删除diff_new.f90中冗余部分，方便后续工作

.gitignore

@@ -1,6 +1,3 @@
 __pycache__
 GW150914
-GW150914-origin
+GW150914-origin
-docs
-*.tmp
-

AMSS_NCKU_ABEtest.py

@@ -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()
+
+##################################################################
+
+

AMSS_NCKU_Input.py

@@ -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)

AMSS_NCKU_Verify_ASC26.py

@@ -277,3 +277,4 @@ def main():
 
 if __name__ == "__main__":
     main()
+

AMSS_NCKU_source/FFT.f90

@@ -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
+INTEGER::isign,nn
-DOUBLE PRECISION, dimension(2*nn), intent(inout) :: dataa
+double precision,dimension(2*nn)::dataa
-
+INTEGER::i,istep,j,m,mmax,n
-type(DFTI_DESCRIPTOR), pointer :: desc
+double precision::tempi,tempr
-integer :: status
+DOUBLE PRECISION::theta,wi,wpi,wpr,wr,wtemp
-
+n=2*nn
-! Create DFTI descriptor for 1D complex-to-complex transform
+j=1
-status = DftiCreateDescriptor(desc, DFTI_DOUBLE, DFTI_COMPLEX, 1, nn)
+do i=1,n,2
-if (status /= 0) return
+  if(j.gt.i)then
-
+     tempr=dataa(j)
-! Set input/output storage as interleaved complex (default)
+     tempi=dataa(j+1)
-status = DftiSetValue(desc, DFTI_PLACEMENT, DFTI_INPLACE)
+     dataa(j)=dataa(i)
-if (status /= 0) then
+     dataa(j+1)=dataa(i+1)
-   status = DftiFreeDescriptor(desc)
+     dataa(i)=tempr
-   return
+     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

AMSS_NCKU_source/TwoPunctures.C

@@ -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
+  int i;
-  // Computes: sqrt(sum(v[i]^2))
+  double result = 0;
-  return cblas_dnrm2(n, v, 1);
+
+  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
+  int i;
-  // Computes: sum(v[i] * w[i])
+  double result = 0;
-  return cblas_ddot(n, v, 1, w, 1);
+
+  for (i = 0; i < n; i++)
+    result += v[i] * w[i];
+
+  return result;
 }
 
 /* -------------------------------------------------------------------------*/

AMSS_NCKU_source/fmisc.f90

@@ -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
-  integer,intent(in) :: ordn
+  real*8, dimension(ordn), intent(in) :: xa, ya
-  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
+  ! Initialization
-  real*8, dimension(ordn) :: c,d,den,ho
+  c = ya
-  real*8 :: dif,dift
+  d = ya
-
+  ho = xa - x
-!~~~~~~>
+  
-
+  ns = 1
-  n=ordn
+  dif = abs(x - xa(1))
-  m=ordn
+  
-
+  ! Find the index of the closest table entry
-  c=ya
+  do i = 2, ordn
-  d=ya
+    dift = abs(x - xa(i))
-  ho=xa-x
+    if (dift < dif) then
-
+      ns = i
-  ns=1
+      dif = dift
-  dif=abs(x-xa(1))
+    end if
-  do m=1,n
-   dift=abs(x-xa(m))
-   if(dift < dif) then
-    ns=m
-    dif=dift
-   end if
   end do
 
-  y=ya(ns)
+  y = ya(ns)
-  ns=ns-1
+  ns = ns - 1
-  do m=1,n-1
+  
-    den(1:n-m)=ho(1:n-m)-ho(1+m:n)
+  ! Main Neville's algorithm loop
-    if (any(den(1:n-m) == 0.0))then
+  do m = 1, ordn - 1
-      write(*,*) 'failure in polint for point',x
+    n_m = ordn - m
-      write(*,*) 'with input points: ',xa
+    do i = 1, n_m
-      stop
+      hp = ho(i)
-    endif
+      h  = ho(i+m)
-    den(1:n-m)=(c(2:n-m+1)-d(1:n-m))/den(1:n-m)
+      den_val = hp - h
-    d(1:n-m)=ho(1+m:n)*den(1:n-m)
+      
-    c(1:n-m)=ho(1:n-m)*den(1:n-m)
+      ! Check for division by zero locally
-    if (2*ns < n-m) then
+      if (den_val == 0.0d0) then
-      dy=c(ns+1)
+        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)
+      dy = d(ns)
-      ns=ns-1
+      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:
+    ! Optimized sequence: Loop over columns (j) 
-  integer,intent(in) :: ordn
+    ! ya(:,j) is a contiguous memory block in Fortran
-  real*8, dimension(1:ordn), intent(in) :: x1a,x2a
+    do j=1,ordn
-  real*8, dimension(1:ordn,1:ordn), intent(in) :: ya
+      call polint(x1a, ya(:,j), x1, ymtmp(j), dy_temp, ordn)
-  real*8, intent(in) :: x1,x2
+    end do
-  real*8, intent(out) :: y,dy
 
