Compare commits
2 Commits
cjy-oneapi
...
yx-fmisc
| Author | SHA1 | Date | |
|---|---|---|---|
| 3f7e20f702 | |||
| 673dd20722 |
5
.gitignore
vendored
5
.gitignore
vendored
@@ -1,6 +1,3 @@
|
||||
__pycache__
|
||||
GW150914
|
||||
GW150914-origin
|
||||
docs
|
||||
*.tmp
|
||||
|
||||
GW150914-origin
|
||||
445
AMSS_NCKU_ABEtest.py
Normal file
445
AMSS_NCKU_ABEtest.py
Normal file
@@ -0,0 +1,445 @@
|
||||
|
||||
##################################################################
|
||||
##
|
||||
## AMSS-NCKU ABE Test Program (Skip TwoPuncture if data exists)
|
||||
## Modified from AMSS_NCKU_Program.py
|
||||
## Author: Xiaoqu
|
||||
## Modified: 2026/02/01
|
||||
##
|
||||
##################################################################
|
||||
|
||||
|
||||
##################################################################
|
||||
|
||||
## Print program introduction
|
||||
|
||||
import print_information
|
||||
|
||||
print_information.print_program_introduction()
|
||||
|
||||
##################################################################
|
||||
|
||||
import AMSS_NCKU_Input as input_data
|
||||
|
||||
##################################################################
|
||||
|
||||
## Create directories to store program run data
|
||||
|
||||
import os
|
||||
import shutil
|
||||
import sys
|
||||
import time
|
||||
|
||||
## Set the output directory according to the input file
|
||||
File_directory = os.path.join(input_data.File_directory)
|
||||
|
||||
## Check if output directory exists and if TwoPuncture data is available
|
||||
skip_twopuncture = False
|
||||
output_directory = os.path.join(File_directory, "AMSS_NCKU_output")
|
||||
binary_results_directory = os.path.join(output_directory, input_data.Output_directory)
|
||||
|
||||
if os.path.exists(File_directory):
|
||||
print( " Output directory already exists." )
|
||||
print()
|
||||
|
||||
# Check if TwoPuncture initial data files exist
|
||||
if (input_data.Initial_Data_Method == "Ansorg-TwoPuncture"):
|
||||
twopuncture_output = os.path.join(output_directory, "TwoPunctureABE")
|
||||
input_par = os.path.join(output_directory, "input.par")
|
||||
|
||||
if os.path.exists(twopuncture_output) and os.path.exists(input_par):
|
||||
print( " Found existing TwoPuncture initial data." )
|
||||
print( " Do you want to skip TwoPuncture phase and reuse existing data?" )
|
||||
print( " Input 'skip' to skip TwoPuncture and start ABE directly" )
|
||||
print( " Input 'regenerate' to regenerate everything from scratch" )
|
||||
print()
|
||||
|
||||
while True:
|
||||
try:
|
||||
inputvalue = input()
|
||||
if ( inputvalue == "skip" ):
|
||||
print( " Skipping TwoPuncture phase, will reuse existing initial data." )
|
||||
print()
|
||||
skip_twopuncture = True
|
||||
break
|
||||
elif ( inputvalue == "regenerate" ):
|
||||
print( " Regenerating everything from scratch." )
|
||||
print()
|
||||
skip_twopuncture = False
|
||||
break
|
||||
else:
|
||||
print( " Please input 'skip' or 'regenerate'." )
|
||||
except ValueError:
|
||||
print( " Please input 'skip' or 'regenerate'." )
|
||||
else:
|
||||
print( " TwoPuncture initial data not found, will regenerate everything." )
|
||||
print()
|
||||
|
||||
# If not skipping, remove and recreate directory
|
||||
if not skip_twopuncture:
|
||||
shutil.rmtree(File_directory, ignore_errors=True)
|
||||
os.mkdir(File_directory)
|
||||
os.mkdir(output_directory)
|
||||
os.mkdir(binary_results_directory)
|
||||
figure_directory = os.path.join(File_directory, "figure")
|
||||
os.mkdir(figure_directory)
|
||||
shutil.copy("AMSS_NCKU_Input.py", File_directory)
|
||||
print( " Output directory has been regenerated." )
|
||||
print()
|
||||
else:
|
||||
# Create fresh directory structure
|
||||
os.mkdir(File_directory)
|
||||
shutil.copy("AMSS_NCKU_Input.py", File_directory)
|
||||
os.mkdir(output_directory)
|
||||
os.mkdir(binary_results_directory)
|
||||
figure_directory = os.path.join(File_directory, "figure")
|
||||
os.mkdir(figure_directory)
|
||||
print( " Output directory has been generated." )
|
||||
print()
|
||||
|
||||
# Ensure figure directory exists
|
||||
figure_directory = os.path.join(File_directory, "figure")
|
||||
if not os.path.exists(figure_directory):
|
||||
os.mkdir(figure_directory)
|
||||
|
||||
##################################################################
|
||||
|
||||
## Output related parameter information
|
||||
|
||||
import setup
|
||||
|
||||
## Print and save input parameter information
|
||||
setup.print_input_data( File_directory )
