fix some light omp loop .

include/LightGBM/tree.h

@@ -102,7 +102,7 @@ class Tree {
   * \param rate The factor of shrinkage
   */
   inline void Shrinkage(double rate) {
-    #pragma omp parallel for schedule(static)
+    #pragma omp parallel for schedule(static, 512) if (num_leaves_ >= 1024)
     for (int i = 0; i < num_leaves_; ++i) {
       leaf_value_[i] *= rate;
       if (leaf_value_[i] > kMaxTreeOutput) { leaf_value_[i] = kMaxTreeOutput; } 

include/LightGBM/utils/array_args.h

@@ -47,7 +47,7 @@ class ArrayArgs {
     if (array.empty()) {
       return 0;
     }
-    if (array.size() > 100) {
+    if (array.size() > 1024) {
       return ArgMaxMT(array);
     } else {
       size_t arg_max = 0;

src/treelearner/data_partition.hpp

@@ -24,8 +24,8 @@ class DataPartition {
     temp_left_indices_.resize(num_data_);
     temp_right_indices_.resize(num_data_);
     used_data_indices_ = nullptr;
-#pragma omp parallel
-#pragma omp master
+    #pragma omp parallel
+    #pragma omp master
     {
       num_threads_ = omp_get_num_threads();
     }
@@ -60,7 +60,7 @@ class DataPartition {
     if (used_data_indices_ == nullptr) {
       // if using all data
       leaf_count_[0] = num_data_;
-#pragma omp parallel for schedule(static)
+      #pragma omp parallel for schedule(static)
       for (data_size_t i = 0; i < num_data_; ++i) {
         indices_[i] = i;
       }
@@ -92,7 +92,7 @@ class DataPartition {
   * \param right_leaf index of right leaf
   */
   void Split(int leaf, const Dataset* dataset, int feature, uint32_t threshold, int right_leaf) {
-    const data_size_t min_inner_size = 1000;
+    const data_size_t min_inner_size = 512;
     // get leaf boundary
     const data_size_t begin = leaf_begin_[leaf];
     const data_size_t cnt = leaf_count_[leaf];
@@ -101,7 +101,7 @@ class DataPartition {
     if (inner_size < min_inner_size) { inner_size = min_inner_size; }
     // split data multi-threading
     OMP_INIT_EX();
-#pragma omp parallel for schedule(static, 1)
+    #pragma omp parallel for schedule(static, 1)
     for (int i = 0; i < num_threads_; ++i) {
       OMP_LOOP_EX_BEGIN();
       left_cnts_buf_[i] = 0;
@@ -112,7 +112,7 @@ class DataPartition {
       if (cur_start + cur_cnt > cnt) { cur_cnt = cnt - cur_start; }
       // split data inner, reduce the times of function called
       data_size_t cur_left_count = dataset->Split(feature, threshold, indices_.data() + begin + cur_start, cur_cnt,
-        temp_left_indices_.data() + cur_start, temp_right_indices_.data() + cur_start);
+                                                  temp_left_indices_.data() + cur_start, temp_right_indices_.data() + cur_start);
       offsets_buf_[i] = cur_start;
       left_cnts_buf_[i] = cur_left_count;
       right_cnts_buf_[i] = cur_cnt - cur_left_count;
@@ -128,15 +128,15 @@ class DataPartition {
     }
     left_cnt = left_write_pos_buf_[num_threads_ - 1] + left_cnts_buf_[num_threads_ - 1];
     // copy back indices of right leaf to indices_
-#pragma omp parallel for schedule(static, 1)
+    #pragma omp parallel for schedule(static, 1)
     for (int i = 0; i < num_threads_; ++i) {
       if (left_cnts_buf_[i] > 0) {
-        std::memcpy(indices_.data() + begin + left_write_pos_buf_[i], 
-          temp_left_indices_.data() + offsets_buf_[i], left_cnts_buf_[i] * sizeof(data_size_t));
+        std::memcpy(indices_.data() + begin + left_write_pos_buf_[i],
+                    temp_left_indices_.data() + offsets_buf_[i], left_cnts_buf_[i] * sizeof(data_size_t));
       }
       if (right_cnts_buf_[i] > 0) {
-        std::memcpy(indices_.data() + begin + left_cnt + right_write_pos_buf_[i], 
-          temp_right_indices_.data() + offsets_buf_[i], right_cnts_buf_[i] * sizeof(data_size_t));
+        std::memcpy(indices_.data() + begin + left_cnt + right_write_pos_buf_[i],
+                    temp_right_indices_.data() + offsets_buf_[i], right_cnts_buf_[i] * sizeof(data_size_t));
       }
     }
     // update leaf boundary

