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Venado optimizations #755

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Venado optimizations #755

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e827603
Add build script for venado hackathon
mewall Jul 2, 2024
096d3fe
Add preliminary method to set matrix size
mewall Jul 18, 2024
eac6576
Add build scripts for hackathon
mewall Jul 18, 2024
e7a8fc0
Add bml_transpose_inplace Fortran subroutine
mewall Jul 19, 2024
b1dcbb1
Modify Fortran bml_transpose API to match the C interface
mewall Jul 22, 2024
7ded60f
Modify tests to use new transpose API
mewall Jul 24, 2024
87d02dd
Venado build modifications
mewall Aug 6, 2024
d766123
Move build scripts to scripts/ dir
mewall Aug 13, 2024
84be6e9
Expose dense matrix pointer using bml_get_ptr_dense
mewall Aug 13, 2024
559bc8e
New introspection methods
mewall Aug 23, 2024
fc03d39
Remove LANL-specific build scripts
mewall Jan 27, 2025
0fc96d2
Remove an additional LANL specific build script
mewall Jan 27, 2025
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48 changes: 48 additions & 0 deletions scripts/build_bml_cray.sh
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#!/bin/bash

# Make sure all the paths are correct

rm -rf build
rm -rf install_cray

MY_PATH=$(pwd)


export CC=${CC:=cc}
#export FC=${FC:=/opt/cray/pe/cce/17.0.0/bin/crayftn}
export FC=${FC:=ftn}
export CXX=${CXX:=CC}
#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-ef -DCRAY_SDK"}
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#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-DINTEL_SDK"}
#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-DCRAY_SDK"}
#export BLAS_VENDOR=${BLAS_VENDOR:=OpenBlas}
#export BLAS_VENDOR=${BLAS_VENDOR:=Intel10_64lp}
#export BLAS_LIBRARIES="-L${MKLROOT}/lib/intel64 -Wl,--no-as-needed -lmkl_intel_lp64 -lmkl_gnu_thread -lmkl_core -lgomp -lpthread -lm -ldl"
#export BLAS_LIBRARIES="-L/projects/darwin-nv/rhel9/aarch64/packages/nvpl/23.11/lib -lnvpl_blas_lp64_gomp"
#export LAPACK_LIBRARIES="-L/projects/darwin-nv/rhel9/aarch64/packages/nvpl/23.11/lib -lnvpl_lapack_lp64_gomp"
export LAPACK_LIBRARIES="-L${CRAY_PE_LIBSCI_PREFIX}/lib"
export BML_OPENMP=${BML_OPENMP:=yes}
export BML_COMPLEX=${BML_COMPLEX:=no}
#export CUDA_ROOT=${CUDA_PATH}
#export CUDA_DIR=${CUDA_PATH}
#export CUDA_INCLUDE=${CUDA_INCLUDES}
#CUDA_INCLUDESexport CUDA_LIB=${CUDA_LIBS}
#export CUDA_TOOLKIT_ROOT_DIR=${CUDATOOLKIT_ROOT}
#export BML_CUDA=${BML_CUDA:=yes}
export BML_CUSOLVER=${BML_CUSOLVER:=yes}
export INSTALL_DIR=${INSTALL_DIR:="${MY_PATH}/install_cray"}
#export BML_TESTING=${BML_TESTING:=yes}
export CMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:=Release}
export MAGMA_ROOT=${MAGMA_ROOT:="/usr/projects/icapt/mewall/venado/packages/magma-2.7.2/install_cray"}
export BML_MAGMA=${BML_MAGMA:=yes}
export CMAKE_PREFIX_PATH="$CUDATOOLKIT_HOME/../../math_libs/lib64"
#export EXTRA_FFLAGS=${EXTRA_FFLAGS:="-DNVHPC_SDK"}
export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-DCRAY_SDK -ef"}
export EXTRA_CFLAGS=${EXTRA_CFLAGS:=""}
export BML_POSIX_MEMALIGN=${BML_POSIX_MEMALIGN:=no}
#export EXTRA_CFLAGS=${EXTRA_CFLAGS:="-DCRAY_SDK -I${CUDA_ROOT}/include -lcudart"}
#export EXTRA_LINK_FLAGS=${EXTRA_LINK_FLAGS:="-DCRAY_SDK -L${CUDA_ROOT}/lib64 -lcudart"}

