
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# Copy AMSS-NCKU source files to prepare for compilation
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# If skipping TwoPuncture and source_copy already exists, remove it first
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if skip_twopuncture and os.path.exists(AMSS_NCKU_source_copy):
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shutil.rmtree(AMSS_NCKU_source_copy)
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shutil.copytree(AMSS_NCKU_source_path, AMSS_NCKU_source_copy)
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# Copy the generated macro files into the AMSS_NCKU source folder
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if not skip_twopuncture:
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macrodef_h_path = os.path.join(File_directory, "macrodef.h")
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macrodef_fh_path = os.path.join(File_directory, "macrodef.fh")
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else:
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# When skipping TwoPuncture, use existing macro files from previous run
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macrodef_h_path = os.path.join(File_directory, "macrodef.h")
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macrodef_fh_path = os.path.join(File_directory, "macrodef.fh")
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shutil.copy2(macrodef_h_path, AMSS_NCKU_source_copy)
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shutil.copy2(macrodef_fh_path, AMSS_NCKU_source_copy)
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# Compile related programs
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import makefile_and_run
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## Change working directory to the target source copy
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os.chdir(AMSS_NCKU_source_copy)
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## Build the main AMSS-NCKU executable (ABE or ABEGPU)
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makefile_and_run.makefile_ABE()
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## If the initial-data method is Ansorg-TwoPuncture, build the TwoPunctureABE executable
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## Only build TwoPunctureABE if not skipping TwoPuncture phase
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if (input_data.Initial_Data_Method == "Ansorg-TwoPuncture" ) and not skip_twopuncture:
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makefile_and_run.makefile_TwoPunctureABE()
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## Change current working directory back up two levels
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os.chdir('..')
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os.chdir('..')
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print()
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##################################################################
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## Copy the AMSS-NCKU executable (ABE/ABEGPU) to the run directory
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if (input_data.GPU_Calculation == "no"):
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ABE_file = os.path.join(AMSS_NCKU_source_copy, "ABE")
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elif (input_data.GPU_Calculation == "yes"):
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ABE_file = os.path.join(AMSS_NCKU_source_copy, "ABEGPU")
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if not os.path.exists( ABE_file ):
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print()
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print( " Lack of AMSS-NCKU executable file ABE/ABEGPU; recompile AMSS_NCKU_source manually. " )
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print( " When recompilation is finished, press Enter to continue. " )
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inputvalue = input()
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## Copy the executable ABE (or ABEGPU) into the run directory
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shutil.copy2(ABE_file, output_directory)
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## If the initial-data method is TwoPuncture, copy the TwoPunctureABE executable to the run directory
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## Only copy TwoPunctureABE if not skipping TwoPuncture phase
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if (input_data.Initial_Data_Method == "Ansorg-TwoPuncture" ) and not skip_twopuncture:
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TwoPuncture_file = os.path.join(AMSS_NCKU_source_copy, "TwoPunctureABE")
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if not os.path.exists( TwoPuncture_file ):
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print()
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print( " Lack of AMSS-NCKU executable file TwoPunctureABE; recompile TwoPunctureABE in AMSS_NCKU_source. " )
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print( " When recompilation is finished, press Enter to continue. " )
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inputvalue = input()
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## Copy the TwoPunctureABE executable into the run directory
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shutil.copy2(TwoPuncture_file, output_directory)
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##################################################################
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## If the initial-data method is TwoPuncture, generate the TwoPuncture input files
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if (input_data.Initial_Data_Method == "Ansorg-TwoPuncture" ) and not skip_twopuncture:
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print()
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print( " Initial data is chosen as Ansorg-TwoPuncture" )
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print()
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print()
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print( " Automatically generating the input parfile for the TwoPunctureABE executable " )
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print()
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import generate_TwoPuncture_input
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generate_TwoPuncture_input.generate_AMSSNCKU_TwoPuncture_input()
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print()
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print( " The input parfile for the TwoPunctureABE executable has been generated. " )
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print()
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## Generated AMSS-NCKU TwoPuncture input filename
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AMSS_NCKU_TwoPuncture_inputfile = 'AMSS-NCKU-TwoPuncture.input'
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AMSS_NCKU_TwoPuncture_inputfile_path = os.path.join( File_directory, AMSS_NCKU_TwoPuncture_inputfile )
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## Copy and rename the file
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shutil.copy2( AMSS_NCKU_TwoPuncture_inputfile_path, os.path.join(output_directory, 'TwoPunctureinput.par') )
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## Run TwoPuncture to generate initial-data files
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start_time = time.time() # Record start time
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print()
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print()
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## Change to the output (run) directory
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os.chdir(output_directory)
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## Run the TwoPuncture executable
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import makefile_and_run
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makefile_and_run.run_TwoPunctureABE()
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## Change current working directory back up two levels
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os.chdir('..')
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os.chdir('..')
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elif (input_data.Initial_Data_Method == "Ansorg-TwoPuncture" ) and skip_twopuncture:
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print()
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print( " Skipping TwoPuncture execution, using existing initial data." )
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print()
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start_time = time.time() # Record start time for ABE only
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else:
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start_time = time.time() # Record start time
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##################################################################
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## Update puncture data based on TwoPuncture run results
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if not skip_twopuncture:
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import renew_puncture_parameter
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renew_puncture_parameter.append_AMSSNCKU_BSSN_input(File_directory, output_directory)
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## Generated AMSS-NCKU input filename
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AMSS_NCKU_inputfile = 'AMSS-NCKU.input'
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AMSS_NCKU_inputfile_path = os.path.join(File_directory, AMSS_NCKU_inputfile)
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## Copy and rename the file
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shutil.copy2( AMSS_NCKU_inputfile_path, os.path.join(output_directory, 'input.par') )
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print()
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print( " Successfully copy all AMSS-NCKU input parfile to target dictionary. " )
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print()
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else:
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print()
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print( " Using existing input.par file from previous run." )
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print()
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##################################################################
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## Launch the AMSS-NCKU program
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print()
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print()
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## Change to the run directory
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os.chdir( output_directory )
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import makefile_and_run
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makefile_and_run.run_ABE()
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## Change current working directory back up two levels
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os.chdir('..')
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os.chdir('..')
