diff --git a/src/objective/rank_objective.hpp b/src/objective/rank_objective.hpp
index 6bd5324812f8..b9c45ae62410 100644
--- a/src/objective/rank_objective.hpp
+++ b/src/objective/rank_objective.hpp
@@ -289,17 +289,12 @@ class LambdarankNDCG : public RankingObjective {
 
   void UpdatePositionBiasFactors(const score_t* lambdas, const score_t* hessians) const override {
     /// get number of threads
-    int num_threads = 1;
-    #pragma omp parallel
-    #pragma omp master
-    {
-      num_threads = omp_get_num_threads();
-    }
+    int num_threads = OMP_NUM_THREADS();
     // create per-thread buffers for first and second derivatives of utility w.r.t. position bias factors
     std::vector<double> bias_first_derivatives(num_position_ids_ * num_threads, 0.0);
     std::vector<double> bias_second_derivatives(num_position_ids_ * num_threads, 0.0);
     std::vector<int> instance_counts(num_position_ids_ * num_threads, 0);
-    #pragma omp parallel for schedule(guided)
+    #pragma omp parallel for schedule(guided) num_threads(num_threads)
     for (data_size_t i = 0; i < num_data_; i++) {
       // get thread ID
       const int tid = omp_get_thread_num();
@@ -310,7 +305,7 @@ class LambdarankNDCG : public RankingObjective {
       bias_second_derivatives[offset] -= hessians[i];
       instance_counts[offset]++;
     }
-    #pragma omp parallel for schedule(guided)
+    #pragma omp parallel for schedule(guided) num_threads(num_threads)
     for (data_size_t i = 0; i < num_position_ids_; i++) {
       double bias_first_derivative = 0.0;
       double bias_second_derivative = 0.0;
diff --git a/src/treelearner/gpu_tree_learner.cpp b/src/treelearner/gpu_tree_learner.cpp
index 294be28b6f86..4cd2c0b2731e 100644
--- a/src/treelearner/gpu_tree_learner.cpp
+++ b/src/treelearner/gpu_tree_learner.cpp
@@ -344,7 +344,7 @@ void GPUTreeLearner::AllocateGPUMemory() {
   // for data transfer time
   auto start_time = std::chrono::steady_clock::now();
   // Now generate new data structure feature4, and copy data to the device
-  int nthreads = std::min(omp_get_max_threads(), static_cast<int>(dense_feature_group_map_.size()) / dword_features_);
+  int nthreads = std::min(OMP_NUM_THREADS(), static_cast<int>(dense_feature_group_map_.size()) / dword_features_);
   nthreads = std::max(nthreads, 1);
   std::vector<Feature4*> host4_vecs(nthreads);
   std::vector<boost::compute::buffer> host4_bufs(nthreads);
diff --git a/src/treelearner/linear_tree_learner.cpp b/src/treelearner/linear_tree_learner.cpp
index 071a6405709c..0850abe8dcf1 100644
--- a/src/treelearner/linear_tree_learner.cpp
+++ b/src/treelearner/linear_tree_learner.cpp
@@ -52,7 +52,7 @@ void LinearTreeLearner::InitLinear(const Dataset* train_data, const int max_leav
   }
   XTHX_by_thread_.clear();
   XTg_by_thread_.clear();
-  int max_threads = omp_get_max_threads();
+  int max_threads = OMP_NUM_THREADS();
   for (int i = 0; i < max_threads; ++i) {
     XTHX_by_thread_.push_back(XTHX_);
     XTg_by_thread_.push_back(XTg_);