|
||||
|
||||
if not skip_twopuncture:
|
||||
setup.generate_AMSSNCKU_input()
|
||||
|
||||
setup.print_puncture_information()
|
||||
|
||||
|
||||
##################################################################
|
||||
|
||||
## Generate AMSS-NCKU program input files based on the configured parameters
|
||||
|
||||
if not skip_twopuncture:
|
||||
print()
|
||||
print( " Generating the AMSS-NCKU input parfile for the ABE executable." )
|
||||
print()
|
||||
|
||||
## Generate cgh-related input files from the grid information
|
||||
|
||||
import numerical_grid
|
||||
|
||||
numerical_grid.append_AMSSNCKU_cgh_input()
|
||||
|
||||
print()
|
||||
print( " The input parfile for AMSS-NCKU C++ executable file ABE has been generated." )
|
||||
print( " However, the input relevant to TwoPuncture need to be appended later." )
|
||||
print()
|
||||
|
||||
|
||||
##################################################################
|
||||
|
||||
## Plot the initial grid configuration
|
||||
|
||||
if not skip_twopuncture:
|
||||
print()
|
||||
print( " Schematically plot the numerical grid structure." )
|
||||
print()
|
||||
|
||||
import numerical_grid
|
||||
numerical_grid.plot_initial_grid()
|
||||
|
||||
|
||||
##################################################################
|
||||
|
||||
## Generate AMSS-NCKU macro files according to the numerical scheme and parameters
|
||||
|
||||
if not skip_twopuncture:
|
||||
print()
|
||||
print( " Automatically generating the macro file for AMSS-NCKU C++ executable file ABE " )
|
||||
print( " (Based on the finite-difference numerical scheme) " )
|
||||
print()
|
||||
|
||||
import generate_macrodef
|
||||
|
||||
generate_macrodef.generate_macrodef_h()
|
||||
print( " AMSS-NCKU macro file macrodef.h has been generated. " )
|
||||
|
||||
generate_macrodef.generate_macrodef_fh()
|
||||
print( " AMSS-NCKU macro file macrodef.fh has been generated. " )
|
||||
|
||||
|
||||
##################################################################
|
||||
|
||||
# Compile the AMSS-NCKU program according to user requirements
|
||||
# NOTE: ABE compilation is always performed, even when skipping TwoPuncture
|
||||
|
||||
print()
|
||||
print( " Preparing to compile and run the AMSS-NCKU code as requested " )
|
||||
print( " Compiling the AMSS-NCKU code based on the generated macro files " )
|
||||
print()
|
||||
|
||||
AMSS_NCKU_source_path = "AMSS_NCKU_source"
|
||||
AMSS_NCKU_source_copy = os.path.join(File_directory, "AMSS_NCKU_source_copy")
|
||||
|
||||
## If AMSS_NCKU source folder is missing, create it and prompt the user
|
||||
if not os.path.exists(AMSS_NCKU_source_path):
|
||||
os.makedirs(AMSS_NCKU_source_path)
|
||||
print( " The AMSS-NCKU source files are incomplete; copy all source files into ./AMSS_NCKU_source. " )
|
||||
print( " Press Enter to continue. " )
|
||||
inputvalue = input()
|
||||
|
||||
# Copy AMSS-NCKU source files to prepare for compilation
|
||||
# If skipping TwoPuncture and source_copy already exists, remove it first
|
||||
if skip_twopuncture and os.path.exists(AMSS_NCKU_source_copy):
|
||||
shutil.rmtree(AMSS_NCKU_source_copy)
|
||||
|
||||
shutil.copytree(AMSS_NCKU_source_path, AMSS_NCKU_source_copy)
|
||||
|
||||
# Copy the generated macro files into the AMSS_NCKU source folder
|
||||
if not skip_twopuncture:
|
||||
macrodef_h_path = os.path.join(File_directory, "macrodef.h")
|
||||
macrodef_fh_path = os.path.join(File_directory, "macrodef.fh")
|
||||
else:
|
||||
# When skipping TwoPuncture, use existing macro files from previous run
|
||||
macrodef_h_path = os.path.join(File_directory, "macrodef.h")
|
||||
macrodef_fh_path = os.path.join(File_directory, "macrodef.fh")
|
||||
|
||||
shutil.copy2(macrodef_h_path, AMSS_NCKU_source_copy)
|
||||
shutil.copy2(macrodef_fh_path, AMSS_NCKU_source_copy)
|
||||
|
||||
# Compile related programs
|
||||
import makefile_and_run
|
||||
|
||||
## Change working directory to the target source copy
|
||||
os.chdir(AMSS_NCKU_source_copy)
|
||||
|
||||
## Build the main AMSS-NCKU executable (ABE or ABEGPU)
|
||||
makefile_and_run.makefile_ABE()
|
||||
|
||||
## If the initial-data method is Ansorg-TwoPuncture, build the TwoPunctureABE executable
|
||||
## Only build TwoPunctureABE if not skipping TwoPuncture phase
|
||||
if (input_data.Initial_Data_Method == "Ansorg-TwoPuncture" ) and not skip_twopuncture:
|
||||
makefile_and_run.makefile_TwoPunctureABE()
|
||||
|
||||
## Change current working directory back up two levels
|
||||
os.chdir('..')
