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The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
# ARCH = -xCORE-AVX2
Put more comments in Makefile.
2021-02-04 18:52:26 +01:00
## Used compilers
Add test infrastructure for datasets
2021-02-16 10:49:15 +01:00
H5CXX = h5c++
TO BE AMENDED
2021-02-23 08:28:09 +01:00
CXX = icpc
FC = ifort
Put more comments in Makefile.
2021-02-04 18:52:26 +01:00
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
## Compiler flags & common obs & libs
* Fixed the 3x3 MaponiA3 C++ example. Update_index vector cannot have values that are smaller than 1 because the first colums in Fortran is stored at 1 and not 0. * Started debugging reading from HDF5 formatted datasets. Slater_inv needs to be transposed before sent to Maponi. Algo fails at the last step. Correct Slater and Inverse fail to produce the identity matrix. Suspect that the matMul function is not working correctly eventhough it looks like it does.
2021-02-24 18:41:48 +01:00
H5CXXFLAGS = -O0 -g
CXXFLAGS = -O0 -g -traceback
FFLAGS = -O0 -g -traceback
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
FLIBS = -lstdc++
OBJS = SM_MaponiA3.o
Added QMCChem test dataset with 3 updates. Renamed files, added the Fortran program QMCChem_dataset_test.f90 to start testing the QMCChem dataset. Wrote and tested Fortran code to import the test dataset.
2021-02-12 12:04:21 +01:00
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
## Deps & objs for C++ cMaponiA3_test_3x3_3
cMaponiA3_test_3x3_3OBJ = cMaponiA3_test_3x3_3.o
fMaponiA3_test_3x3_3OBJ = SM_MaponiA3_mod.o fMaponiA3_test_3x3_3.o
* Fixed the 3x3 MaponiA3 C++ example. Update_index vector cannot have values that are smaller than 1 because the first colums in Fortran is stored at 1 and not 0. * Started debugging reading from HDF5 formatted datasets. Slater_inv needs to be transposed before sent to Maponi. Algo fails at the last step. Correct Slater and Inverse fail to produce the identity matrix. Suspect that the matMul function is not working correctly eventhough it looks like it does.
2021-02-24 18:41:48 +01:00
fMaponiA3_test_4x4_2OBJ = Helpers_mod.o SM_MaponiA3_mod.o fMaponiA3_test_4x4_2.o
TO BE AMENDED
2021-02-23 08:28:09 +01:00
QMCChem_dataset_testOBJ = Helpers_mod.o SM_MaponiA3_mod.o QMCChem_dataset_test.o
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
## Default build target: build everything
Test infrastructure for hdf5 datasets and python hdf5 datasert conversion tool. Made small corrections to compensate for changes made after branching-off from test_dataset. Everything compilers Everything works except for the HDF5 dataset test program that gives an I/O error.
2021-02-22 10:51:07 +01:00
all: cMaponiA3_test_3x3_3 fMaponiA3_test_3x3_3 fMaponiA3_test_4x4_2 QMCChem_dataset_test tests/test
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
Added QMCChem test dataset with 3 updates. Renamed files, added the Fortran program QMCChem_dataset_test.f90 to start testing the QMCChem dataset. Wrote and tested Fortran code to import the test dataset.
2021-02-12 12:04:21 +01:00
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
## Compile recipes for C++
%.o: %.cpp
Prepared the main Fortran infrastructure for the C++ calls. Updated the makefile.
2021-02-04 11:39:00 +01:00
$(CXX) $(ARCH) $(CXXFLAGS) -c -o $@ $<
Added some initial data likle Makefiles and .gitignore.
2021-01-26 07:26:54 +01:00
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
## Compile recepies for Fortran
%.o: %.f90
Prepared the main Fortran infrastructure for the C++ calls. Updated the makefile.
2021-02-04 11:39:00 +01:00
$(FC) $(ARCH) $(FFLAGS) -c -o $@ $<
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
## Explicit recipe to trigger rebuild and relinking when headerfile is changed
SM_MaponiA3.o: SM_MaponiA3.cpp Helpers.hpp
* Fixed the 3x3 MaponiA3 C++ example. Update_index vector cannot have values that are smaller than 1 because the first colums in Fortran is stored at 1 and not 0. * Started debugging reading from HDF5 formatted datasets. Slater_inv needs to be transposed before sent to Maponi. Algo fails at the last step. Correct Slater and Inverse fail to produce the identity matrix. Suspect that the matMul function is not working correctly eventhough it looks like it does.
2021-02-24 18:41:48 +01:00
$(CXX) $(ARCH) $(CXXFLAGS) -fPIC -c -o $@ $<
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
Put more comments in Makefile.
2021-02-04 18:52:26 +01:00
## Build tagets
Prepared the example matrix of Example 8 of the paper and its decomposition in the Fortran code and made a basic call to the subroutine MYSUBROUTINE, which is bound to the C++ void function 'Sherman-Morrison();. For now compilation fails with lots of undefined references.'