src/treelearner/feature_histogram.hpp

@@ -359,9 +359,10 @@ class HistogramPool {
 
   void DynamicChangeSize(const Dataset* train_data, const TreeConfig* tree_config, int cache_size, int total_size) {
     if (feature_metas_.empty()) {
-      feature_metas_.resize(train_data->num_features());
-      #pragma omp parallel for schedule(static)
-      for (int i = 0; i < train_data->num_features(); ++i) {
+      int num_feature = train_data->num_features();
+      feature_metas_.resize(num_feature);
+      #pragma omp parallel for schedule(static, 512) if(num_feature >= 1024)
+      for (int i = 0; i < num_feature; ++i) {
         feature_metas_[i].num_bin = train_data->FeatureNumBin(i);
         if (train_data->FeatureBinMapper(i)->GetDefaultBin() == 0) {
           feature_metas_[i].bias = 1;
@@ -400,8 +401,9 @@ class HistogramPool {
   }
 
   void ResetConfig(const TreeConfig* tree_config) {
-    #pragma omp parallel for schedule(static)
-    for (int i = 0; i < static_cast<int>(feature_metas_.size()); ++i) {
+    int size = static_cast<int>(feature_metas_.size());
+    #pragma omp parallel for schedule(static, 512) if(size >= 1024)
+    for (int i = 0; i < size; ++i) {
       feature_metas_[i].tree_config = tree_config;
     }
   }

src/treelearner/serial_tree_learner.cpp

@@ -259,14 +259,15 @@ void SerialTreeLearner::BeforeTrain() {
     std::memset(is_feature_used_.data(), 0, sizeof(int8_t) * num_features_);
     // Get used feature at current tree
     auto used_feature_indices = random_.Sample(train_data_->num_total_features(), used_feature_cnt);
-    #pragma omp parallel for schedule(static)
-    for (int i = 0; i < static_cast<int>(used_feature_indices.size()); ++i) {
+    int omp_loop_size = static_cast<int>(used_feature_indices.size());
+    #pragma omp parallel for schedule(static, 512) if (omp_loop_size >= 1024)
+    for (int i = 0; i < omp_loop_size; ++i) {
       int inner_feature_index = train_data_->InnerFeatureIndex(used_feature_indices[i]);
       if (inner_feature_index < 0) { continue; }
       is_feature_used_[inner_feature_index] = 1;
     }
   } else {
-    #pragma omp parallel for schedule(static)
+    #pragma omp parallel for schedule(static, 512) if (num_features_ >= 1024)
     for (int i = 0; i < num_features_; ++i) {
       is_feature_used_[i] = 1;
     }
@@ -314,7 +315,8 @@ void SerialTreeLearner::BeforeTrain() {
       const data_size_t* indices = data_partition_->indices();
       data_size_t begin = data_partition_->leaf_begin(0);
       data_size_t end = begin + data_partition_->leaf_count(0);
-      #pragma omp parallel for schedule(static)
+      data_size_t loop_size = end - begin;
+      #pragma omp parallel for schedule(static, 512) if(loop_size >= 1024)
       for (data_size_t i = begin; i < end; ++i) {
         is_data_in_leaf_[indices[i]] = 1;
       }
@@ -327,7 +329,7 @@ void SerialTreeLearner::BeforeTrain() {
         OMP_LOOP_EX_END();
       }
       OMP_THROW_EX();
-      #pragma omp parallel for schedule(static)
+      #pragma omp parallel for schedule(static, 512) if(loop_size >= 1024)
       for (data_size_t i = begin; i < end; ++i) {
         is_data_in_leaf_[indices[i]] = 0;
       }
@@ -388,12 +390,13 @@ bool SerialTreeLearner::BeforeFindBestSplit(const Tree* tree, int left_leaf, int
     char mark = 1;
     data_size_t begin = data_partition_->leaf_begin(left_leaf);
     data_size_t end = begin + left_cnt;
+    data_size_t loop_size = end - begin;
     if (left_cnt > right_cnt) {
       begin = data_partition_->leaf_begin(right_leaf);
       end = begin + right_cnt;
       mark = 0;
     }
-    #pragma omp parallel for schedule(static)
+    #pragma omp parallel for schedule(static, 512) if(loop_size >= 1024)
     for (data_size_t i = begin; i < end; ++i) {
       is_data_in_leaf_[indices[i]] = 1;
     }
@@ -406,7 +409,7 @@ bool SerialTreeLearner::BeforeFindBestSplit(const Tree* tree, int left_leaf, int
       OMP_LOOP_EX_END();
     }
     OMP_THROW_EX();
-    #pragma omp parallel for schedule(static)
+    #pragma omp parallel for schedule(static, 512) if(loop_size >= 1024)
     for (data_size_t i = begin; i < end; ++i) {
       is_data_in_leaf_[indices[i]] = 0;
     }
@@ -447,7 +450,7 @@ void SerialTreeLearner::ConstructHistograms(const std::vector<int8_t>& is_featur
 
 void SerialTreeLearner::FindBestThresholds() {
   std::vector<int8_t> is_feature_used(num_features_, 0);
-  #pragma omp parallel for schedule(static)
+  #pragma omp parallel for schedule(static,1024) if (num_features_ >= 2048)
   for (int feature_index = 0; feature_index < num_features_; ++feature_index) {
     if (!is_feature_used_[feature_index]) continue;
     if (parent_leaf_histogram_array_ != nullptr