|
||||
os.chdir('..')
|
||||
|
||||
print()
|
||||
|
||||
##################################################################
|
||||
|
||||
## Copy the AMSS-NCKU executable (ABE/ABEGPU) to the run directory
|
||||
|
||||
if (input_data.GPU_Calculation == "no"):
|
||||
ABE_file = os.path.join(AMSS_NCKU_source_copy, "ABE")
|
||||
elif (input_data.GPU_Calculation == "yes"):
|
||||
ABE_file = os.path.join(AMSS_NCKU_source_copy, "ABEGPU")
|
||||
|
||||
if not os.path.exists( ABE_file ):
|
||||
print()
|
||||
print( " Lack of AMSS-NCKU executable file ABE/ABEGPU; recompile AMSS_NCKU_source manually. " )
|
||||
print( " When recompilation is finished, press Enter to continue. " )
|
||||
inputvalue = input()
|
||||
|
||||
## Copy the executable ABE (or ABEGPU) into the run directory
|
||||
shutil.copy2(ABE_file, output_directory)
|
||||
|
||||
## If the initial-data method is TwoPuncture, copy the TwoPunctureABE executable to the run directory
|
||||
## Only copy TwoPunctureABE if not skipping TwoPuncture phase
|
||||
if (input_data.Initial_Data_Method == "Ansorg-TwoPuncture" ) and not skip_twopuncture:
|
||||
TwoPuncture_file = os.path.join(AMSS_NCKU_source_copy, "TwoPunctureABE")
|
||||
|
||||
if not os.path.exists( TwoPuncture_file ):
|
||||
print()
|
||||
print( " Lack of AMSS-NCKU executable file TwoPunctureABE; recompile TwoPunctureABE in AMSS_NCKU_source. " )
|
||||
print( " When recompilation is finished, press Enter to continue. " )
|
||||
inputvalue = input()
|
||||
|
||||
## Copy the TwoPunctureABE executable into the run directory
|
||||
shutil.copy2(TwoPuncture_file, output_directory)
|
||||
|
||||
##################################################################
|
||||
|
||||
## If the initial-data method is TwoPuncture, generate the TwoPuncture input files
|
||||
|
||||
if (input_data.Initial_Data_Method == "Ansorg-TwoPuncture" ) and not skip_twopuncture:
|
||||
|
||||
print()
|
||||
print( " Initial data is chosen as Ansorg-TwoPuncture" )
|
||||
print()
|
||||
|
||||
print()
|
||||
print( " Automatically generating the input parfile for the TwoPunctureABE executable " )
|
||||
print()
|
||||
|
||||
import generate_TwoPuncture_input
|
||||
|
||||
generate_TwoPuncture_input.generate_AMSSNCKU_TwoPuncture_input()
|
||||
|
||||
print()
|
||||
print( " The input parfile for the TwoPunctureABE executable has been generated. " )
|
||||
print()
|
||||
|
||||
## Generated AMSS-NCKU TwoPuncture input filename
|
||||
AMSS_NCKU_TwoPuncture_inputfile = 'AMSS-NCKU-TwoPuncture.input'
|
||||
AMSS_NCKU_TwoPuncture_inputfile_path = os.path.join( File_directory, AMSS_NCKU_TwoPuncture_inputfile )
|
||||
|
||||
## Copy and rename the file
|
||||
shutil.copy2( AMSS_NCKU_TwoPuncture_inputfile_path, os.path.join(output_directory, 'TwoPunctureinput.par') )
|
||||
|
||||
## Run TwoPuncture to generate initial-data files
|
||||
|
||||
start_time = time.time() # Record start time
|
||||
|
||||
print()
|
||||
print()
|
||||
|
||||
## Change to the output (run) directory
|
||||
os.chdir(output_directory)
|
||||
|
||||
## Run the TwoPuncture executable
|
||||
import makefile_and_run
|
||||
makefile_and_run.run_TwoPunctureABE()
|
||||
|
||||
## Change current working directory back up two levels
|
||||
os.chdir('..')
|
||||
os.chdir('..')
|
||||
|
||||
elif (input_data.Initial_Data_Method == "Ansorg-TwoPuncture" ) and skip_twopuncture:
|
||||
print()
|
||||
print( " Skipping TwoPuncture execution, using existing initial data." )
|
||||
print()
|
||||
start_time = time.time() # Record start time for ABE only
|
||||
else:
|
||||
start_time = time.time() # Record start time
|
||||
|
||||
##################################################################
|
||||
|
||||
## Update puncture data based on TwoPuncture run results
|
||||
|
||||
if not skip_twopuncture:
|
||||
import renew_puncture_parameter
|
||||
renew_puncture_parameter.append_AMSSNCKU_BSSN_input(File_directory, output_directory)
|
||||
|
||||
## Generated AMSS-NCKU input filename
|
||||
AMSS_NCKU_inputfile = 'AMSS-NCKU.input'
|
||||
AMSS_NCKU_inputfile_path = os.path.join(File_directory, AMSS_NCKU_inputfile)
|
||||
|
||||
## Copy and rename the file
|
||||
shutil.copy2( AMSS_NCKU_inputfile_path, os.path.join(output_directory, 'input.par') )
|
||||
|
||||
print()
|
||||
print( " Successfully copy all AMSS-NCKU input parfile to target dictionary. " )
|
||||
print()
|
||||
else:
|
||||
print()
|
||||
print( " Using existing input.par file from previous run." )
|
||||
print()
|
||||
|
||||
##################################################################
|
||||
|
||||
## Launch the AMSS-NCKU program
|
||||
|
||||
print()
|
||||
print()
|
||||
|
||||
## Change to the run directory
|
||||
os.chdir( output_directory )
|
||||
|
||||
import makefile_and_run
|
||||
makefile_and_run.run_ABE()
|
||||
|
||||
## Change current working directory back up two levels
|
||||
os.chdir('..')