./build.sh configure


54 changes: 54 additions & 0 deletions scripts/build_bml_cray_nvpl.sh
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#!/bin/bash

# Make sure all the paths are correct

rm -rf build
rm -rf install_cray_nvpl

MY_PATH=$(pwd)


export CC=${CC:=cc}
#export FC=${FC:=/opt/cray/pe/cce/17.0.0/bin/crayftn}
export FC=${FC:=ftn}
export CXX=${CXX:=CC}
#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-ef -DCRAY_SDK"}
#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-DINTEL_SDK"}
#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-DCRAY_SDK"}
#export BLAS_VENDOR=${BLAS_VENDOR:=OpenBlas}
#export BLAS_VENDOR=${BLAS_VENDOR:=Intel10_64lp}
#export BLAS_LIBRARIES="-L${MKLROOT}/lib/intel64 -Wl,--no-as-needed -lmkl_intel_lp64 -lmkl_gnu_thread -lmkl_core -lgomp -lpthread -lm -ldl"
export BLAS_LIBRARIES="-L$NVPL_HOME/lib -lnvpl_blas_lp64_gomp"
export LAPACK_LIBRARIES="-L$NVPL_HOME/lib -lnvpl_lapack_lp64_gomp"
#export LAPACK_LIBRARIES="-L${CRAY_PE_LIBSCI_PREFIX}/lib"
export BML_OPENMP=${BML_OPENMP:=yes}
export BML_COMPLEX=${BML_COMPLEX:=no}
#export CUDA_ROOT=${CUDA_PATH}
#export CUDA_DIR=${CUDA_PATH}
#export CUDA_INCLUDE=${CUDA_INCLUDES}
#CUDA_INCLUDESexport CUDA_LIB=${CUDA_LIBS}
#export CUDA_TOOLKIT_ROOT_DIR=${CUDATOOLKIT_ROOT}
#export BML_CUDA=${BML_CUDA:=yes}
export BML_CUSOLVER=${BML_CUSOLVER:=yes}
export INSTALL_DIR=${INSTALL_DIR:="${MY_PATH}/install_cray_nvpl"}
#export BML_TESTING=${BML_TESTING:=yes}
export CMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:=Release}
export MAGMA_ROOT=${MAGMA_ROOT:="/usr/projects/icapt/mewall/venado/packages/magma-2.7.2/install_cray_nvpl"}
export BML_MAGMA=${BML_MAGMA:=yes}
export CMAKE_PREFIX_PATH="$CUDATOOLKIT_HOME/../../math_libs/lib64"
#export EXTRA_FFLAGS=${EXTRA_FFLAGS:="-DNVHPC_SDK"}
#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-DCRAY_SDK -ef"}
export EXTRA_CFLAGS=${EXTRA_CFLAGS:="-I/usr/include -I/usr/include/c++/12 -I/usr/include/c++/12/aarch64-suse-linux"}
#export CMAKE_C_FLAGS=${CMAKE_CXX_FLAGS:="-I/usr/include -I/usr/include/c++/12 -I/usr/include/c++/12/aarch64-suse-linux"}
#export CMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS:="-I/usr/include -I/usr/include/c++/12 -I/usr/include/c++/12/aarch64-suse-linux"}
export BML_POSIX_MEMALIGN=${BML_POSIX_MEMALIGN:=no}
export CMAKE_C_FLAGS="-homp -g -L/usr/lib64/gcc/aarch64-suse-linux/12 -DCRAY_SDK"
export CMAKE_CXX_FLAGS="-homp -g -L/usr/lib64/gcc/aarch64-suse-linux/12"
export CMAKE_Fortran_FLAGS="-homp -g -ef -L/usr/lib64/gcc/aarch64-suse-linux/12 -DCRAY_SDK"
#export EXTRA_CFLAGS=${EXTRA_CFLAGS:="-DCRAY_SDK -I${CUDA_ROOT}/include -lcudart"}
#export EXTRA_LINK_FLAGS=${EXTRA_LINK_FLAGS:="-DCRAY_SDK -L${CUDA_ROOT}/lib64 -lcudart"}
#EXTRA_LINK_FLAGS="-L/usr/lib64/gcc/aarch64-suse-linux/12"

