1 files changed, 26 insertions, 46 deletions

diff --git a/src/km-solve.c b/src/km-solve.c
index b6d98d6..271795b 100644
--- a/src/km-solve.c
+++ b/src/km-solve.c
@@ -6,36 +6,18 @@
 
 #define MIN_DELTA 0.00001
 
-// alloc and initialize row map
-static km_row_map_t *
-km_row_map_init(
+static void
+km_row_map_clear(
+  km_row_map_t * const row_map,
   const size_t num_rows
 ) {
-  // alloc row map
-  km_row_map_t * const row_map = malloc(sizeof(km_row_map_t) * num_rows);
-
-  // check for error
-  if (!row_map) {
-    // return failure
-    return false;
-  }
-
   // init row map
+  #pragma omp parallel for
   for (size_t i = 0; i < num_rows; i++) {
     // setting distances to maximum
     row_map[i].d2 = FLT_MAX;
     row_map[i].d2_near = FLT_MAX;
   }
-
-  // return row map
-  return row_map;
-}
-
-static void
-km_row_map_fini(
-  km_row_map_t * const row_map
-) {
-  free(row_map);
 }
 
 // use k-means to iteratively update the cluster centroids until there
@@ -44,18 +26,15 @@ bool
 km_solve(
   km_set_t * const cs,
   const km_set_t * const set,
+  km_row_map_t * const row_map,
   const km_solve_cbs_t * const cbs,
   void * const cb_data
 ) {
   const size_t num_clusters = cs->num_rows,
                num_floats = set->shape.num_floats;
 
-  // row map: row => distance, cluster ID
-  km_row_map_t *row_map = km_row_map_init(set->num_rows);
-  if (!row_map) {
-    // return failure
-    return false;
-  }
+  // clear row map distances
+  km_row_map_clear(row_map, set->num_rows);
 
   // calculate clusters by doing the following:
   //   * walk all clusters and all rows
@@ -68,11 +47,13 @@ km_solve(
     // no changes yet
     changed = false;
 
+    #pragma omp parallel for collapse(2)
     for (size_t i = 0; i < num_clusters; i++) {
-      // get the floats for this cluster
-      const float * const floats = km_set_get_row(cs, i);
-
       for (size_t j = 0; j < set->num_rows; j++) {
+        // get the floats for this cluster
+        // NOTE: moved to inner loop for "collapse(2)" above
+        const float * const floats = km_set_get_row(cs, i);
+
         // get row values
         const float * const vals = km_set_get_row(set, j);
 
@@ -101,7 +82,8 @@ km_solve(
       // if there were changes, then we need to calculate the new
       // cluster centers
 
-      // calculate new center
+      // calculate new cluster centers
+      #pragma omp parallel for
       for (size_t i = 0; i < num_clusters; i++) {
         size_t num_rows = 0;
         float * const floats = km_set_get_row(cs, i);
@@ -141,30 +123,31 @@ km_solve(
 
   if (cbs && cbs->on_stats) {
     float sum = 0,
-          silouette = 0;
+          silhouette = 0;
 
-    // calculate mean numerators and silouette
+    // calculate mean numerators and silhouette
+    #pragma omp parallel for reduction(+:sum,silhouette)
     for (size_t i = 0; i < set->num_rows; i++) {
-      // sum distances
-      sum += row_map[i].d2;
-
-      // calculate silouette denominator
+      // calculate silhouette denominator
       // (https://en.wikipedia.org/wiki/Silhouette_%28clustering%29)
       const float delta = (row_map[i].d2_near - row_map[i].d2);
 
-      // sum silouette
-      silouette += (delta < -MIN_DELTA || delta > MIN_DELTA)
+      // sum silhouette
+      silhouette += (delta < -MIN_DELTA || delta > MIN_DELTA)
         ? (delta / MAX(row_map[i].d2, row_map[i].d2_near))
         : 0.0;
+
+      // sum distances
+      sum += row_map[i].d2;
     }
 
-    // finalize silouette
-    silouette /= set->num_rows;
+    // finalize silhouette
+    silhouette /= set->num_rows;
 
     // build stats
     const km_solve_stats_t stats = {
       .sum          = sum,
-      .silouette    = silouette,
+      .silhouette   = silhouette,
       .num_clusters = num_clusters,
     };
 
@@ -172,9 +155,6 @@ km_solve(
     cbs->on_stats(cs, &stats, cb_data);
   }
 
-  // free row_map
-  km_row_map_fini(row_map);
-
   // return success
   return true;
 }