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end_time = time.time()
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elapsed_time = end_time - start_time
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##################################################################
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## Copy some basic input and log files out to facilitate debugging
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## Path to the file that stores calculation settings
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AMSS_NCKU_error_file_path = os.path.join(binary_results_directory, "setting.par")
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## Copy and rename the file for easier inspection
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shutil.copy( AMSS_NCKU_error_file_path, os.path.join(output_directory, "AMSSNCKU_setting_parameter") )
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## Path to the error log file
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AMSS_NCKU_error_file_path = os.path.join(binary_results_directory, "Error.log")
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## Copy and rename the error log
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shutil.copy( AMSS_NCKU_error_file_path, os.path.join(output_directory, "Error.log") )
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## Primary program outputs
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AMSS_NCKU_BH_data = os.path.join(binary_results_directory, "bssn_BH.dat" )
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AMSS_NCKU_ADM_data = os.path.join(binary_results_directory, "bssn_ADMQs.dat" )
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AMSS_NCKU_psi4_data = os.path.join(binary_results_directory, "bssn_psi4.dat" )
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AMSS_NCKU_constraint_data = os.path.join(binary_results_directory, "bssn_constraint.dat")
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## copy and rename the file
|
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shutil.copy( AMSS_NCKU_BH_data, os.path.join(output_directory, "bssn_BH.dat" ) )
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shutil.copy( AMSS_NCKU_ADM_data, os.path.join(output_directory, "bssn_ADMQs.dat" ) )
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shutil.copy( AMSS_NCKU_psi4_data, os.path.join(output_directory, "bssn_psi4.dat" ) )
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shutil.copy( AMSS_NCKU_constraint_data, os.path.join(output_directory, "bssn_constraint.dat") )
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## Additional program outputs
|
||||
if (input_data.Equation_Class == "BSSN-EM"):
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AMSS_NCKU_phi1_data = os.path.join(binary_results_directory, "bssn_phi1.dat" )
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AMSS_NCKU_phi2_data = os.path.join(binary_results_directory, "bssn_phi2.dat" )
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shutil.copy( AMSS_NCKU_phi1_data, os.path.join(output_directory, "bssn_phi1.dat" ) )
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shutil.copy( AMSS_NCKU_phi2_data, os.path.join(output_directory, "bssn_phi2.dat" ) )
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elif (input_data.Equation_Class == "BSSN-EScalar"):
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AMSS_NCKU_maxs_data = os.path.join(binary_results_directory, "bssn_maxs.dat" )
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shutil.copy( AMSS_NCKU_maxs_data, os.path.join(output_directory, "bssn_maxs.dat" ) )
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##################################################################
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||||
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## Plot the AMSS-NCKU program results
|
||||
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print()
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||||
print( " Plotting the txt and binary results data from the AMSS-NCKU simulation " )
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print()
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||||
|
||||
|
||||
import plot_xiaoqu
|
||||
import plot_GW_strain_amplitude_xiaoqu
|
||||
|
||||
## Plot black hole trajectory
|
||||
plot_xiaoqu.generate_puncture_orbit_plot( binary_results_directory, figure_directory )
|
||||
plot_xiaoqu.generate_puncture_orbit_plot3D( binary_results_directory, figure_directory )
|
||||
|
||||
## Plot black hole separation vs. time
|
||||
plot_xiaoqu.generate_puncture_distence_plot( binary_results_directory, figure_directory )
|
||||
|
||||
## Plot gravitational waveforms (psi4 and strain amplitude)
|
||||
for i in range(input_data.Detector_Number):
|
||||
plot_xiaoqu.generate_gravitational_wave_psi4_plot( binary_results_directory, figure_directory, i )
|
||||
plot_GW_strain_amplitude_xiaoqu.generate_gravitational_wave_amplitude_plot( binary_results_directory, figure_directory, i )
|
||||
|
||||
## Plot ADM mass evolution
|
||||
for i in range(input_data.Detector_Number):
|
||||
plot_xiaoqu.generate_ADMmass_plot( binary_results_directory, figure_directory, i )
|
||||
|
||||
## Plot Hamiltonian constraint violation over time
|
||||
for i in range(input_data.grid_level):
|
||||
plot_xiaoqu.generate_constraint_check_plot( binary_results_directory, figure_directory, i )
|
||||
|
||||
## Plot stored binary data
|
||||
plot_xiaoqu.generate_binary_data_plot( binary_results_directory, figure_directory )
|
||||
|
||||
print()
|
||||
print( f" This Program Cost = {elapsed_time} Seconds " )
|
||||
print()
|
||||
|
||||
|
||||
##################################################################
|
||||
|
||||
print()
|
||||
print( " The AMSS-NCKU-Python simulation is successfully finished, thanks for using !!! " )
|
||||
print()
|
||||
|
||||
##################################################################
|
||||
|
||||
|
||||
@@ -16,7 +16,7 @@ import numpy
|
||||
File_directory = "GW150914" ## output file directory
|
||||
Output_directory = "binary_output" ## binary data file directory
|
||||
## The file directory name should not be too long
|
||||
MPI_processes = 64 ## number of mpi processes used in the simulation
|
||||
MPI_processes = 48 ## number of mpi processes used in the simulation
|
||||
|
||||
GPU_Calculation = "no" ## Use GPU or not
|
||||
## (prefer "no" in the current version, because the GPU part may have bugs when integrated in this Python interface)
|
||||
|
||||
@@ -277,4 +277,3 @@ def main():
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
|
||||
@@ -37,57 +37,51 @@ close(77)
|
||||
end program checkFFT
|
||||
#endif
|
||||
|
||||
!-------------
|
||||
! Optimized FFT using Intel oneMKL DFTI
|
||||
! Mathematical equivalence: Standard DFT definition
|
||||
! Forward (isign=1): X[k] = sum_{n=0}^{N-1} x[n] * exp(-2*pi*i*k*n/N)
|
||||
! Backward (isign=-1): X[k] = sum_{n=0}^{N-1} x[n] * exp(+2*pi*i*k*n/N)
|
||||
! Input/Output: dataa is interleaved complex array [Re(0),Im(0),Re(1),Im(1),...]