|
||||
os.chdir('..')
|
||||
|
||||
end_time = time.time()
|
||||
elapsed_time = end_time - start_time
|
||||
|
||||
##################################################################
|
||||
|
||||
## Copy some basic input and log files out to facilitate debugging
|
||||
|
||||
## Path to the file that stores calculation settings
|
||||
AMSS_NCKU_error_file_path = os.path.join(binary_results_directory, "setting.par")
|
||||
## Copy and rename the file for easier inspection
|
||||
shutil.copy( AMSS_NCKU_error_file_path, os.path.join(output_directory, "AMSSNCKU_setting_parameter") )
|
||||
|
||||
## Path to the error log file
|
||||
AMSS_NCKU_error_file_path = os.path.join(binary_results_directory, "Error.log")
|
||||
## Copy and rename the error log
|
||||
shutil.copy( AMSS_NCKU_error_file_path, os.path.join(output_directory, "Error.log") )
|
||||
|
||||
## Primary program outputs
|
||||
AMSS_NCKU_BH_data = os.path.join(binary_results_directory, "bssn_BH.dat" )
|
||||
AMSS_NCKU_ADM_data = os.path.join(binary_results_directory, "bssn_ADMQs.dat" )
|
||||
AMSS_NCKU_psi4_data = os.path.join(binary_results_directory, "bssn_psi4.dat" )
|
||||
AMSS_NCKU_constraint_data = os.path.join(binary_results_directory, "bssn_constraint.dat")
|
||||
## copy and rename the file
|
||||
shutil.copy( AMSS_NCKU_BH_data, os.path.join(output_directory, "bssn_BH.dat" ) )
|
||||
shutil.copy( AMSS_NCKU_ADM_data, os.path.join(output_directory, "bssn_ADMQs.dat" ) )
|
||||
shutil.copy( AMSS_NCKU_psi4_data, os.path.join(output_directory, "bssn_psi4.dat" ) )
|
||||
shutil.copy( AMSS_NCKU_constraint_data, os.path.join(output_directory, "bssn_constraint.dat") )
|
||||
|
||||
## Additional program outputs
|
||||
if (input_data.Equation_Class == "BSSN-EM"):
|
||||
AMSS_NCKU_phi1_data = os.path.join(binary_results_directory, "bssn_phi1.dat" )
|
||||
AMSS_NCKU_phi2_data = os.path.join(binary_results_directory, "bssn_phi2.dat" )
|
||||
shutil.copy( AMSS_NCKU_phi1_data, os.path.join(output_directory, "bssn_phi1.dat" ) )
|
||||
shutil.copy( AMSS_NCKU_phi2_data, os.path.join(output_directory, "bssn_phi2.dat" ) )
|
||||
elif (input_data.Equation_Class == "BSSN-EScalar"):
|
||||
AMSS_NCKU_maxs_data = os.path.join(binary_results_directory, "bssn_maxs.dat" )
|
||||
shutil.copy( AMSS_NCKU_maxs_data, os.path.join(output_directory, "bssn_maxs.dat" ) )
|
||||
|
||||
##################################################################
|
||||
|
||||
## Plot the AMSS-NCKU program results
|
||||
|
||||
print()
|
||||
print( " Plotting the txt and binary results data from the AMSS-NCKU simulation " )
|
||||
print()
|
||||
|
||||
|
||||
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 = 48 ## number of mpi processes used in the simulation
|
||||
MPI_processes = 64 ## 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)
|
||||
|
||||
@@ -277,3 +277,4 @@ def main():
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
|
||||
@@ -37,51 +37,57 @@ 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, 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
|
||||
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
|
||||
endif
|
||||
m=nn
|
||||
1 if ((m.ge.2).and.(j.gt.m)) then
|
||||
j=j-m
|
||||
m=m/2
|
||||
goto 1
|
||||
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
|
||||
endif
|
||||
|
||||
! Commit the descriptor
|
||||
status = DftiCommitDescriptor(desc)
|
||||
if (status /= 0) then
|
||||
status = DftiFreeDescriptor(desc)
|
||||
return
|
||||
endif
|
||||
|
||||
! 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
|
||||
|
||||
@@ -27,7 +27,6 @@ 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,
|
||||
@@ -892,17 +891,25 @@ double TwoPunctures::norm1(double *v, int n)
|
||||
/* -------------------------------------------------------------------------*/
|
||||
double TwoPunctures::norm2(double *v, int n)
|
||||
{
|
||||
// Optimized with oneMKL BLAS DNRM2
|
||||
// Computes: sqrt(sum(v[i]^2))
|
||||
return cblas_dnrm2(n, v, 1);
|
||||
int i;
|
||||
double result = 0;
|
||||
|
||||
for (i = 0; i < n; i++)