./build.sh configure


49 changes: 49 additions & 0 deletions scripts/build_bml_nvhpc.sh
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#!/bin/bash

# Make sure all the paths are correct

rm -rf build
rm -rf install_nvhpc

MY_PATH=$(pwd)


export CC=${CC:=nvc}
export FC=${FC:=nvfortran}
export CXX=${CXX:=nvc++}
#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-ef -DCRAY_SDK"}
#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-DINTEL_SDK"}
#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-DCRAY_SDK"}
#export BLAS_VENDOR=${BLAS_VENDOR:=OpenBlas}
#export BLAS_VENDOR=${BLAS_VENDOR:=Intel10_64lp}
#export BLAS_LIBRARIES="-L${MKLROOT}/lib/intel64 -Wl,--no-as-needed -lmkl_intel_lp64 -lmkl_gnu_thread -lmkl_core -lgomp -lpthread -lm -ldl"
#export BLAS_LIBRARIES="-L/projects/darwin-nv/rhel9/aarch64/packages/nvpl/23.11/lib -lnvpl_blas_lp64_gomp"
#export LAPACK_LIBRARIES="-L/projects/darwin-nv/rhel9/aarch64/packages/nvpl/23.11/lib -lnvpl_lapack_lp64_gomp"
#export LAPACK_LIBRARIES="-L${CRAY_PE_LIBSCI_PREFIX}/lib"
export BLAS_LIBRARIES="-L$NVPL_HOME/lib -lnvpl_blas_lp64_gomp"
export LAPACK_LIBRARIES="-L$NVPL_HOME/lib -lnvpl_lapack_lp64_gomp"
export BML_OPENMP=${BML_OPENMP:=yes}
export BML_COMPLEX=${BML_COMPLEX:=yes}
#export CUDA_ROOT=${CUDA_PATH}
export CUDA_DIR=${CUDA_PATH}
#export CUDA_INCLUDE=${CUDA_INCLUDES}
#CUDA_INCLUDESexport CUDA_LIB=${CUDA_LIBS}
#export CUDA_TOOLKIT_ROOT_DIR=${CUDATOOLKIT_ROOT}
#export BML_CUDA=${BML_CUDA:=yes}
export BML_CUSOLVER=${BML_CUSOLVER:=yes}
export INSTALL_DIR=${INSTALL_DIR:="${MY_PATH}/install_nvhpc"}
#export BML_TESTING=${BML_TESTING:=yes}
export CMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:=Release}
export MAGMA_ROOT=${MAGMA_ROOT:="/usr/projects/icapt/mewall/venado/packages/magma-2.7.2/install_nvhpc"}
export BML_MAGMA=${BML_MAGMA:=yes}
export CMAKE_PREFIX_PATH="${NVHPC_ROOT}/math_libs/lib64"
#export EXTRA_FFLAGS=${EXTRA_FFLAGS:="-DNVHPC_SDK"}
export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-DNVHPC_SDK -O2"}
export CMAKE_C_FLAGS=${EXTRA_CFLAGS:="-DNVHPC_SDK -O2"}
#export EXTRA_CFLAGS=${EXTRA_CFLAGS:="-DNVHPC_SDK"}
#export EXTRA_CFLAGS=${EXTRA_CFLAGS:="-DCRAY_SDK -I${CUDA_ROOT}/include -lcudart"}
#export EXTRA_LINK_FLAGS=${EXTRA_LINK_FLAGS:="-DCRAY_SDK -L${CUDA_ROOT}/lib64 -lcudart"}

./build.sh configure


46 changes: 46 additions & 0 deletions scripts/build_chicoma_hackathon.sh
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#!/bin/bash