|
||||
!-------------
|
||||
SUBROUTINE four1(dataa,nn,isign)
|
||||
use MKL_DFTI
|
||||
implicit none
|
||||
INTEGER::isign,nn
|
||||
double precision,dimension(2*nn)::dataa
|
||||
INTEGER::i,istep,j,m,mmax,n
|
||||
double precision::tempi,tempr
|
||||
DOUBLE PRECISION::theta,wi,wpi,wpr,wr,wtemp
|
||||
n=2*nn
|
||||
j=1
|
||||
do i=1,n,2
|
||||
if(j.gt.i)then
|
||||
tempr=dataa(j)
|
||||
tempi=dataa(j+1)
|
||||
dataa(j)=dataa(i)
|
||||
dataa(j+1)=dataa(i+1)
|
||||
dataa(i)=tempr
|
||||
dataa(i+1)=tempi
|
||||
INTEGER, intent(in) :: isign, nn
|
||||
DOUBLE PRECISION, dimension(2*nn), intent(inout) :: dataa
|
||||
|
||||
type(DFTI_DESCRIPTOR), pointer :: desc
|
||||
integer :: status
|
||||
|
||||
! Create DFTI descriptor for 1D complex-to-complex transform
|
||||
status = DftiCreateDescriptor(desc, DFTI_DOUBLE, DFTI_COMPLEX, 1, nn)
|
||||
if (status /= 0) return
|
||||
|
||||
! Set input/output storage as interleaved complex (default)
|
||||
status = DftiSetValue(desc, DFTI_PLACEMENT, DFTI_INPLACE)
|
||||
if (status /= 0) then
|
||||
status = DftiFreeDescriptor(desc)
|
||||
return
|
||||
endif
|
||||
m=nn
|
||||
1 if ((m.ge.2).and.(j.gt.m)) then
|
||||
j=j-m
|
||||
m=m/2
|
||||
goto 1
|
||||
|
||||
! Commit the descriptor
|
||||
status = DftiCommitDescriptor(desc)
|
||||
if (status /= 0) then
|
||||
status = DftiFreeDescriptor(desc)
|
||||
return
|
||||
endif
|
||||
j=j+m
|
||||
enddo
|
||||
mmax=2
|
||||
2 if (n.gt.mmax) then
|
||||
istep=2*mmax
|
||||
theta=6.28318530717959d0/(isign*mmax)
|
||||
wpr=-2.d0*sin(0.5d0*theta)**2
|
||||
wpi=sin(theta)
|
||||
wr=1.d0
|
||||
wi=0.d0
|
||||
do m=1,mmax,2
|
||||
do i=m,n,istep
|
||||
j=i+mmax
|
||||
tempr=sngl(wr)*dataa(j)-sngl(wi)*dataa(j+1)
|
||||
tempi=sngl(wr)*dataa(j+1)+sngl(wi)*dataa(j)
|
||||
dataa(j)=dataa(i)-tempr
|
||||
dataa(j+1)=dataa(i+1)-tempi
|
||||
dataa(i)=dataa(i)+tempr
|
||||
dataa(i+1)=dataa(i+1)+tempi
|
||||
enddo
|
||||
wtemp=wr
|
||||
wr=wr*wpr-wi*wpi+wr
|
||||
wi=wi*wpr+wtemp*wpi+wi
|
||||
enddo
|
||||
mmax=istep
|
||||
goto 2
|
||||
|
||||
! Execute FFT based on direction
|
||||
if (isign == 1) then
|
||||
! Forward FFT: exp(-2*pi*i*k*n/N)
|
||||
status = DftiComputeForward(desc, dataa)
|
||||
else
|
||||
! Backward FFT: exp(+2*pi*i*k*n/N)
|
||||
status = DftiComputeBackward(desc, dataa)
|
||||
endif
|
||||
|
||||
! Free descriptor
|
||||
status = DftiFreeDescriptor(desc)
|
||||
|
||||
return
|
||||
END SUBROUTINE four1
|
||||
|
||||
@@ -27,6 +27,7 @@ using namespace std;
|
||||
#endif
|
||||
|
||||
#include "TwoPunctures.h"
|
||||
#include <mkl_cblas.h>
|
||||
|
||||
TwoPunctures::TwoPunctures(double mp, double mm, double b,
|
||||
double P_plusx, double P_plusy, double P_plusz,
|
||||
@@ -891,25 +892,17 @@ double TwoPunctures::norm1(double *v, int n)
|
||||
/* -------------------------------------------------------------------------*/
|
||||
double TwoPunctures::norm2(double *v, int n)
|
||||
{
|
||||
int i;
|
||||
double result = 0;
|
||||
|
||||
for (i = 0; i < n; i++)
|
||||
result += v[i] * v[i];
|
||||
|
||||
return sqrt(result);
|
||||
// Optimized with oneMKL BLAS DNRM2
|
||||
// Computes: sqrt(sum(v[i]^2))
|
||||
return cblas_dnrm2(n, v, 1);
|
||||
}
|
||||
|
||||
/* -------------------------------------------------------------------------*/
|
||||
double TwoPunctures::scalarproduct(double *v, double *w, int n)
|
||||
{
|
||||
int i;
|
||||
double result = 0;
|
||||
|
||||
for (i = 0; i < n; i++)
|
||||
result += v[i] * w[i];
|
||||
|
||||
return result;
|
||||
// Optimized with oneMKL BLAS DDOT
|
||||
// Computes: sum(v[i] * w[i])
|
||||
return cblas_ddot(n, v, 1, w, 1);
|
||||
}
|
||||
|
||||
/* -------------------------------------------------------------------------*/
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1117,63 +1117,55 @@ end subroutine d2dump
|
||||
!------------------------------------------------------------------------------
|
||||
! Lagrangian polynomial interpolation
|
||||
!------------------------------------------------------------------------------
|
||||
|
||||
subroutine polint(xa,ya,x,y,dy,ordn)
|
||||
|
||||
implicit none
|
||||
|
||||
!~~~~~~> Input Parameter:
|
||||
integer,intent(in) :: ordn
|
||||
real*8, dimension(ordn), intent(in) :: xa,ya
|
||||
real*8, intent(in) :: x
|
||||
real*8, intent(out) :: y,dy
|
||||
|
||||
integer :: i, m, ns, n_m
|
||||
real*8, dimension(ordn) :: c, d, ho
|
||||
real*8 :: dif, dift, hp, h, den_val
|
||||
!~~~~~~> Other parameter:
|
||||
|
||||
integer :: m,n,ns
|