|
||||
result += v[i] * v[i];
|
||||
|
||||
return sqrt(result);
|
||||
}
|
||||
|
||||
/* -------------------------------------------------------------------------*/
|
||||
double TwoPunctures::scalarproduct(double *v, double *w, int n)
|
||||
{
|
||||
// Optimized with oneMKL BLAS DDOT
|
||||
// Computes: sum(v[i] * w[i])
|
||||
return cblas_ddot(n, v, 1, w, 1);
|
||||
int i;
|
||||
double result = 0;
|
||||
|
||||
for (i = 0; i < n; i++)
|
||||
result += v[i] * w[i];
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/* -------------------------------------------------------------------------*/
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -1117,149 +1117,140 @@ end subroutine d2dump
|
||||
!------------------------------------------------------------------------------
|
||||
! Lagrangian polynomial interpolation
|
||||
!------------------------------------------------------------------------------
|
||||
|
||||
subroutine polint(xa,ya,x,y,dy,ordn)
|
||||
|
||||
subroutine polint(xa, ya, x, y, dy, ordn)
|
||||
implicit none
|
||||
|
||||
!~~~~~~> Input Parameter:
|
||||
integer,intent(in) :: ordn
|
||||
real*8, dimension(ordn), intent(in) :: xa,ya
|
||||
integer, intent(in) :: ordn
|
||||
real*8, dimension(ordn), intent(in) :: xa, ya
|
||||
real*8, intent(in) :: x
|
||||
real*8, intent(out) :: y,dy
|
||||
real*8, intent(out) :: y, dy
|
||||
|
||||
!~~~~~~> Other parameter:
|
||||
integer :: i, m, ns, n_m
|
||||
real*8, dimension(ordn) :: c, d, ho
|
||||
real*8 :: dif, dift, hp, h, den_val
|
||||
|
||||
integer :: m,n,ns
|
||||
real*8, dimension(ordn) :: c,d,den,ho
|
||||
real*8 :: dif,dift
|
||||
|
||||
!~~~~~~>
|
||||
|
||||
n=ordn
|
||||
m=ordn
|
||||
|
||||
c=ya
|
||||
d=ya
|
||||
ho=xa-x
|
||||
|
||||
ns=1
|
||||
dif=abs(x-xa(1))
|
||||
do m=1,n
|
||||
dift=abs(x-xa(m))
|
||||
if(dift < dif) then
|
||||
ns=m
|
||||
dif=dift
|
||||
end if
|
||||
! 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))
|
||||
if (dift < dif) then
|
||||
ns = i
|
||||
dif = dift
|
||||
end if
|
||||
end do
|
||||
|
||||
y=ya(ns)
|
||||
ns=ns-1
|
||||
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
|
||||
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)
|
||||
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
|
||||
write(*,*) 'failure in polint for point',x
|
||||
write(*,*) 'with input points: ',xa
|
||||
stop
|
||||
end if
|
||||
|
||||
! 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
|
||||
dy = c(ns + 1)
|
||||
else
|
||||
dy=d(ns)
|
||||
ns=ns-1
|
||||
dy = d(ns)
|
||||
ns = ns - 1
|
||||
end if
|
||||
y=y+dy
|
||||
y = y + dy
|
||||
end do
|
||||
|
||||
return
|
||||
|
||||
end subroutine polint
|
||||
!------------------------------------------------------------------------------
|
||||
!
|
||||
! interpolation in 2 dimensions, follow yx order
|
||||
!
|
||||
!------------------------------------------------------------------------------
|
||||
subroutine polin2(x1a,x2a,ya,x1,x2,y,dy,ordn)
|
||||
subroutine polin2(x1a,x2a,ya,x1,x2,y,dy,ordn)
|
||||
implicit none
|
||||
integer,intent(in) :: ordn
|
||||
real*8, dimension(ordn), intent(in) :: x1a,x2a
|
||||
real*8, dimension(ordn,ordn), intent(in) :: ya
|
||||
real*8, intent(in) :: x1,x2
|
||||
real*8, intent(out) :: y,dy
|
||||
|
||||
implicit none
|
||||
integer :: j
|
||||
real*8, dimension(ordn) :: ymtmp
|
||||
real*8 :: dy_temp ! Local variable to prevent overwriting result
|
||||
|
||||
!~~~~~~> Input parameters:
|
||||
integer,intent(in) :: ordn
|
||||
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
|
||||
! 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
|
||||
|
||||
!~~~~~~> 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)
|
||||
|
||||
end do
|
||||
|
||||
call polint(x1a,ymtmp,x1,y,dy,ordn)
|
||||
|
||||
return
|
||||
! Final interpolation on the results
|
||||
call polint(x2a, ymtmp, x2, y, dy, ordn)
|
||||
|
||||
return
|
||||
end subroutine polin2
|
||||
!------------------------------------------------------------------------------
|
||||
!
