2021-02-26 17:28:52 +01:00
|
|
|
SRC_DIR := src
|
|
|
|
|
INC_DIR := include
|
|
|
|
|
OBJ_DIR := build
|
|
|
|
|
BIN_DIR := bin
|
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
|
|
|
|
2021-02-04 18:52:26 +01:00
|
|
|
## Used compilers
|
2021-02-26 17:28:52 +01:00
|
|
|
ARCH = -xCORE-AVX2
|
2021-02-16 10:49:15 +01:00
|
|
|
H5CXX = h5c++
|
2021-02-23 08:28:09 +01:00
|
|
|
CXX = icpc
|
|
|
|
|
FC = ifort
|
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
|
2021-02-24 18:41:48 +01:00
|
|
|
H5CXXFLAGS = -O0 -g
|
|
|
|
|
CXXFLAGS = -O0 -g -traceback
|
|
|
|
|
FFLAGS = -O0 -g -traceback
|
2021-02-26 17:28:52 +01:00
|
|
|
INCLUDE = -I$(INC_DIR)
|
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++
|
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
|
2021-02-26 17:28:52 +01:00
|
|
|
OBJS = $(OBJ_DIR)/SM_MaponiA3.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
|
|
|
cMaponiA3_test_3x3_3OBJ = cMaponiA3_test_3x3_3.o
|
|
|
|
|
fMaponiA3_test_3x3_3OBJ = SM_MaponiA3_mod.o fMaponiA3_test_3x3_3.o
|
2021-02-24 18:41:48 +01:00
|
|
|
fMaponiA3_test_4x4_2OBJ = Helpers_mod.o SM_MaponiA3_mod.o fMaponiA3_test_4x4_2.o
|
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
|
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
|
|
|
|
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++
|
2021-02-26 17:28:52 +01:00
|
|
|
$(OBJ_DIR)/%.o: $(SRC_DIR)/%.cpp | $(OBJ_DIR)
|
|
|
|
|
$(CXX) $(ARCH) $(CXXFLAGS) $(INCLUDE) -c -o $@ $<
|
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
|
2021-02-26 17:28:52 +01:00
|
|
|
$(OBJ_DIR)/%.o: $(SRC_DIR)/%.f90 | $(OBJ_DIR)
|
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
|
2021-02-26 17:28:52 +01:00
|
|
|
$(OBJ_DIR)/SM_MaponiA3.o: $(SRC_DIR)/SM_MaponiA3.cpp $(INC_DIR)/Helpers.hpp| $(OBJ_DIR)
|
|
|
|
|
$(CXX) $(ARCH) $(CXXFLAGS) $(INCLUDE) -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
|
|
|
|
|
|
|
|
|
2021-02-04 18:52:26 +01:00
|
|
|
## Build tagets
|
2021-02-04 13:12:34 +01:00
|
|
|
.PHONY: all clean distclean
|
|
|
|
|
|
|
|
|
|
clean:
|
2021-02-26 17:28:52 +01:00
|
|
|
@rm -vrf $(OBJ_DIR)
|
2021-02-09 13:40:52 +01:00
|
|
|
|
2021-02-04 13:12:34 +01:00
|
|
|
distclean: clean
|
2021-02-26 17:28:52 +01:00
|
|
|
@rm -vrf $(BIN_DIR) \
|
|
|
|
|
Slater* Updates.dat
|
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
|
|
|
|
2021-02-04 13:12:34 +01:00
|
|
|
|
|
|
|
|
## Linking the C++ example program
|
2021-02-26 17:28:52 +01:00
|
|
|
$(BIN_DIR)/cMaponiA3_test_3x3_3: $(OBJ_DIR)/$(cMaponiA3_test_3x3_3OBJ) $(OBJS) | $(BIN_DIR)
|
|
|
|
|
$(CXX) $(ARCH) $(CXXFLAGS) $(INCLUDE) -o $@ $^
|
2021-02-04 11:39:00 +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
|
|
|
## Linking Fortran example program calling the C++ function
|
2021-02-26 17:28:52 +01:00
|
|
|
$(BIN_DIR)/fMaponiA3_test_3x3_3: $(OBJ_DIR)/$(fMaponiA3_test_3x3_3OBJ) $(OBJS) | $(BIN_DIR)
|
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
|
|
|
$(FC) $(ARCH) $(FFLAGS) $(FLIBS) -o $@ $^
|
2021-02-12 12:04:21 +01:00
|
|
|
|
2021-02-26 17:28:52 +01:00
|
|
|
$(BIN_DIR)/fMaponiA3_test_4x4_2: $(OBJ_DIR)/$(fMaponiA3_test_4x4_2OBJ) $(OBJS) | $(BIN_DIR)
|
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
|
|
|
$(FC) $(ARCH) $(FFLAGS) $(FLIBS) -o $@ $^
|
2021-02-16 10:49:15 +01:00
|
|
|
|
2021-02-26 17:28:52 +01:00
|
|
|
$(BIN_DIR)/QMCChem_dataset_test: $(OBJ_DIR)/$(QMCChem_dataset_testOBJ) $(OBJS) | $(BIN_DIR)
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The algorithm now works for the following 4x4 example with 2 updates:
S = [1,0,1,-1; 0,1,1,0; -1,0,-1,0; 1,1,1,1]
S_inv = [1,-1,1,1; 1,0,2,1; -1,1,-2,-1; -1,0,-1,0]
u1 = [0,-2,0,0]
u2 = [0,-1,0,0]
upd_idx = [2,4]
To go from Maponi's examples where the number of updates is always equal
to the the dimension of the matrix, and the decomposition is always
diagonal, to cases with a non-diagonal decomposition and a number of
updates unequal to its size, the following changed needed to be made:
* in the calculation of the {y0k} an extra inner for-loop needs to be
added to make it a full matrix-vector multiplication due to the fact
that A0 is not a diagonal matrix
* in some places the use of the update-order vector p needs
the be replaced with that of upd_idx to make sure the correct
component of the ylk is selected and the proper rank-1 matrices are
constructed
* when a matrix is passed from Fortran to C++ with 2D adressing, it is
passed in colum-major order. The passed matrix needs to be transposed
before passing to C++. Doing this inside the algorithm will break
compatibility with called from C/C++.
2021-02-21 18:28:08 +01:00
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$(FC) $(ARCH) $(FFLAGS) $(FLIBS) -o $@ $^
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2021-02-22 10:51:07 +01:00
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2021-02-26 17:28:52 +01:00
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$(BIN_DIR)/test: $(SRC_DIR)/test.cpp $(OBJS) | $(BIN_DIR)
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$(H5CXX) $(H5CXXFLAGS) $(INCLUDE) -o $@ $^
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$(BIN_DIR) $(OBJ_DIR):
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mkdir -p $@
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