-!~~~~~~> Other parameters:
+    ! Final interpolation on the results
-
+    call polint(x2a, ymtmp, x2, y, dy, ordn)
-  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
 
+    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:
+    ! Sequence change: Process the contiguous first dimension (x1) first.
-  integer,intent(in) :: ordn
+    ! We loop through the 'slow' planes (j, k) to extract 'fast' columns.
-  real*8, dimension(1:ordn), intent(in) :: x1a,x2a,x3a
+    do k=1,ordn
-  real*8, dimension(1:ordn,1:ordn,1:ordn), intent(in) :: ya
+      do j=1,ordn
-  real*8, intent(in) :: x1,x2,x3
+        ! ya(:,j,k) is contiguous; much faster than ya(i,j,:)
-  real*8, intent(out) :: y,dy
+        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
+    ! Final dimension
-  real*8, dimension(ordn,ordn) :: yatmp
+    call polint(x3a, ymtmp, x3, y, dy, ordn)
-  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
 
+    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
+  integer::i,j,k
-  real*8, dimension(:), allocatable :: f_flat
-  real*8, external :: DDOT
 
   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
+f_out = sum(f(imin:imax,jmin:jmax,kmin:kmax)*f(imin:imax,jmin:jmax,kmin:kmax))
-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
 
@@ -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
+  integer::i,j,k
-  real*8, dimension(:), allocatable :: f_flat
-  real*8, external :: DDOT
 
   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
+f_out = sum(f(imin:imax,jmin:jmax,kmin:kmax)*f(imin:imax,jmin:jmax,kmin:kmax))
-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
 
@@ -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=0
-  f_int = DDOT(ORDN, coef(1:ORDN), 1, tmp1, 1)
+  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=0
-  f_int = DDOT(ORDN, coef(1:ORDN), 1, tmp1, 1)
+  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=0
-  f_int = DDOT(ORDN, coef, 1, ya, 1)
+  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)
+  integer :: i
-  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
 
-  ! Use lookup table for small N (fast path)
+  gont = 1.d0
-  if(N <= 20)then
+  do i=1,N
-     gont = fact_table(N)
+     gont = gont*i
-  else
+  enddo
-     ! 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
 
@@ -2304,3 +2263,4 @@ subroutine find_maximum(ext,X,Y,Z,fun,val,pos,llb,uub)
   return
 
 end subroutine
+

AMSS_NCKU_source/gaussj.C

@@ -16,66 +16,115 @@ 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)
 {
-  // Allocate pivot array and workspace
+  double swap;
-  lapack_int *ipiv = new lapack_int[n];
-  lapack_int info;
 
-  // Make a copy of matrix a for solving (dgesv modifies it to LU form)
+  int *indxc, *indxr, *ipiv;
-  double *a_copy = new double[n * n];
+  indxc = new int[n];
-  for (int i = 0; i < n * n; i++) {
+  indxr = new int[n];
-    a_copy[i] = a[i];
+  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
+  for (l = n - 1; l >= 0; l--)
-  // 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 (indxr[l] != indxc[l])
-
+      for (k = 0; k < n; k++)
-  if (info != 0) {
+      {
-    cout << "gaussj: Singular Matrix (dgesv info=" << info << ")" << endl;
+        swap = a[k * n + indxr[l]];
-    delete[] ipiv;
+        a[k * n + indxr[l]] = a[k * n + indxc[l]];
-    delete[] a_copy;
+        a[k * n + indxc[l]] = swap;
-    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[] indxc;
+  delete[] indxr;
   delete[] ipiv;
-  delete[] a_copy;
 
   return 0;
 }

AMSS_NCKU_source/ilucg.f90

@@ -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
+      SUM = 0.0D0
-      DGVV = DDOT(N, V, 1, W, 1)
+            DO 10 I = 1,N
+            SUM = SUM + V(I)*W(I)
+10          CONTINUE
+      DGVV = SUM
       RETURN
       END

AMSS_NCKU_source/macrodef.h

@@ -2,7 +2,7 @@
 #ifndef MICRODEF_H
 #define MICRODEF_H
 
-#include "macrodef.fh"
+#include "microdef.fh"
 
 // application parameters
 

AMSS_NCKU_source/makefile

@@ -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\

AMSS_NCKU_source/makefile.inc

@@ -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 \
+## Added -lifcore for Intel Fortran runtime and -limf for Intel math library
-          -L${MKLROOT}/lib -lmkl_intel_lp64 -lmkl_sequential -lmkl_core \
+LDLIBS  = -L${MKLROOT}/lib -lmkl_intel_lp64 -lmkl_sequential -lmkl_core -lifcore -limf -lpthread -lm -ldl
-          -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
+

makefile_and_run.py

@@ -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
+    ## Build command
-    makefile_command = f"{NUMACTL_CPU_BIND} make -j{BUILD_JOBS} TwoPunctureABE"
+    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