|
||||
! interpolation in 3 dimensions, follow zyx order
|
||||
!
|
||||
!------------------------------------------------------------------------------
|
||||
subroutine polin3(x1a,x2a,x3a,ya,x1,x2,x3,y,dy,ordn)
|
||||
subroutine polin3(x1a,x2a,x3a,ya,x1,x2,x3,y,dy,ordn)
|
||||
implicit none
|
||||
integer,intent(in) :: ordn
|
||||
real*8, dimension(ordn), intent(in) :: x1a,x2a,x3a
|
||||
real*8, dimension(ordn,ordn,ordn), intent(in) :: ya
|
||||
real*8, intent(in) :: x1,x2,x3
|
||||
real*8, intent(out) :: y,dy
|
||||
|
||||
implicit none
|
||||
integer :: j, k
|
||||
real*8, dimension(ordn,ordn) :: yatmp
|
||||
real*8, dimension(ordn) :: ymtmp
|
||||
real*8 :: dy_temp
|
||||
|
||||
!~~~~~~> Input parameters:
|
||||
integer,intent(in) :: ordn
|
||||
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
|
||||
! 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
|
||||
|
||||
!~~~~~~> Other parameters:
|
||||
! Now process the second dimension
|
||||
do k=1,ordn
|
||||
call polint(x2a, yatmp(:,k), x2, ymtmp(k), dy_temp, ordn)
|
||||
end do
|
||||
|
||||
integer :: i,j,m,n
|
||||
real*8, dimension(ordn,ordn) :: yatmp
|
||||
real*8, dimension(ordn) :: ymtmp
|
||||
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)
|
||||
|
||||
end do
|
||||
|
||||
yntmp=yatmp(i,:)
|
||||
call polint(x2a,yntmp,x2,ymtmp(i),dy,ordn)
|
||||
|
||||
end do
|
||||
|
||||
call polint(x1a,ymtmp,x1,y,dy,ordn)
|
||||
|
||||
return
|
||||
! Final dimension
|
||||
call polint(x3a, ymtmp, x3, y, dy, ordn)
|
||||
|
||||
return
|
||||
end subroutine polin3
|
||||
!--------------------------------------------------------------------------------------
|
||||
! calculate L2norm
|
||||
! calculate L2norm
|
||||
subroutine l2normhelper(ex, X, Y, Z,xmin,ymin,zmin,xmax,ymax,zmax,&
|
||||
f,f_out,gw)
|
||||
|
||||
@@ -1276,9 +1267,7 @@ end subroutine d2dump
|
||||
real*8 :: dX, dY, dZ
|
||||
integer::imin,jmin,kmin
|
||||
integer::imax,jmax,kmax
|
||||
integer::i,j,k,n_elements
|
||||
real*8, dimension(:), allocatable :: f_flat
|
||||
real*8, external :: DDOT
|
||||
integer::i,j,k
|
||||
|
||||
dX = X(2) - X(1)
|
||||
dY = Y(2) - Y(1)
|
||||
@@ -1302,12 +1291,7 @@ if(dabs(X(1)-xmin) < dX) imin = 1
|
||||
if(dabs(Y(1)-ymin) < dY) jmin = 1
|
||||
if(dabs(Z(1)-zmin) < dZ) kmin = 1
|
||||
|
||||
! 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 = sum(f(imin:imax,jmin:jmax,kmin:kmax)*f(imin:imax,jmin:jmax,kmin:kmax))
|
||||
|
||||
f_out = f_out*dX*dY*dZ
|
||||
|
||||
@@ -1332,9 +1316,7 @@ f_out = f_out*dX*dY*dZ
|
||||
real*8 :: dX, dY, dZ
|
||||
integer::imin,jmin,kmin
|
||||
integer::imax,jmax,kmax
|
||||
integer::i,j,k,n_elements
|
||||
real*8, dimension(:), allocatable :: f_flat
|
||||
real*8, external :: DDOT
|
||||
integer::i,j,k
|
||||
|
||||
real*8 :: PIo4
|
||||
|
||||
@@ -1397,12 +1379,7 @@ if(Symmetry==2)then
|
||||
if(dabs(ymin+gw*dY)<dY.and.Y(1)<0.d0) jmin = gw+1
|
||||
endif
|
||||
|
||||
! 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 = sum(f(imin:imax,jmin:jmax,kmin:kmax)*f(imin:imax,jmin:jmax,kmin:kmax))
|
||||
|
||||
f_out = f_out*dX*dY*dZ
|
||||
|
||||
@@ -1430,8 +1407,6 @@ 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
|
||||
|
||||
@@ -1494,12 +1469,11 @@ if(Symmetry==2)then
|
||||
if(dabs(ymin+gw*dY)<dY.and.Y(1)<0.d0) jmin = gw+1
|
||||
endif
|
||||
|
||||
! Optimized with oneMKL BLAS DDOT for dot product
|
||||
f_out = sum(f(imin:imax,jmin:jmax,kmin:kmax)*f(imin:imax,jmin:jmax,kmin:kmax))
|
||||
|
||||
f_out = f_out
|
||||
|
||||
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
|
||||
|
||||
@@ -1697,7 +1671,6 @@ deallocate(f_flat)
|
||||
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
|
||||
@@ -1733,21 +1706,20 @@ deallocate(f_flat)
|
||||
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
|
||||
|
||||
! Third dimension: x-direction weighted sum using BLAS DDOT
|
