Remove unnecessary memory allocations when using SuiteSparse.
1. Add SuiteSparse::CreateDenseVectorView
2. Replace calls to SuiteSparse::CreateDenseVector with
SuiteSparse::CreateDenseVectorView.
2. Replace NULL with nullptr in suitesparse.cc and
dynamic_sparse_normal_cholesky_solver.cc
Change-Id: I94355c1dc27789e5b987a7b2850e9db6176a0914
diff --git a/internal/ceres/dynamic_sparse_normal_cholesky_solver.cc b/internal/ceres/dynamic_sparse_normal_cholesky_solver.cc
index a46c85e..f966083 100644
--- a/internal/ceres/dynamic_sparse_normal_cholesky_solver.cc
+++ b/internal/ceres/dynamic_sparse_normal_cholesky_solver.cc
@@ -66,7 +66,7 @@
VectorRef(x, num_cols).setZero();
A->LeftMultiply(b, x);
- if (per_solve_options.D != NULL) {
+ if (per_solve_options.D != nullptr) {
// Temporarily append a diagonal block to the A matrix, but undo
// it before returning the matrix to the user.
std::unique_ptr<CompressedRowSparseMatrix> regularizer;
@@ -96,7 +96,7 @@
<< options_.sparse_linear_algebra_library_type;
}
- if (per_solve_options.D != NULL) {
+ if (per_solve_options.D != nullptr) {
A->DeleteRows(num_cols);
}
@@ -253,19 +253,18 @@
cholmod_factor* factor = ss.AnalyzeCholesky(&lhs, &summary.message);
event_logger.AddEvent("Analysis");
- if (factor == NULL) {
+ if (factor == nullptr) {
summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
return summary;
}
summary.termination_type = ss.Cholesky(&lhs, factor, &summary.message);
if (summary.termination_type == LINEAR_SOLVER_SUCCESS) {
- cholmod_dense* rhs =
- ss.CreateDenseVector(rhs_and_solution, num_cols, num_cols);
- cholmod_dense* solution = ss.Solve(factor, rhs, &summary.message);
+ cholmod_dense cholmod_rhs =
+ ss.CreateDenseVectorView(rhs_and_solution, num_cols);
+ cholmod_dense* solution = ss.Solve(factor, &cholmod_rhs, &summary.message);
event_logger.AddEvent("Solve");
- ss.Free(rhs);
- if (solution != NULL) {
+ if (solution != nullptr) {
memcpy(
rhs_and_solution, solution->x, num_cols * sizeof(*rhs_and_solution));
ss.Free(solution);
diff --git a/internal/ceres/suitesparse.cc b/internal/ceres/suitesparse.cc
index 5e93d2e..cc07a01 100644
--- a/internal/ceres/suitesparse.cc
+++ b/internal/ceres/suitesparse.cc
@@ -97,11 +97,11 @@
m.nrow = A->num_cols();
m.ncol = A->num_rows();
m.nzmax = A->num_nonzeros();
- m.nz = NULL;
+ m.nz = nullptr;
m.p = reinterpret_cast<void*>(A->mutable_rows());
m.i = reinterpret_cast<void*>(A->mutable_cols());
m.x = reinterpret_cast<void*>(A->mutable_values());
- m.z = NULL;
+ m.z = nullptr;
if (A->storage_type() == CompressedRowSparseMatrix::LOWER_TRIANGULAR) {
m.stype = 1;
@@ -120,12 +120,24 @@
return m;
}
+cholmod_dense SuiteSparse::CreateDenseVectorView(const double* x, int size) {
+ cholmod_dense v;
+ v.nrow = size;
+ v.ncol = 1;
+ v.nzmax = size;
+ v.d = size;
+ v.x = const_cast<void*>(reinterpret_cast<const void*>(x));
+ v.xtype = CHOLMOD_REAL;
+ v.dtype = CHOLMOD_DOUBLE;
+ return v;
+}
+
cholmod_dense* SuiteSparse::CreateDenseVector(const double* x,
int in_size,
int out_size) {
CHECK_LE(in_size, out_size);
cholmod_dense* v = cholmod_zeros(out_size, 1, CHOLMOD_REAL, &cc_);
- if (x != NULL) {
+ if (x != nullptr) {
memcpy(v->x, x, in_size * sizeof(*x));
}
return v;
@@ -149,7 +161,7 @@
if (cc_.status != CHOLMOD_OK) {
*message =
StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
- return NULL;
+ return nullptr;
}
return CHECK_NOTNULL(factor);
@@ -161,7 +173,7 @@
string* message) {
vector<int> ordering;
if (!BlockAMDOrdering(A, row_blocks, col_blocks, &ordering)) {
- return NULL;
+ return nullptr;
}
return AnalyzeCholeskyWithUserOrdering(A, ordering, message);
}
@@ -174,14 +186,14 @@
cc_.method[0].ordering = CHOLMOD_GIVEN;
cholmod_factor* factor =
- cholmod_analyze_p(A, const_cast<int*>(&ordering[0]), NULL, 0, &cc_);
+ cholmod_analyze_p(A, const_cast<int*>(&ordering[0]), nullptr, 0, &cc_);
if (VLOG_IS_ON(2)) {
cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
}
if (cc_.status != CHOLMOD_OK) {
*message =
StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
- return NULL;
+ return nullptr;
}