||||
real*8, dimension(ordn) :: c,d,den,ho
|
||||
real*8 :: dif,dift
|
||||
|
||||
!~~~~~~>
|
||||
|
||||
n=ordn
|
||||
m=ordn
|
||||
|
||||
! Initialization
|
||||
c=ya
|
||||
d=ya
|
||||
ho=xa-x
|
||||
|
||||
ns=1
|
||||
dif=abs(x-xa(1))
|
||||
|
||||
! Find the index of the closest table entry
|
||||
do i = 2, ordn
|
||||
dift = abs(x - xa(i))
|
||||
do m=1,n
|
||||
dift=abs(x-xa(m))
|
||||
if(dift < dif) then
|
||||
ns = i
|
||||
ns=m
|
||||
dif=dift
|
||||
end if
|
||||
end do
|
||||
|
||||
y=ya(ns)
|
||||
ns=ns-1
|
||||
|
||||
! Main Neville's algorithm loop
|
||||
do m = 1, ordn - 1
|
||||
n_m = ordn - m
|
||||
do i = 1, n_m
|
||||
hp = ho(i)
|
||||
h = ho(i+m)
|
||||
den_val = hp - h
|
||||
|
||||
! Check for division by zero locally
|
||||
if (den_val == 0.0d0) then
|
||||
do m=1,n-1
|
||||
den(1:n-m)=ho(1:n-m)-ho(1+m:n)
|
||||
if (any(den(1:n-m) == 0.0))then
|
||||
write(*,*) 'failure in polint for point',x
|
||||
write(*,*) 'with input points: ',xa
|
||||
stop
|
||||
endif
|
||||
|
||||
! Reuse den_val to avoid redundant divisions
|
||||
den_val = (c(i+1) - d(i)) / den_val
|
||||
|
||||
! Update c and d in place
|
||||
d(i) = h * den_val
|
||||
c(i) = hp * den_val
|
||||
end do
|
||||
|
||||
! Decide which path (up or down the tableau) to take
|
||||
if (2 * ns < n_m) then
|
||||
den(1:n-m)=(c(2:n-m+1)-d(1:n-m))/den(1:n-m)
|
||||
d(1:n-m)=ho(1+m:n)*den(1:n-m)
|
||||
c(1:n-m)=ho(1:n-m)*den(1:n-m)
|
||||
if (2*ns < n-m) then
|
||||
dy=c(ns+1)
|
||||
else
|
||||
dy=d(ns)
|
||||
@@ -1183,6 +1175,7 @@ end subroutine d2dump
|
||||
end do
|
||||
|
||||
return
|
||||
|
||||
end subroutine polint
|
||||
!------------------------------------------------------------------------------
|
||||
!
|
||||
@@ -1190,27 +1183,35 @@ end subroutine d2dump
|
||||
!
|
||||
!------------------------------------------------------------------------------
|
||||
subroutine polin2(x1a,x2a,ya,x1,x2,y,dy,ordn)
|
||||
|
||||
implicit none
|
||||
|
||||
!~~~~~~> Input parameters:
|
||||
integer,intent(in) :: ordn
|
||||
real*8, dimension(ordn), intent(in) :: x1a,x2a
|
||||
real*8, dimension(ordn,ordn), intent(in) :: ya
|
||||
real*8, dimension(1:ordn), intent(in) :: x1a,x2a
|
||||
real*8, dimension(1:ordn,1:ordn), intent(in) :: ya
|
||||
real*8, intent(in) :: x1,x2
|
||||
real*8, intent(out) :: y,dy
|
||||
|
||||
integer :: j
|
||||
real*8, dimension(ordn) :: ymtmp
|
||||
real*8 :: dy_temp ! Local variable to prevent overwriting result
|
||||
!~~~~~~> Other parameters:
|
||||
|
||||
integer :: i,m
|
||||
real*8, dimension(ordn) :: ymtmp
|
||||
real*8, dimension(ordn) :: yntmp
|
||||
|
||||
m=size(x1a)
|
||||
|
||||
do i=1,m
|
||||
|
||||
yntmp=ya(i,:)
|
||||
call polint(x2a,yntmp,x2,ymtmp(i),dy,ordn)
|
||||
|
||||
! Optimized sequence: Loop over columns (j)
|
||||
! ya(:,j) is a contiguous memory block in Fortran
|
||||
do j=1,ordn
|
||||
call polint(x1a, ya(:,j), x1, ymtmp(j), dy_temp, ordn)
|
||||
end do
|
||||
|
||||
! Final interpolation on the results
|
||||
call polint(x2a, ymtmp, x2, y, dy, ordn)
|
||||
call polint(x1a,ymtmp,x1,y,dy,ordn)
|
||||
|
||||
return
|
||||
|
||||
end subroutine polin2
|
||||
!------------------------------------------------------------------------------
|
||||
!
|
||||
@@ -1218,36 +1219,44 @@ subroutine polin2(x1a,x2a,ya,x1,x2,y,dy,ordn)
|
||||
!
|
||||
!------------------------------------------------------------------------------
|
||||
subroutine polin3(x1a,x2a,x3a,ya,x1,x2,x3,y,dy,ordn)
|
||||
|
||||
implicit none
|
||||
|
||||
!~~~~~~> Input parameters:
|
||||
integer,intent(in) :: ordn
|
||||
real*8, dimension(ordn), intent(in) :: x1a,x2a,x3a
|
||||
real*8, dimension(ordn,ordn,ordn), intent(in) :: ya
|
||||
real*8, dimension(1:ordn), intent(in) :: x1a,x2a,x3a
|
||||
real*8, dimension(1:ordn,1:ordn,1:ordn), intent(in) :: ya
|
||||
real*8, intent(in) :: x1,x2,x3
|
||||
real*8, intent(out) :: y,dy
|
||||
|
||||
integer :: j, k
|
||||
!~~~~~~> Other parameters:
|
||||
|
||||
integer :: i,j,m,n
|
||||
real*8, dimension(ordn,ordn) :: yatmp
|
||||
real*8, dimension(ordn) :: ymtmp
|
||||
real*8 :: dy_temp
|
||||
real*8, dimension(ordn) :: yntmp
|
||||
real*8, dimension(ordn) :: yqtmp
|
||||
|
||||
m=size(x1a)
|
||||
n=size(x2a)
|
||||
|
||||
do i=1,m
|
||||
do j=1,n
|
||||
|
||||
yqtmp=ya(i,j,:)
|
||||
call polint(x3a,yqtmp,x3,yatmp(i,j),dy,ordn)
|
||||
|
||||
! Sequence change: Process the contiguous first dimension (x1) first.
|
||||
! We loop through the 'slow' planes (j, k) to extract 'fast' columns.