2021-02-04 13:12:34 +01:00
.PHONY: all clean distclean
clean:
@rm -vf *.o *.mod
C++ redesign of data-structures - Use flat arrays - Use real type for all matrices - Merge _f MaponiA3 files
2021-02-09 13:40:52 +01:00
Prepared the example matrix of Example 8 of the paper and its decomposition in the Fortran code and made a basic call to the subroutine MYSUBROUTINE, which is bound to the C++ void function 'Sherman-Morrison();. For now compilation fails with lots of undefined references.'
2021-02-04 13:12:34 +01:00
distclean: clean
Problem solved. Problems was caused by reading the inverse matrix S_inv the same way as the matrix S, but apparently what has been stored is transpose(S_inv) and not S_inv. Therefore it needs to be read transposed compared to S in order to for S*S_inv to give the Identity matrix. TODO: Calls from C/C++ are still broken because inside the MaponiA3 algo there is compensation for the fact that Fortran sends it's arrays in column-major order. This must be resolved by transposeing S_inv before it is passed to MaponiA3 and remove the compensations made there.
2021-02-22 09:50:42 +01:00
@rm -vf cMaponiA3_test_3x3_3 \
fMaponiA3_test_3x3_3 fMaponiA3_test_4x4_2 \
QMCChem_dataset_test \
Added tests/test to distclean in Makefile
2021-02-22 10:58:27 +01:00
Slater* Updates.dat \
tests/test
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
Prepared the example matrix of Example 8 of the paper and its decomposition in the Fortran code and made a basic call to the subroutine MYSUBROUTINE, which is bound to the C++ void function 'Sherman-Morrison();. For now compilation fails with lots of undefined references.'
2021-02-04 13:12:34 +01:00
## Linking the C++ example program
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
cMaponiA3_test_3x3_3: $(cMaponiA3_test_3x3_3OBJ) $(OBJS)
Prepared the main Fortran infrastructure for the C++ calls. Updated the makefile.
2021-02-04 11:39:00 +01:00
$(CXX) $(ARCH) $(CXXFLAGS) -o $@ $^
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
## Linking Fortran example program calling the C++ function
fMaponiA3_test_3x3_3: $(fMaponiA3_test_3x3_3OBJ) $(OBJS)
$(FC) $(ARCH) $(FFLAGS) $(FLIBS) -o $@ $^
Added QMCChem test dataset with 3 updates. Renamed files, added the Fortran program QMCChem_dataset_test.f90 to start testing the QMCChem dataset. Wrote and tested Fortran code to import the test dataset.
2021-02-12 12:04:21 +01:00
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
fMaponiA3_test_4x4_2: $(fMaponiA3_test_4x4_2OBJ) $(OBJS)
$(FC) $(ARCH) $(FFLAGS) $(FLIBS) -o $@ $^
Add test infrastructure for datasets
2021-02-16 10:49:15 +01:00
The algorithm now works for the following 4x4 example with 2 updates: S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1] S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0] u1 = [0,-2,0,0] u2 = [0,-1,0,0] upd_idx = [2,4] To go from Maponi's examples where the number of updates is always equal to the the dimension of the matrix, and the decomposition is always diagonal, to cases with a non-diagonal decomposition and a number of updates unequal to its size, the following changed needed to be made: * in the calculation of the {y0k} an extra inner for-loop needs to be added to make it a full matrix-vector multiplication due to the fact that A0 is not a diagonal matrix * in some places the use of the update-order vector p needs the be replaced with that of upd_idx to make sure the correct component of the ylk is selected and the proper rank-1 matrices are constructed * when a matrix is passed from Fortran to C++ with 2D adressing, it is passed in colum-major order. The passed matrix needs to be transposed before passing to C++. Doing this inside the algorithm will break compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
QMCChem_dataset_test: $(QMCChem_dataset_testOBJ) $(OBJS)
$(FC) $(ARCH) $(FFLAGS) $(FLIBS) -o $@ $^
Test infrastructure for hdf5 datasets and python hdf5 datasert conversion tool. Made small corrections to compensate for changes made after branching-off from test_dataset. Everything compilers Everything works except for the HDF5 dataset test program that gives an I/O error.
2021-02-22 10:51:07 +01:00
tests/test: tests/test.cpp SM_MaponiA3.o
* Fixed the 3x3 MaponiA3 C++ example. Update_index vector cannot have values that are smaller than 1 because the first colums in Fortran is stored at 1 and not 0. * Started debugging reading from HDF5 formatted datasets. Slater_inv needs to be transposed before sent to Maponi. Algo fails at the last step. Correct Slater and Inverse fail to produce the identity matrix. Suspect that the matMul function is not working correctly eventhough it looks like it does.
2021-02-24 18:41:48 +01:00
$(H5CXX) $(ARCH) $(H5CXXFLAGS) -o $@ $^
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