return CHECK_NOTNULL(factor);
@@ -200,7 +212,7 @@
if (cc_.status != CHOLMOD_OK) {
*message =
StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
- return NULL;
+ return nullptr;
}
return CHECK_NOTNULL(factor);
@@ -230,7 +242,7 @@
block_matrix.nzmax = block_rows.size();
block_matrix.p = reinterpret_cast<void*>(&block_cols[0]);
block_matrix.i = reinterpret_cast<void*>(&block_rows[0]);
- block_matrix.x = NULL;
+ block_matrix.x = nullptr;
block_matrix.stype = A->stype;
block_matrix.itype = CHOLMOD_INT;
block_matrix.xtype = CHOLMOD_PATTERN;
@@ -239,7 +251,7 @@
block_matrix.packed = 1;
vector<int> block_ordering(num_row_blocks);
- if (!cholmod_amd(&block_matrix, NULL, 0, &block_ordering[0], &cc_)) {
+ if (!cholmod_amd(&block_matrix, nullptr, 0, &block_ordering[0], &cc_)) {
return false;
}
@@ -311,7 +323,7 @@
string* message) {
if (cc_.status != CHOLMOD_OK) {
*message = "cholmod_solve failed. CHOLMOD status is not CHOLMOD_OK";
- return NULL;
+ return nullptr;
}
return cholmod_solve(CHOLMOD_A, L, b, &cc_);
@@ -319,13 +331,13 @@
bool SuiteSparse::ApproximateMinimumDegreeOrdering(cholmod_sparse* matrix,
int* ordering) {
- return cholmod_amd(matrix, NULL, 0, ordering, &cc_);
+ return cholmod_amd(matrix, nullptr, 0, ordering, &cc_);
}
bool SuiteSparse::ConstrainedApproximateMinimumDegreeOrdering(
cholmod_sparse* matrix, int* constraints, int* ordering) {
#ifndef CERES_NO_CAMD
- return cholmod_camd(matrix, NULL, 0, constraints, ordering, &cc_);
+ return cholmod_camd(matrix, nullptr, 0, constraints, ordering, &cc_);
#else
LOG(FATAL) << "Congratulations you have found a bug in Ceres."
<< "Ceres Solver was compiled with SuiteSparse "
@@ -342,24 +354,24 @@
}
SuiteSparseCholesky::SuiteSparseCholesky(const OrderingType ordering_type)
- : ordering_type_(ordering_type), factor_(NULL) {}
+ : ordering_type_(ordering_type), factor_(nullptr) {}
SuiteSparseCholesky::~SuiteSparseCholesky() {
- if (factor_ != NULL) {
+ if (factor_ != nullptr) {
ss_.Free(factor_);
}
}
LinearSolverTerminationType SuiteSparseCholesky::Factorize(
CompressedRowSparseMatrix* lhs, string* message) {
- if (lhs == NULL) {
+ if (lhs == nullptr) {
*message = "Failure: Input lhs is NULL.";
return LINEAR_SOLVER_FATAL_ERROR;
}
cholmod_sparse cholmod_lhs = ss_.CreateSparseMatrixTransposeView(lhs);
- if (factor_ == NULL) {
+ if (factor_ == nullptr) {
if (ordering_type_ == NATURAL) {
factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(&cholmod_lhs, message);
} else {
@@ -371,7 +383,7 @@
}
}
- if (factor_ == NULL) {
+ if (factor_ == nullptr) {
return LINEAR_SOLVER_FATAL_ERROR;
}
}
@@ -390,18 +402,17 @@
double* solution,
string* message) {
// Error checking
- if (factor_ == NULL) {
+ if (factor_ == nullptr) {
*message = "Solve called without a call to Factorize first.";
return LINEAR_SOLVER_FATAL_ERROR;
}
const int num_cols = factor_->n;
- cholmod_dense* cholmod_dense_rhs =
- ss_.CreateDenseVector(rhs, num_cols, num_cols);
+ cholmod_dense cholmod_rhs = ss_.CreateDenseVectorView(rhs, num_cols);
cholmod_dense* cholmod_dense_solution =
- ss_.Solve(factor_, cholmod_dense_rhs, message);
- ss_.Free(cholmod_dense_rhs);
- if (cholmod_dense_solution == NULL) {
+ ss_.Solve(factor_, &cholmod_rhs, message);
+
+ if (cholmod_dense_solution == nullptr) {
return LINEAR_SOLVER_FAILURE;
}
diff --git a/internal/ceres/suitesparse.h b/internal/ceres/suitesparse.h
index 0646602..91cd24b 100644
--- a/internal/ceres/suitesparse.h
+++ b/internal/ceres/suitesparse.h
@@ -99,6 +99,11 @@
// use the SuiteSparse machinery to allocate memory.
cholmod_sparse CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A);
+ // Create a cholmod_dense vector around the contents of the array x.
+ // This is a shallow object, which refers to the contents of x and
+ // does not use the SuiteSparse machinery to allocate memory.
+ cholmod_dense CreateDenseVectorView(const double* x, int size);
+
// Given a vector x, build a cholmod_dense vector of size out_size
// with the first in_size entries copied from x. If x is NULL, then
// an all zeros vector is returned. Caller owns the result.