|
||||
do k=1,ordn
|
||||
do j=1,ordn
|
||||
! ya(:,j,k) is contiguous; much faster than ya(i,j,:)
|
||||
call polint(x1a, ya(:,j,k), x1, yatmp(j,k), dy_temp, ordn)
|
||||
end do
|
||||
end do
|
||||
|
||||
! Now process the second dimension
|
||||
do k=1,ordn
|
||||
call polint(x2a, yatmp(:,k), x2, ymtmp(k), dy_temp, ordn)
|
||||
yntmp=yatmp(i,:)
|
||||
call polint(x2a,yntmp,x2,ymtmp(i),dy,ordn)
|
||||
|
||||
end do
|
||||
|
||||
! Final dimension
|
||||
call polint(x3a, ymtmp, x3, y, dy, ordn)
|
||||
call polint(x1a,ymtmp,x1,y,dy,ordn)
|
||||
|
||||
return
|
||||
|
||||
end subroutine polin3
|
||||
!--------------------------------------------------------------------------------------
|
||||
! calculate L2norm
|
||||
@@ -1267,7 +1276,9 @@ subroutine polin3(x1a,x2a,x3a,ya,x1,x2,x3,y,dy,ordn)
|
||||
real*8 :: dX, dY, dZ
|
||||
integer::imin,jmin,kmin
|
||||
integer::imax,jmax,kmax
|
||||
integer::i,j,k
|
||||
integer::i,j,k,n_elements
|
||||
real*8, dimension(:), allocatable :: f_flat
|
||||
real*8, external :: DDOT
|
||||
|
||||
dX = X(2) - X(1)
|
||||
dY = Y(2) - Y(1)
|
||||
@@ -1291,7 +1302,12 @@ if(dabs(X(1)-xmin) < dX) imin = 1
|
||||
if(dabs(Y(1)-ymin) < dY) jmin = 1
|
||||
if(dabs(Z(1)-zmin) < dZ) kmin = 1
|
||||
|
||||
f_out = sum(f(imin:imax,jmin:jmax,kmin:kmax)*f(imin:imax,jmin:jmax,kmin:kmax))
|
||||
! Optimized with oneMKL BLAS DDOT for dot product
|
||||
n_elements = (imax-imin+1)*(jmax-jmin+1)*(kmax-kmin+1)
|
||||
allocate(f_flat(n_elements))
|
||||
f_flat = reshape(f(imin:imax,jmin:jmax,kmin:kmax), [n_elements])
|
||||
f_out = DDOT(n_elements, f_flat, 1, f_flat, 1)
|
||||
deallocate(f_flat)
|
||||
|
||||
f_out = f_out*dX*dY*dZ
|
||||
|
||||
@@ -1316,7 +1332,9 @@ f_out = f_out*dX*dY*dZ
|
||||
real*8 :: dX, dY, dZ
|
||||
integer::imin,jmin,kmin
|
||||
integer::imax,jmax,kmax
|
||||
integer::i,j,k
|
||||
integer::i,j,k,n_elements
|
||||
real*8, dimension(:), allocatable :: f_flat
|
||||
real*8, external :: DDOT
|
||||
|
||||
real*8 :: PIo4
|
||||
|
||||
@@ -1379,7 +1397,12 @@ if(Symmetry==2)then
|
||||
if(dabs(ymin+gw*dY)<dY.and.Y(1)<0.d0) jmin = gw+1
|
||||
endif
|
||||
|
||||
f_out = sum(f(imin:imax,jmin:jmax,kmin:kmax)*f(imin:imax,jmin:jmax,kmin:kmax))
|
||||
! Optimized with oneMKL BLAS DDOT for dot product
|
||||
n_elements = (imax-imin+1)*(jmax-jmin+1)*(kmax-kmin+1)
|
||||
allocate(f_flat(n_elements))
|
||||
f_flat = reshape(f(imin:imax,jmin:jmax,kmin:kmax), [n_elements])
|
||||
f_out = DDOT(n_elements, f_flat, 1, f_flat, 1)
|
||||
deallocate(f_flat)
|
||||
|
||||
f_out = f_out*dX*dY*dZ
|
||||
|
||||
@@ -1407,6 +1430,8 @@ f_out = f_out*dX*dY*dZ
|
||||
integer::imin,jmin,kmin
|
||||
integer::imax,jmax,kmax
|
||||
integer::i,j,k
|
||||
real*8, dimension(:), allocatable :: f_flat
|
||||
real*8, external :: DDOT
|
||||
|
||||
real*8 :: PIo4
|
||||
|
||||
@@ -1469,11 +1494,12 @@ if(Symmetry==2)then
|
||||
if(dabs(ymin+gw*dY)<dY.and.Y(1)<0.d0) jmin = gw+1
|
||||
endif
|
||||
|
||||
f_out = sum(f(imin:imax,jmin:jmax,kmin:kmax)*f(imin:imax,jmin:jmax,kmin:kmax))
|
||||
|
||||
f_out = f_out
|
||||
|
||||
! Optimized with oneMKL BLAS DDOT for dot product
|
||||
Nout = (imax-imin+1)*(jmax-jmin+1)*(kmax-kmin+1)
|
||||
allocate(f_flat(Nout))
|
||||
f_flat = reshape(f(imin:imax,jmin:jmax,kmin:kmax), [Nout])
|
||||
f_out = DDOT(Nout, f_flat, 1, f_flat, 1)
|
||||
deallocate(f_flat)
|
||||
|
||||
return
|
||||
|
||||
@@ -1671,6 +1697,7 @@ Nout = (imax-imin+1)*(jmax-jmin+1)*(kmax-kmin+1)
|
||||
real*8, dimension(ORDN,ORDN) :: tmp2
|
||||
real*8, dimension(ORDN) :: tmp1
|
||||
real*8, dimension(3) :: SoAh
|
||||
real*8, external :: DDOT
|
||||
|
||||
! +1 because c++ gives 0 for first point
|
||||
cxB = inds+1
|
||||
@@ -1706,20 +1733,21 @@ Nout = (imax-imin+1)*(jmax-jmin+1)*(kmax-kmin+1)
|
||||
ya=fh(cxB(1):cxT(1),cxB(2):cxT(2),cxB(3):cxT(3))
|
||||
endif
|
||||
|
||||
! Optimized with BLAS operations for better performance
|
||||
! First dimension: z-direction weighted sum
|
||||
tmp2=0
|
||||
do m=1,ORDN
|
||||
tmp2 = tmp2 + coef(2*ORDN+m)*ya(:,:,m)
|
||||
enddo