# Make sure all the paths are correct

rm -r build
#rm -r install_magma_2.7.2
rm -r install_hackathon

MY_PATH=$(pwd)


export CC=${CC:=cc}
export FC=${FC:=ftn}
export CXX=${CXX:=CC}
#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-ef -DCRAY_SDK"}
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#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-DINTEL_SDK"}
#export CMAKE_Fortran_FLAGS=${CMAKE_Fortran_FLAGS:="-DCRAY_SDK"}
export BLAS_VENDOR=${BLAS_VENDOR:=Intel10_64lp}
#export BLAS_VENDOR=${BLAS_VENDOR:=OpenBLAS}
#export LAPACK_LIBRARIES="-L${MKLROOT}/lib/intel64 -Wl,--no-as-needed -lmkl_intel_lp64 -lmkl_gnu_thread -lmkl_core -lgomp -lpthread -lm -ldl"
#export BLAS_LIBRARIES="-L${MKLROOT}/lib/intel64 -Wl,--no-as-needed -lmkl_intel_lp64 -lmkl_gnu_thread -lmkl_core -lgomp -lpthread -lm -ldl"
export BML_OPENMP=${BML_OPENMP:=yes}
export BML_COMPLEX=${BML_COMPLEX:=yes}
#export CUDA_ROOT=${CUDA_PATH}
#export CUDA_DIR=${CUDA_PATH}
#export CUDA_INCLUDE=${CUDA_INCLUDES}
#CUDA_INCLUDESexport CUDA_LIB=${CUDA_LIBS}
#export CUDA_TOOLKIT_ROOT_DIR=${CUDATOOLKIT_ROOT}
#export BML_CUDA=${BML_CUDA:=yes}
export BML_CUSOLVER=${BML_CUSOLVER:=yes}
export INSTALL_DIR=${INSTALL_DIR:="${MY_PATH}/install_hackathon"}
#export BML_TESTING=${BML_TESTING:=yes}
export CMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:=Release}
#export MAGMA_ROOT=${MAGMA_ROOT:="/usr/projects/icapt/mewall/packages/gpmd/magma/install"}
export MAGMA_ROOT=${MAGMA_ROOT:="/usr/projects/icapt/mewall/packages/gpmd/magma-2.7.2/install"}
export BML_MAGMA=${BML_MAGMA:=yes}
#export BML_POSIX_MEMALIGN=${BML_POSIX_MEMALIGN:=no}
#export CMAKE_PREFIX_PATH="$MKLROOT/lib/intel64"
#export EXTRA_FFLAGS=${EXTRA_FFLAGS:="-fdefault-integer-8"}
#export EXTRA_CFLAGS=${EXTRA_CFLAGS:=""}
#export EXTRA_CFLAGS=${EXTRA_CFLAGS:="-DCRAY_SDK -I${CUDA_ROOT}/include -lcudart"}
#export EXTRA_LINK_FLAGS=${EXTRA_LINK_FLAGS:="-DCRAY_SDK -L${CUDA_ROOT}/lib64 -lcudart"}

./build.sh configure


36 changes: 36 additions & 0 deletions scripts/build_venado_hackathon.sh
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#!/bin/bash

# Make sure all the paths are correct

rm -r build
#rm -r install_magma_2.7.2
rm -r install_hackathon

MY_PATH=$(pwd)


export CC=${CC:=cc}
export FC=${FC:=ftn}
export CXX=${CXX:=CC}
#export BLAS_VENDOR=${BLAS_VENDOR:=OpenBLAS}
#export LAPACK_LIBRARIES="-L${MKLROOT}/lib/intel64 -Wl,--no-as-needed -lmkl_intel_lp64 -lmkl_gnu_thread -lmkl_core -lgomp -lpthread -lm -ldl"
#export BLAS_LIBRARIES="-L${MKLROOT}/lib/intel64 -Wl,--no-as-needed -lmkl_intel_lp64 -lmkl_gnu_thread -lmkl_core -lgomp -lpthread -lm -ldl"
export LAPACK_LIBRARIES="-L/opt/cray/pe/libsci/23.12.5/GNU/12.3/aarch64/lib"
export BML_OPENMP=${BML_OPENMP:=yes}
export BML_COMPLEX=${BML_COMPLEX:=yes}
export BML_CUSOLVER=${BML_CUSOLVER:=yes}
export INSTALL_DIR=${INSTALL_DIR:="${MY_PATH}/install_hackathon"}
#export BML_TESTING=${BML_TESTING:=yes}
export CMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:=Release}
export MAGMA_ROOT=${MAGMA_ROOT:="/usr/projects/icapt/mewall/venado/packages/magma-2.7.2/install"}
export BML_MAGMA=${BML_MAGMA:=yes}
#export BML_POSIX_MEMALIGN=${BML_POSIX_MEMALIGN:=no}
export CMAKE_PREFIX_PATH=$CUDA_HOME/../../math_libs/lib64:$CMAKE_PREFIX_PATH
export EXTRA_FFLAGS=${EXTRA_CFLAGS:="-mcpu=neoverse-v2"}
export EXTRA_CFLAGS=${EXTRA_CFLAGS:="-mcpu=neoverse-v2"}
#export EXTRA_CFLAGS=${EXTRA_CFLAGS:="-DCRAY_SDK -I${CUDA_ROOT}/include -lcudart"}
#export EXTRA_LINK_FLAGS=${EXTRA_LINK_FLAGS:="-DCRAY_SDK -L${CUDA_ROOT}/lib64 -lcudart"}