||||
f_int = DDOT(ORDN, coef(1:ORDN), 1, tmp1, 1)
|
||||
f_int=0
|
||||
do m=1,ORDN
|
||||
f_int = f_int + coef(m)*tmp1(m)
|
||||
enddo
|
||||
|
||||
return
|
||||
|
||||
@@ -1777,7 +1749,6 @@ deallocate(f_flat)
|
||||
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
|
||||
@@ -1807,14 +1778,15 @@ deallocate(f_flat)
|
||||
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
|
||||
|
||||
! Use BLAS DDOT for final weighted sum
|
||||
f_int = DDOT(ORDN, coef(1:ORDN), 1, tmp1, 1)
|
||||
f_int=0
|
||||
do m=1,ORDN
|
||||
f_int = f_int + coef(m)*tmp1(m)
|
||||
enddo
|
||||
|
||||
return
|
||||
|
||||
@@ -1845,7 +1817,6 @@ deallocate(f_flat)
|
||||
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
|
||||
@@ -1906,8 +1877,10 @@ deallocate(f_flat)
|
||||
write(*,*)"error in global_interpind1d, not recognized dumyd = ",dumyd
|
||||
endif
|
||||
|
||||
! Optimized with BLAS DDOT for weighted sum
|
||||
f_int = DDOT(ORDN, coef, 1, ya, 1)
|
||||
f_int=0
|
||||
do m=1,ORDN
|
||||
f_int = f_int + coef(m)*ya(m)
|
||||
enddo
|
||||
|
||||
return
|
||||
|
||||
@@ -2139,38 +2112,24 @@ deallocate(f_flat)
|
||||
|
||||
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 ]
|
||||
integer :: i
|
||||
|
||||
! sanity check
|
||||
if(N < 0)then
|
||||
write(*,*) "ffact: error input for factorial"
|
||||
gont = 1.d0
|
||||
return
|
||||
endif
|
||||
|
||||
! 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
|
||||
gont = 1.d0
|
||||
do i=1,N
|
||||
gont = gont*i
|
||||
enddo
|
||||
|
||||
return
|
||||
|
||||
@@ -2304,3 +2263,4 @@ subroutine find_maximum(ext,X,Y,Z,fun,val,pos,llb,uub)
|
||||
return
|
||||
|
||||
end subroutine
|
||||
|
||||
|
||||
@@ -16,66 +16,115 @@ using namespace std;
|
||||
#include <string.h>
|
||||
#include <math.h>
|
||||
#endif
|
||||
|
||||
// Intel oneMKL LAPACK interface
|
||||
#include <mkl_lapacke.h>
|
||||
/* Linear equation solution using Intel oneMKL LAPACK.
|
||||
/* Linear equation solution by Gauss-Jordan elimination.
|
||||
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.
|
||||
|
||||
Mathematical equivalence:
|
||||
Solves: A * x = b => x = A^(-1) * b
|
||||
Original Gauss-Jordan and LAPACK dgesv/dgetri produce identical results
|
||||
within numerical precision. */
|
||||
corresponding set of solution vectors */
|
||||
|
||||
int gaussj(double *a, double *b, int n)
|
||||
{
|
||||
// Allocate pivot array and workspace
|
||||
lapack_int *ipiv = new lapack_int[n];
|
||||
lapack_int info;
|
||||
double swap;
|
||||
|
||||
// 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];
|
||||
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
|
||||
}
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
}
|
||||
|
||||
// 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);
|
||||
|
||||
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;
|
||||
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;
|
||||
delete[] ipiv;
|
||||
delete[] a_copy;
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
@@ -512,10 +512,11 @@
|
||||
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)
|
||||
|
||||
DOUBLE PRECISION, EXTERNAL :: DDOT
|
||||
DGVV = DDOT(N, V, 1, W, 1)
|
||||
SUM = 0.0D0
|
||||
DO 10 I = 1,N
|
||||
SUM = SUM + V(I)*W(I)
|
||||
10 CONTINUE
|
||||
DGVV = SUM
|
||||
RETURN
|
||||
END
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
#ifndef MICRODEF_H
|
||||
#define MICRODEF_H
|
||||
|
||||
#include "macrodef.fh"
|
||||
#include "microdef.fh"
|
||||
|
||||
// application parameters
|
||||
|
||||
|
||||
@@ -34,7 +34,7 @@ C++FILES_GPU = ABE.o Ansorg.o Block.o misc.o monitor.o Parallel.o MPatch.o var.o
|
||||
|
||||
F90FILES = enforce_algebra.o fmisc.o initial_puncture.o prolongrestrict.o\
|
||||
prolongrestrict_cell.o prolongrestrict_vertex.o\
|
||||
rungekutta4_rout.o bssn_rhs_opt.o bssn_rhs.o bssn_rhs_legacy.o diff_new.o kodiss.o kodiss_sh.o\