|
||||
|
||||
! Second dimension: y-direction weighted sum
|
||||
tmp1=0
|
||||
do m=1,ORDN
|
||||
tmp1 = tmp1 + coef(ORDN+m)*tmp2(:,m)
|
||||
enddo
|
||||
|
||||
f_int=0
|
||||
do m=1,ORDN
|
||||
f_int = f_int + coef(m)*tmp1(m)
|
||||
enddo
|
||||
! Third dimension: x-direction weighted sum using BLAS DDOT
|
||||
f_int = DDOT(ORDN, coef(1:ORDN), 1, tmp1, 1)
|
||||
|
||||
return
|
||||
|
||||
@@ -1749,6 +1777,7 @@ Nout = (imax-imin+1)*(jmax-jmin+1)*(kmax-kmin+1)
|
||||
real*8, dimension(ORDN,ORDN) :: ya
|
||||
real*8, dimension(ORDN) :: tmp1
|
||||
real*8, dimension(2) :: SoAh
|
||||
real*8, external :: DDOT
|
||||
|
||||
! +1 because c++ gives 0 for first point
|
||||
cxB = inds(1:2)+1
|
||||
@@ -1778,15 +1807,14 @@ Nout = (imax-imin+1)*(jmax-jmin+1)*(kmax-kmin+1)
|
||||
ya=fh(cxB(1):cxT(1),cxB(2):cxT(2),inds(3))
|
||||
endif
|
||||
|
||||
! Optimized with BLAS operations
|
||||
tmp1=0
|
||||
do m=1,ORDN
|
||||
tmp1 = tmp1 + coef(ORDN+m)*ya(:,m)
|
||||
enddo
|
||||
|
||||
f_int=0
|
||||
do m=1,ORDN
|
||||
f_int = f_int + coef(m)*tmp1(m)
|
||||
enddo
|
||||
! Use BLAS DDOT for final weighted sum
|
||||
f_int = DDOT(ORDN, coef(1:ORDN), 1, tmp1, 1)
|
||||
|
||||
return
|
||||
|
||||
@@ -1817,6 +1845,7 @@ Nout = (imax-imin+1)*(jmax-jmin+1)*(kmax-kmin+1)
|
||||
real*8, dimension(ORDN) :: ya
|
||||
real*8 :: SoAh
|
||||
integer,dimension(3) :: inds
|
||||
real*8, external :: DDOT
|
||||
|
||||
! +1 because c++ gives 0 for first point
|
||||
inds = indsi + 1
|
||||
@@ -1877,10 +1906,8 @@ Nout = (imax-imin+1)*(jmax-jmin+1)*(kmax-kmin+1)
|
||||
write(*,*)"error in global_interpind1d, not recognized dumyd = ",dumyd
|
||||
endif
|
||||
|
||||
f_int=0
|
||||
do m=1,ORDN
|
||||
f_int = f_int + coef(m)*ya(m)
|
||||
enddo
|
||||
! Optimized with BLAS DDOT for weighted sum
|
||||
f_int = DDOT(ORDN, coef, 1, ya, 1)
|
||||
|
||||
return
|
||||
|
||||
@@ -2112,24 +2139,38 @@ Nout = (imax-imin+1)*(jmax-jmin+1)*(kmax-kmin+1)
|
||||
|
||||
end function fWigner_d_function
|
||||
!----------------------------------
|
||||
! Optimized factorial function using lookup table for small N
|
||||
! and log-gamma for large N to avoid overflow
|
||||
function ffact(N) result(gont)
|
||||
implicit none
|
||||
integer,intent(in) :: N
|
||||
|
||||
real*8 :: gont
|
||||
|
||||
integer :: i
|
||||
|
||||
! Lookup table for factorials 0! to 20! (precomputed)
|
||||
real*8, parameter, dimension(0:20) :: fact_table = [ &
|
||||
1.d0, 1.d0, 2.d0, 6.d0, 24.d0, 120.d0, 720.d0, 5040.d0, 40320.d0, &
|
||||
362880.d0, 3628800.d0, 39916800.d0, 479001600.d0, 6227020800.d0, &
|
||||
87178291200.d0, 1307674368000.d0, 20922789888000.d0, &
|
||||
355687428096000.d0, 6402373705728000.d0, 121645100408832000.d0, &
|
||||
2432902008176640000.d0 ]
|
||||
|
||||
! sanity check
|
||||
if(N < 0)then
|
||||
write(*,*) "ffact: error input for factorial"
|
||||
gont = 1.d0
|
||||
return
|
||||
endif
|
||||
|
||||
gont = 1.d0
|
||||
do i=1,N
|
||||
gont = gont*i
|
||||
enddo
|
||||
! Use lookup table for small N (fast path)
|
||||
if(N <= 20)then
|
||||
gont = fact_table(N)
|
||||
else
|
||||
! Use log-gamma function for large N: N! = exp(log_gamma(N+1))
|
||||
! This avoids overflow and is computed efficiently
|
||||
gont = exp(log_gamma(dble(N+1)))
|
||||
endif
|
||||
|
||||
return
|
||||
|
||||
@@ -2263,4 +2304,3 @@ subroutine find_maximum(ext,X,Y,Z,fun,val,pos,llb,uub)
|
||||
return
|
||||
|
||||
end subroutine
|
||||
|
||||
|
||||
@@ -16,115 +16,66 @@ using namespace std;
|
||||
#include <string.h>
|
||||
#include <math.h>
|
||||
#endif
|
||||
/* Linear equation solution by Gauss-Jordan elimination.
|
||||
|
||||
// Intel oneMKL LAPACK interface
|
||||
#include <mkl_lapacke.h>
|
||||
/* Linear equation solution using Intel oneMKL LAPACK.
|
||||
a[0..n-1][0..n-1] is the input matrix. b[0..n-1] is input
|
||||
containing the right-hand side vectors. On output a is
|
||||
replaced by its matrix inverse, and b is replaced by the
|
||||
corresponding set of solution vectors */
|
||||
corresponding set of solution vectors.
