Reduce memory consumption

lap.h

@@ -228,15 +228,14 @@ find_umins(
 /// @param u out dual variables, row reduction numbers / size dim
 /// @param v out dual variables, column reduction numbers / size dim
 /// @return achieved minimum assignment cost
-template <bool avx2, typename idx, typename cost>
-cost lap(int dim, const cost *restrict assign_cost, bool verbose,
+template <bool avx2, bool verbose, typename idx, typename cost>
+cost lap(int dim, const cost *restrict assign_cost,
          idx *restrict rowsol, idx *restrict colsol,
          cost *restrict u, cost *restrict v) {
-  auto free = std::unique_ptr<idx[]>(new idx[dim]);     // list of unassigned rows.
-  auto collist = std::unique_ptr<idx[]>(new idx[dim]);  // list of columns to be scanned in various ways.
-  auto matches = std::unique_ptr<idx[]>(new idx[dim]);  // counts how many times a row could be assigned.
-  auto d = std::unique_ptr<cost[]>(new cost[dim]);      // 'cost-distance' in augmenting path calculation.
-  auto pred = std::unique_ptr<idx[]>(new idx[dim]);     // row-predecessor of column in augmenting/alternating path.
+  auto collist = std::make_unique<idx[]>(dim);  // list of columns to be scanned in various ways.
+  auto matches = std::make_unique<idx[]>(dim);  // counts how many times a row could be assigned.
+  auto d = std::make_unique<cost[]>(dim);       // 'cost-distance' in augmenting path calculation.
+  auto pred = std::make_unique<idx[]>(dim);     // row-predecessor of column in augmenting/alternating path.
 
   // init how many times a row will be assigned in the column reduction.
   #if _OPENMP >= 201307
@@ -273,6 +272,7 @@ cost lap(int dim, const cost *restrict assign_cost, bool verbose,
   }
 
   // REDUCTION TRANSFER
+  auto free = matches.get();  // list of unassigned rows.
   idx numfree = 0;
   for (idx i = 0; i < dim; i++) {
     const cost *local_cost = &assign_cost[i * dim];

python.cc

@@ -64,7 +64,8 @@ using pyobj = _pyobj<PyObject>;
 using pyarray = _pyobj<PyArrayObject>;
 
 template <typename F>
-static always_inline double call_lap(int dim, const void *restrict cost_matrix, bool verbose,
+static always_inline double call_lap(int dim, const void *restrict cost_matrix,
+                                     bool verbose, bool disable_avx,
                                      int *restrict row_ind, int *restrict col_ind,
                                      void *restrict u, void *restrict v) {
   double lapcost;
@@ -76,10 +77,18 @@ static always_inline double call_lap(int dim, const void *restrict cost_matrix,
   auto cost_matrix_typed = reinterpret_cast<const F*>(cost_matrix);
   auto u_typed = reinterpret_cast<F*>(u);
   auto v_typed = reinterpret_cast<F*>(v);
-  if (hasAVX2) {
-    lapcost = lap<true>(dim, cost_matrix_typed, verbose, row_ind, col_ind, u_typed, v_typed);
+  if (hasAVX2 && !disable_avx) {
+    if (verbose) {
+      lapcost = lap<true, true>(dim, cost_matrix_typed, row_ind, col_ind, u_typed, v_typed);
+    } else {
+      lapcost = lap<true, false>(dim, cost_matrix_typed, row_ind, col_ind, u_typed, v_typed);
+    }
   } else {
-    lapcost = lap<false>(dim, cost_matrix_typed, verbose, row_ind, col_ind, u_typed, v_typed);
+    if (verbose) {
+      lapcost = lap<false, true>(dim, cost_matrix_typed, row_ind, col_ind, u_typed, v_typed);
+    } else {
+      lapcost = lap<false, false>(dim, cost_matrix_typed, row_ind, col_ind, u_typed, v_typed);
+    }
   }
   Py_END_ALLOW_THREADS
   return lapcost;
@@ -88,12 +97,13 @@ static always_inline double call_lap(int dim, const void *restrict cost_matrix,
 static PyObject *py_lapjv(PyObject *self, PyObject *args, PyObject *kwargs) {
   PyObject *cost_matrix_obj;
   int verbose = 0;
+  int disable_avx = 0;
   int force_doubles = 0;
   static const char *kwlist[] = {
-      "cost_matrix", "verbose", "force_doubles", NULL};
+      "cost_matrix", "verbose", "disable_avx", "force_doubles", NULL};
   if (!PyArg_ParseTupleAndKeywords(
-      args, kwargs, "O|pb", const_cast<char**>(kwlist),
-      &cost_matrix_obj, &verbose, &force_doubles)) {
+      args, kwargs, "O|pbb", const_cast<char**>(kwlist),
+      &cost_matrix_obj, &verbose, &disable_avx, &force_doubles)) {
     return NULL;
   }
   pyarray cost_matrix_array;
@@ -144,9 +154,9 @@ static PyObject *py_lapjv(PyObject *self, PyObject *args, PyObject *kwargs) {
   auto u = PyArray_DATA(u_array.get());
   auto v = PyArray_DATA(v_array.get());
   if (float32) {
-    lapcost = call_lap<float>(dim, cost_matrix, verbose, row_ind, col_ind, u, v);
+    lapcost = call_lap<float>(dim, cost_matrix, verbose, disable_avx, row_ind, col_ind, u, v);
   } else {
-    lapcost = call_lap<double>(dim, cost_matrix, verbose, row_ind, col_ind, u, v);
+    lapcost = call_lap<double>(dim, cost_matrix, verbose, disable_avx, row_ind, col_ind, u, v);
   }
   return Py_BuildValue("(OO(dOO))",
                        row_ind_array.get(), col_ind_array.get(), lapcost,