|
||||
rungekutta4_rout.o bssn_rhs.o diff_new.o kodiss.o kodiss_sh.o\
|
||||
lopsidediff.o sommerfeld_rout.o getnp4.o diff_new_sh.o\
|
||||
shellfunctions.o bssn_rhs_ss.o Set_Rho_ADM.o\
|
||||
getnp4EScalar.o bssnEScalar_rhs.o bssn_constraint.o ricci_gamma.o\
|
||||
|
||||
@@ -7,9 +7,8 @@
|
||||
filein = -I/usr/include/ -I${MKLROOT}/include
|
||||
|
||||
## Using sequential MKL (OpenMP disabled for better single-threaded performance)
|
||||
LDLIBS = -L/usr/lib/x86_64-linux-gnu -L/usr/lib64 -lifcore -limf -lmpi \
|
||||
-L${MKLROOT}/lib -lmkl_intel_lp64 -lmkl_sequential -lmkl_core \
|
||||
-lpthread -lm -ldl
|
||||
## Added -lifcore for Intel Fortran runtime and -limf for Intel math library
|
||||
LDLIBS = -L${MKLROOT}/lib -lmkl_intel_lp64 -lmkl_sequential -lmkl_core -lifcore -limf -lpthread -lm -ldl
|
||||
|
||||
## Aggressive optimization flags:
|
||||
## -O3: Maximum optimization
|
||||
@@ -31,3 +30,4 @@ 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
|
||||
|
||||
|
||||
@@ -11,17 +11,6 @@
|
||||
import AMSS_NCKU_Input as input_data
|
||||
import subprocess
|
||||
|
||||
## CPU core binding configuration using taskset
|
||||
## taskset ensures all child processes inherit the CPU affinity mask
|
||||
## This forces make and all compiler processes to use only nohz_full cores (4-55, 60-111)
|
||||
## Format: taskset -c 4-55,60-111 ensures processes only run on these cores
|
||||
NUMACTL_CPU_BIND = "taskset -c 4-55,60-111"
|
||||
|
||||
## 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 = 104
|
||||
|
||||
|
||||
##################################################################
|
||||
|
||||
@@ -37,11 +26,11 @@ def makefile_ABE():
|
||||
print( " Compiling the AMSS-NCKU executable file ABE/ABEGPU " )
|
||||
print( )
|
||||
|
||||
## Build command with CPU binding to nohz_full cores
|
||||
## Build command
|
||||
if (input_data.GPU_Calculation == "no"):
|
||||
makefile_command = f"{NUMACTL_CPU_BIND} make -j{BUILD_JOBS} ABE"
|
||||
makefile_command = "make -j4" + " ABE"
|
||||
elif (input_data.GPU_Calculation == "yes"):
|
||||
makefile_command = f"{NUMACTL_CPU_BIND} make -j{BUILD_JOBS} ABEGPU"
|
||||
makefile_command = "make -j4" + " ABEGPU"
|
||||
else:
|
||||
print( " CPU/GPU numerical calculation setting is wrong " )
|
||||
print( )
|
||||
@@ -78,8 +67,8 @@ def makefile_TwoPunctureABE():
|
||||
print( " Compiling the AMSS-NCKU executable file TwoPunctureABE " )
|
||||
print( )
|
||||
|
||||
## Build command with CPU binding to nohz_full cores
|
||||
makefile_command = f"{NUMACTL_CPU_BIND} make -j{BUILD_JOBS} TwoPunctureABE"
|
||||
## Build command
|
||||
makefile_command = "make" + " 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)
|
||||
@@ -116,10 +105,10 @@ def run_ABE():
|
||||
## Define the command to run; cast other values to strings as needed
|
||||
|
||||
if (input_data.GPU_Calculation == "no"):
|
||||
mpi_command = NUMACTL_CPU_BIND + " mpirun -np " + str(input_data.MPI_processes) + " ./ABE"
|
||||
mpi_command = "mpirun -np " + str(input_data.MPI_processes) + " ./ABE"
|
||||
mpi_command_outfile = "ABE_out.log"
|
||||
elif (input_data.GPU_Calculation == "yes"):
|
||||
mpi_command = NUMACTL_CPU_BIND + " mpirun -np " + str(input_data.MPI_processes) + " ./ABEGPU"
|
||||
mpi_command = "mpirun -np " + str(input_data.MPI_processes) + " ./ABEGPU"
|
||||
mpi_command_outfile = "ABEGPU_out.log"
|
||||
|
||||
## Execute the MPI command and stream output
|
||||
@@ -158,7 +147,7 @@ def run_TwoPunctureABE():
|
||||
print( )
|
||||
|
||||
## Define the command to run
|
||||
TwoPuncture_command = NUMACTL_CPU_BIND + " ./TwoPunctureABE"
|
||||
TwoPuncture_command = "./TwoPunctureABE"
|
||||
TwoPuncture_command_outfile = "TwoPunctureABE_out.log"
|
||||
|
||||
## Execute the command with subprocess.Popen and stream output
|
||||
|
||||
Reference in New Issue
Block a user