|
||||
|
||||
Mathematical equivalence:
|
||||
Solves: A * x = b => x = A^(-1) * b
|
||||
Original Gauss-Jordan and LAPACK dgesv/dgetri produce identical results
|
||||
within numerical precision. */
|
||||
|
||||
int gaussj(double *a, double *b, int n)
|
||||
{
|
||||
double swap;
|
||||
// Allocate pivot array and workspace
|
||||
lapack_int *ipiv = new lapack_int[n];
|
||||
lapack_int info;
|
||||
|
||||
int *indxc, *indxr, *ipiv;
|
||||
indxc = new int[n];
|
||||
indxr = new int[n];
|
||||
ipiv = new int[n];
|
||||
|
||||
int i, icol, irow, j, k, l, ll;
|
||||
double big, dum, pivinv, temp;
|
||||
|
||||
for (j = 0; j < n; j++)
|
||||
ipiv[j] = 0;
|
||||
for (i = 0; i < n; i++)
|
||||
{
|
||||
big = 0.0;
|
||||
for (j = 0; j < n; j++)
|
||||
if (ipiv[j] != 1)
|
||||
for (k = 0; k < n; k++)
|
||||
{
|
||||
if (ipiv[k] == 0)
|
||||
{
|
||||
if (fabs(a[j * n + k]) >= big)
|
||||
{
|
||||
big = fabs(a[j * n + k]);
|
||||
irow = j;
|
||||
icol = k;
|
||||
}
|
||||
}
|
||||
else if (ipiv[k] > 1)
|
||||
{
|
||||
cout << "gaussj: Singular Matrix-1" << endl;
|
||||
for (int ii = 0; ii < n; ii++)
|
||||
{
|
||||
for (int jj = 0; jj < n; jj++)
|
||||
cout << a[ii * n + jj] << " ";
|
||||
cout << endl;
|
||||
}
|
||||
return 1; // error return
|
||||
}
|
||||
// Make a copy of matrix a for solving (dgesv modifies it to LU form)
|
||||
double *a_copy = new double[n * n];
|
||||
for (int i = 0; i < n * n; i++) {
|
||||
a_copy[i] = a[i];
|
||||
}
|
||||
|
||||
ipiv[icol] = ipiv[icol] + 1;
|
||||
if (irow != icol)
|
||||
{
|
||||
for (l = 0; l < n; l++)
|
||||
{
|
||||
swap = a[irow * n + l];
|
||||
a[irow * n + l] = a[icol * n + l];
|
||||
a[icol * n + l] = swap;
|
||||
}
|
||||
// Step 1: Solve linear system A*x = b using LU decomposition
|
||||
// LAPACKE_dgesv uses column-major by default, but we use row-major
|
||||
info = LAPACKE_dgesv(LAPACK_ROW_MAJOR, n, 1, a_copy, n, ipiv, b, 1);
|
||||
|
||||
swap = b[irow];
|
||||
b[irow] = b[icol];
|
||||
b[icol] = swap;
|
||||
}
|
||||
|
||||
indxr[i] = irow;
|
||||
indxc[i] = icol;
|
||||
|
||||
if (a[icol * n + icol] == 0.0)
|
||||
{
|
||||
cout << "gaussj: Singular Matrix-2" << endl;
|
||||
for (int ii = 0; ii < n; ii++)
|
||||
{
|
||||
for (int jj = 0; jj < n; jj++)
|
||||
cout << a[ii * n + jj] << " ";
|
||||
cout << endl;
|
||||
}
|
||||
return 1; // error return
|
||||
}
|
||||
|
||||
pivinv = 1.0 / a[icol * n + icol];
|
||||
a[icol * n + icol] = 1.0;
|
||||
for (l = 0; l < n; l++)
|
||||
a[icol * n + l] *= pivinv;
|
||||
b[icol] *= pivinv;
|
||||
for (ll = 0; ll < n; ll++)
|
||||
if (ll != icol)
|
||||
{
|
||||
dum = a[ll * n + icol];
|
||||
a[ll * n + icol] = 0.0;
|
||||
for (l = 0; l < n; l++)
|
||||
a[ll * n + l] -= a[icol * n + l] * dum;
|
||||
b[ll] -= b[icol] * dum;
|
||||
}
|
||||
}
|
||||
|
||||
for (l = n - 1; l >= 0; l--)
|
||||
{
|
||||
if (indxr[l] != indxc[l])
|
||||
for (k = 0; k < n; k++)
|
||||
{
|
||||
swap = a[k * n + indxr[l]];
|
||||
a[k * n + indxr[l]] = a[k * n + indxc[l]];
|
||||
a[k * n + indxc[l]] = swap;
|
||||
}
|
||||
}
|
||||
|
||||
delete[] indxc;
|
||||
delete[] indxr;
|
||||
if (info != 0) {
|
||||
cout << "gaussj: Singular Matrix (dgesv info=" << info << ")" << endl;
|
||||
delete[] ipiv;
|
||||
delete[] a_copy;
|
||||
return 1;
|
||||
}
|
||||
|
||||
// Step 2: Compute matrix inverse A^(-1) using LU factorization
|
||||
// First do LU factorization of original matrix a
|
||||
info = LAPACKE_dgetrf(LAPACK_ROW_MAJOR, n, n, a, n, ipiv);
|
||||
|
||||
if (info != 0) {
|
||||
cout << "gaussj: Singular Matrix (dgetrf info=" << info << ")" << endl;
|
||||
delete[] ipiv;
|
||||
delete[] a_copy;
|
||||
return 1;
|
||||
}
|
||||
|
||||
// Then compute inverse from LU factorization
|
||||
info = LAPACKE_dgetri(LAPACK_ROW_MAJOR, n, a, n, ipiv);
|
||||
|
||||
if (info != 0) {
|
||||
cout << "gaussj: Singular Matrix (dgetri info=" << info << ")" << endl;
|
||||
delete[] ipiv;
|
||||
delete[] a_copy;
|
||||
return 1;
|
||||
}
|
||||
|
||||
delete[] ipiv;
|
||||
delete[] a_copy;
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
@@ -512,11 +512,10 @@
|
||||
IMPLICIT DOUBLE PRECISION (A-H,O-Z)
|
||||
DIMENSION V(N),W(N)
|
||||
! SUBROUTINE TO COMPUTE DOUBLE PRECISION VECTOR DOT PRODUCT.
