Wide dataset quantile performance improvement (#5306)

src/common/hist_util.cc

@@ -286,15 +286,28 @@ void DenseCuts::Build(DMatrix* p_fmat, uint32_t max_num_bins) {
     }
   }
 
-  Init(&sketchs, max_num_bins);
+  Init(&sketchs, max_num_bins, info.num_row_);
   monitor_.Stop(__FUNCTION__);
 }
 
+/**
+ * \param [in,out]  in_sketchs
+ * \param           max_num_bins  The maximum number bins.
+ * \param           max_rows      Number of rows in this DMatrix.
+ */
 void DenseCuts::Init
-(std::vector<WQSketch>* in_sketchs, uint32_t max_num_bins) {
+(std::vector<WQSketch>* in_sketchs, uint32_t max_num_bins, size_t max_rows) {
   monitor_.Start(__func__);
   std::vector<WQSketch>& sketchs = *in_sketchs;
+
+  // Compute how many cuts samples we need at each node
+  // Do not require more than the number of total rows  in training data
+  // This allows efficient training on wide data
+  size_t global_max_rows = max_rows;
+  rabit::Allreduce<rabit::op::Sum>(&global_max_rows, 1);
   constexpr int kFactor = 8;
+  size_t intermediate_num_cuts =
+      std::min(global_max_rows, static_cast<size_t>(max_num_bins * kFactor));
   // gather the histogram data
   rabit::SerializeReducer<WQSketch::SummaryContainer> sreducer;
   std::vector<WQSketch::SummaryContainer> summary_array;
@@ -302,11 +315,11 @@ void DenseCuts::Init
   for (size_t i = 0; i < sketchs.size(); ++i) {
     WQSketch::SummaryContainer out;
     sketchs[i].GetSummary(&out);
-    summary_array[i].Reserve(max_num_bins * kFactor);
+    summary_array[i].Reserve(intermediate_num_cuts);
-    summary_array[i].SetPrune(out, max_num_bins * kFactor);
+    summary_array[i].SetPrune(out, intermediate_num_cuts);
   }
   CHECK_EQ(summary_array.size(), in_sketchs->size());
-  size_t nbytes = WQSketch::SummaryContainer::CalcMemCost(max_num_bins * kFactor);
+  size_t nbytes = WQSketch::SummaryContainer::CalcMemCost(intermediate_num_cuts);
   // TODO(chenqin): rabit failure recovery assumes no boostrap onetime call after loadcheckpoint
   // we need to move this allreduce before loadcheckpoint call in future
   sreducer.Allreduce(dmlc::BeginPtr(summary_array), nbytes, summary_array.size());

src/common/hist_util.cu

@@ -148,7 +148,7 @@ class GPUSketcher {
       this->SketchBatch(batch, info);
     }
 
-    hmat->Init(&sketch_container_->sketches_, max_bin_);
+    hmat->Init(&sketch_container_->sketches_, max_bin_, info.num_row_);
     return row_stride_;
   }
 

src/common/hist_util.h

@@ -244,7 +244,7 @@ class DenseCuts  : public CutsBuilder {
       CutsBuilder(container) {
     monitor_.Init(__FUNCTION__);
   }
-  void Init(std::vector<WQSketch>* sketchs, uint32_t max_num_bins);
+  void Init(std::vector<WQSketch>* sketchs, uint32_t max_num_bins, size_t max_rows);
   void Build(DMatrix* p_fmat, uint32_t max_num_bins) override;
 };
 

src/common/quantile.h

@@ -702,6 +702,7 @@ class QuantileSketchTemplate {
     nlevel = 1;
     while (true) {
       limit_size = static_cast<size_t>(ceil(nlevel / eps)) + 1;
+      limit_size = std::min(maxn, limit_size);
       size_t n = (1ULL << nlevel);
       if (n * limit_size >= maxn) break;
       ++nlevel;
@@ -709,7 +710,8 @@ class QuantileSketchTemplate {
     // check invariant
     size_t n = (1ULL << nlevel);
     CHECK(n * limit_size >= maxn) << "invalid init parameter";
-    CHECK(nlevel <= limit_size * eps) << "invalid init parameter";
+    CHECK(nlevel <= std::max(1, static_cast<int>(limit_size * eps)))
+        << "invalid init parameter";
   }
 
   /*!