./build.sh configure


19 changes: 19 additions & 0 deletions src/C-interface/dense/bml_introspection_dense.c
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Expand Up @@ -200,3 +200,22 @@ bml_get_data_ptr_dense(
{
return A->matrix;
}

/** Return the dense matrix ld parameter.
*
* \param A The dense matrix.
* \return The matrix ld parameter.
*/
int
bml_get_ld_dense(
bml_matrix_dense_t * A)
{
if (A != NULL)
{
return A->ld;
}
else
{
return -1;
}
}
16 changes: 16 additions & 0 deletions src/C-interface/dense/bml_setters_dense.c
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Expand Up @@ -3,6 +3,22 @@
#include "bml_setters_dense.h"
#include "bml_types_dense.h"

#ifdef BML_USE_MAGMA
#include "magma_v2.h"
#endif

void bml_set_N_dense(
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I don't see a use case for setting N after construction. Is it to avoid constructing a new matrix?

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@jeanlucf22 Yes. This was addressed in the commit comment but the bullets needed fixing to clarify (now corrected). This is to avoid repeated memory allocations which became a bottleneck in MD simulations.

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Looks dangerous to me: you modify N without modifying anything else will lead to matrices in inconsistent state!

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As I mention in the comments, it is unsafe. It's possible to change N so that the matrix will exceed its original allocation. It's exposed only as bml_set_N_dense(), for experts. It works. Can you explain how the matrix will be inconsistent? It's needed for an application, we need to figure out how to make this method available in some form.

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If needed we can protect against exceeding initial size by adding an extra variable to the struct keeping track of the size of the originally allocated matrix. But it's less intrusive to leave the struct alone...

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I believe the performance issue comes from the allocation of the struct members domain and domain2:

bml/src/C-interface/dense/bml_types_dense.h

Line 30 in ef2ee26

bml_domain_t *domain;

I looked into it at some point, but the fix I tried was breaking qmd-progress.
These two struct members could be static or initialized as needed.
Increasing N would obviously lead to a memory allocation too small to hold an NxN matrix.

What is the use case? Is N decreasing and you want to reuse the allocation? Whatever it is, I think looking into domain and domain2 is a safer and better alternative.

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The issue is needing to use a MAGMA working array of differing size on different MD iterations, where max N is known. The problem is that creating a new array each time is slow, due to the GPU allocation. The solution is to allocate using max N once and to resize the array using bml_set_N_dense() as needed. How can I use domain and domain2 to do this? I'm not familiar with those.

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I understand now.

I was not suggesting to use domain and domain2. I was just saying their allocation may be the main culprit when it comes to allocation time for a dense matrix. Maybe an issue to deal with another time.

Another suggestion: having a function resizeNoAlloc(int n) that would just change N if n<=N, otherwise would change N and reallocate memory? Having an extra struct member keeping track of allocated memory size would be good in that case.

bml_matrix_dense_t * A,
int N)
{
A->N = N;
#ifdef BML_USE_MAGMA
A->ld = magma_roundup(A->N, 32);
#else
A->ld = A->N;
#endif
}

void
bml_set_element_dense(
bml_matrix_dense_t * A,
Expand Down
4 changes: 4 additions & 0 deletions src/C-interface/dense/bml_setters_dense.h
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Expand Up @@ -7,6 +7,10 @@