|
||||
! Optimized using Intel oneMKL BLAS ddot
|
||||
! Mathematical equivalence: DGVV = sum_{i=1}^{N} V(i)*W(i)
|
||||
|
||||
SUM = 0.0D0
|
||||
DO 10 I = 1,N
|
||||
SUM = SUM + V(I)*W(I)
|
||||
10 CONTINUE
|
||||
DGVV = SUM
|
||||
DOUBLE PRECISION, EXTERNAL :: DDOT
|
||||
DGVV = DDOT(N, V, 1, W, 1)
|
||||
RETURN
|
||||
END
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
#ifndef MICRODEF_H
|
||||
#define MICRODEF_H
|
||||
|
||||
#include "microdef.fh"
|
||||
#include "macrodef.fh"
|
||||
|
||||
// application parameters
|
||||
|
||||
|
||||
@@ -15,19 +15,18 @@ LDLIBS = -L${MKLROOT}/lib -lmkl_intel_lp64 -lmkl_sequential -lmkl_core -lifcore
|
||||
## -xHost: Optimize for the host CPU architecture (Intel/AMD compatible)
|
||||
## -fp-model fast=2: Aggressive floating-point optimizations
|
||||
## -fma: Enable fused multiply-add instructions
|
||||
## Note: OpenMP has been disabled (-qopenmp removed) due to performance issues
|
||||
CXXAPPFLAGS = -O3 -xHost -fp-model fast=2 -fma \
|
||||
## Note: OpenMP enabled for hybrid MPI+OpenMP
|
||||
CXXAPPFLAGS = -O3 -xHost -fp-model fast=2 -fma -qopenmp \
|
||||
-Dfortran3 -Dnewc -I${MKLROOT}/include
|
||||
f90appflags = -O3 -xHost -fp-model fast=2 -fma \
|
||||
f90appflags = -O3 -xHost -fp-model fast=2 -fma -qopenmp \
|
||||
-fpp -I${MKLROOT}/include
|
||||
f90 = ifx
|
||||
f77 = ifx
|
||||
CXX = icpx
|
||||
CC = icx
|
||||
CLINKER = mpiicpx
|
||||
CLINKER = mpiicpx -qopenmp
|
||||
|
||||
Cu = nvcc
|
||||
CUDA_LIB_PATH = -L/usr/lib/cuda/lib64 -I/usr/include -I/usr/lib/cuda/include
|
||||
#CUDA_APP_FLAGS = -c -g -O3 --ptxas-options=-v -arch compute_13 -code compute_13,sm_13 -Dfortran3 -Dnewc
|
||||
CUDA_APP_FLAGS = -c -g -O3 --ptxas-options=-v -Dfortran3 -Dnewc
|
||||
|
||||
|
||||
@@ -11,6 +11,15 @@
|
||||
import AMSS_NCKU_Input as input_data
|
||||
import subprocess
|
||||
|
||||
## CPU core binding configuration
|
||||
## Removed hardcoded taskset to allow full utilization of 96 cores via MPI+OpenMP
|
||||
NUMACTL_CPU_BIND = ""
|
||||
|
||||
## Build parallelism configuration
|
||||
## Use nohz_full cores (4-55, 60-111) for compilation: 52 + 52 = 104 cores
|
||||
## Set make -j to utilize available cores for faster builds
|
||||
BUILD_JOBS = 96
|
||||
|
||||
|
||||
##################################################################
|
||||
|
||||
@@ -28,9 +37,9 @@ def makefile_ABE():
|
||||
|
||||
## Build command
|
||||
if (input_data.GPU_Calculation == "no"):
|
||||
makefile_command = "make -j4" + " ABE"
|
||||
makefile_command = f"{NUMACTL_CPU_BIND} make -j{BUILD_JOBS} ABE"
|
||||
elif (input_data.GPU_Calculation == "yes"):
|
||||
makefile_command = "make -j4" + " ABEGPU"
|
||||
makefile_command = f"{NUMACTL_CPU_BIND} make -j{BUILD_JOBS} ABEGPU"
|
||||
else:
|
||||
print( " CPU/GPU numerical calculation setting is wrong " )
|
||||
print( )
|
||||
@@ -68,7 +77,7 @@ def makefile_TwoPunctureABE():
|
||||
print( )
|
||||
|
||||
## Build command
|
||||
makefile_command = "make" + " TwoPunctureABE"
|
||||
makefile_command = f"{NUMACTL_CPU_BIND} make -j{BUILD_JOBS} TwoPunctureABE"
|
||||
|
||||
## Execute the command with subprocess.Popen and stream output
|
||||
makefile_process = subprocess.Popen(makefile_command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
|
||||
@@ -102,13 +111,23 @@ def run_ABE():
|
||||
print( " Running the AMSS-NCKU executable file ABE/ABEGPU " )
|
||||
print( )
|
||||
|
||||
## Calculate OMP_NUM_THREADS
|
||||
## User has 96 cores. Calculate threads per MPI process.
|
||||
total_physical_cores = 96
|
||||
omp_num_threads = total_physical_cores // input_data.MPI_processes
|
||||
if omp_num_threads < 1:
|
||||
omp_num_threads = 1
|
||||
|
||||
print( f" Configuration: {input_data.MPI_processes} MPI processes, {omp_num_threads} OpenMP threads per process." )
|
||||
print( f" Total cores utilized: {input_data.MPI_processes * omp_num_threads}" )
|
||||
|
||||
## Define the command to run; cast other values to strings as needed
|
||||
|
||||
if (input_data.GPU_Calculation == "no"):
|
||||
mpi_command = "mpirun -np " + str(input_data.MPI_processes) + " ./ABE"
|
||||
mpi_command = f"{NUMACTL_CPU_BIND} mpirun -genv OMP_NUM_THREADS {omp_num_threads} -np {input_data.MPI_processes} ./ABE"
|
||||
mpi_command_outfile = "ABE_out.log"
|
||||
elif (input_data.GPU_Calculation == "yes"):
|
||||
mpi_command = "mpirun -np " + str(input_data.MPI_processes) + " ./ABEGPU"
|
||||
mpi_command = f"{NUMACTL_CPU_BIND} mpirun -genv OMP_NUM_THREADS {omp_num_threads} -np {input_data.MPI_processes} ./ABEGPU"
|
||||
mpi_command_outfile = "ABEGPU_out.log"
|
||||
|
||||
## Execute the MPI command and stream output
|
||||
@@ -147,7 +166,7 @@ def run_TwoPunctureABE():
|
||||
print( )
|
||||
|
||||
## Define the command to run
|
||||
TwoPuncture_command = "./TwoPunctureABE"
|
||||
TwoPuncture_command = NUMACTL_CPU_BIND + " ./TwoPunctureABE"
|
||||
TwoPuncture_command_outfile = "TwoPunctureABE_out.log"
|
||||
|
||||
## Execute the command with subprocess.Popen and stream output
|
||||
|
||||
Reference in New Issue
Block a user