#include <complex.h>

void bml_set_N_dense(
bml_matrix_dense_t * A,
int N);

void bml_set_element_dense(
bml_matrix_dense_t * A,
int i,
Expand Down
23 changes: 23 additions & 0 deletions src/Fortran-interface/bml_c_interface_m.F90
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Expand Up @@ -370,6 +370,18 @@ function bml_get_bandwidth_C(a) bind(C, name="bml_get_bandwidth")
type(C_PTR), value, intent(in) :: a
integer(C_INT) :: bml_get_bandwidth_C
end function bml_get_bandwidth_C

function bml_get_data_ptr_dense_C(a) bind(C, name="bml_get_data_ptr_dense")
import :: C_PTR
type(C_PTR), value, intent(in) :: a
type(C_PTR) :: bml_get_data_ptr_dense_C
end function bml_get_data_ptr_dense_C

function bml_get_ld_dense_C(a) bind(C, name="bml_get_ld_dense")
import :: C_PTR, C_INT
type(C_PTR), value, intent(in) :: a
integer(C_INT) :: bml_get_ld_dense_C
end function bml_get_ld_dense_C

function bml_get_sparsity_C(a, threshold) bind(C, name="bml_get_sparsity")
import :: C_PTR, C_DOUBLE, C_INT
Expand Down Expand Up @@ -448,6 +460,12 @@ subroutine bml_scale_inplace_C(alpha, a) bind(C, name="bml_scale_inplace")
type(C_PTR), value :: a
end subroutine bml_scale_inplace_C

subroutine bml_set_N_dense_C(a,n) bind(C, name="bml_set_N_dense")
import :: C_PTR, C_INT
type(C_PTR), value, intent(in) :: a
integer(C_INT), value, intent(in) :: n
end subroutine bml_set_N_dense_C

subroutine bml_set_row_C(a, i, row, threshold) bind(C, name="bml_set_row")
import :: C_PTR, C_INT, C_DOUBLE
type(C_PTR), value, intent(in) :: a
Expand Down Expand Up @@ -616,6 +634,11 @@ function bml_transpose_new_C(a) bind(C, name="bml_transpose_new")
type(C_PTR) :: bml_transpose_new_C
end function bml_transpose_new_C

subroutine bml_transpose_C(a) bind(C, name="bml_transpose")
import :: C_PTR
type(C_PTR), value, intent(in) :: a
end subroutine bml_transpose_C

subroutine bml_write_bml_matrix_C(a, filename) &
& bind(C, name="bml_write_bml_matrix")
import :: C_PTR, C_CHAR
Expand Down
23 changes: 23 additions & 0 deletions src/Fortran-interface/bml_introspection_m.F90
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Expand Up @@ -18,6 +18,8 @@ module bml_introspection_m
public :: bml_get_bandwidth
public :: bml_get_distribution_mode
public :: bml_get_sparsity
public :: bml_get_data_ptr_dense
public :: bml_get_ld_dense

contains

Expand Down Expand Up @@ -247,4 +249,25 @@ function bml_get_sparsity(a, threshold) result(sparsity)

end function bml_get_sparsity

function bml_get_data_ptr_dense(a)
type(bml_matrix_t), intent(inout) :: a
type(C_PTR) :: bml_get_data_ptr_dense

bml_get_data_ptr_dense = bml_get_data_ptr_dense_C(a%ptr)

end function bml_get_data_ptr_dense

!> Return the dense matrix ld parameter.
!!
!!\param a The matrix.
!!\return The matrix ld parameter.
function bml_get_ld_dense(a)

type(bml_matrix_t), intent(in) :: a
integer :: bml_get_ld_dense

bml_get_ld_dense = bml_get_ld_dense_C(a%ptr)

end function bml_get_ld_dense

end module bml_introspection_m
11 changes: 10 additions & 1 deletion src/Fortran-interface/bml_setters_m.F90
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Expand Up @@ -52,9 +52,18 @@ module bml_setters_m
public :: bml_set_diagonal
public :: bml_set_element
public :: bml_set_element_new

public :: bml_set_N_dense

contains

subroutine bml_set_N_dense(a,n)
type(bml_matrix_t), intent(inout) :: a
integer(C_INT), intent(in) :: n

call bml_set_N_dense_C(a%ptr,n)

end subroutine bml_set_N_dense

!Setters for element new
subroutine bml_set_element_new_single_real(a,i,j,element)

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