Optimizations for CPU

src/common/column_matrix.h

@@ -8,11 +8,11 @@
 #ifndef XGBOOST_COMMON_COLUMN_MATRIX_H_
 #define XGBOOST_COMMON_COLUMN_MATRIX_H_
 
+#include <dmlc/timer.h>
 #include <limits>
 #include <vector>
 #include "hist_util.h"
 
-
 namespace xgboost {
 namespace common {
 
@@ -51,6 +51,10 @@ class Column {
   }
   const size_t* GetRowData() const { return row_ind_; }
 
+  const uint32_t* GetIndex() const {
+    return index_;
+  }
+
  private:
   ColumnType type_;
   const uint32_t* index_;
@@ -80,7 +84,7 @@ class ColumnMatrix {
     std::fill(feature_counts_.begin(), feature_counts_.end(), 0);
 
     uint32_t max_val = std::numeric_limits<uint32_t>::max();
-    for (bst_uint fid = 0; fid < nfeature; ++fid) {
+    for (int32_t fid = 0; fid < nfeature; ++fid) {
       CHECK_LE(gmat.cut.row_ptr[fid + 1] - gmat.cut.row_ptr[fid], max_val);
     }
 
@@ -113,13 +117,12 @@ class ColumnMatrix {
       boundary_[fid].index_end = accum_index_;
       boundary_[fid].row_ind_end = accum_row_ind_;
     }
-
     index_.resize(boundary_[nfeature - 1].index_end);
     row_ind_.resize(boundary_[nfeature - 1].row_ind_end);
 
     // store least bin id for each feature
     index_base_.resize(nfeature);
-    for (bst_uint fid = 0; fid < nfeature; ++fid) {
+    for (int32_t fid = 0; fid < nfeature; ++fid) {
       index_base_[fid] = gmat.cut.row_ptr[fid];
     }
 

src/common/hist_util.cc

@@ -1,15 +1,15 @@
 /*!
  * Copyright 2017-2019 by Contributors
- * \file hist_util.h
+  * \file hist_util.cc
  */
+#include "./hist_util.h"
+#include <dmlc/timer.h>
 #include <rabit/rabit.h>
 #include <dmlc/omp.h>
 #include <numeric>
 #include <vector>
-
 #include "./random.h"
 #include "./column_matrix.h"
-#include "./hist_util.h"
 #include "./quantile.h"
 #include "./../tree/updater_quantile_hist.h"
 
@@ -178,7 +178,7 @@ uint32_t HistCutMatrix::GetBinIdx(const Entry& e) {
 
 void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_num_bins) {
   cut.Init(p_fmat, max_num_bins);
-  const size_t nthread = omp_get_max_threads();
+  const int32_t nthread = omp_get_max_threads();
   const uint32_t nbins = cut.row_ptr.back();
   hit_count.resize(nbins, 0);
   hit_count_tloc_.resize(nthread * nbins, 0);
@@ -260,8 +260,8 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_num_bins) {
     }
 
     #pragma omp parallel for num_threads(nthread) schedule(static)
-    for (bst_omp_uint idx = 0; idx < bst_omp_uint(nbins); ++idx) {
-      for (size_t tid = 0; tid < nthread; ++tid) {
+    for (int32_t idx = 0; idx < int32_t(nbins); ++idx) {
+      for (int32_t tid = 0; tid < nthread; ++tid) {
         hit_count[idx] += hit_count_tloc_[tid * nbins + idx];
       }
     }
@@ -411,7 +411,7 @@ FastFeatureGrouping(const GHistIndexMatrix& gmat,
         for (auto fid : group) {
           nnz += feature_nnz[fid];
         }
-        double nnz_rate = static_cast<double>(nnz) / nrow;
+        float nnz_rate = static_cast<float>(nnz) / nrow;
         // take apart small sparse group, due it will not gain on speed
         if (nnz_rate <= param.sparse_threshold) {
           for (auto fid : group) {
@@ -496,177 +496,145 @@ void GHistIndexBlockMatrix::Init(const GHistIndexMatrix& gmat,
   }
 }
 
-void GHistBuilder::BuildHist(const std::vector<GradientPair>& gpair,
-                             const RowSetCollection::Elem row_indices,
-                             const GHistIndexMatrix& gmat,
-                             GHistRow hist) {
-  const size_t nthread = static_cast<size_t>(this->nthread_);
-  data_.resize(nbins_ * nthread_);
-
-  const size_t* rid =  row_indices.begin;
-  const size_t nrows = row_indices.Size();
-  const uint32_t* index = gmat.index.data();
-  const size_t* row_ptr =  gmat.row_ptr.data();
-  const float* pgh = reinterpret_cast<const float*>(gpair.data());
-
-  double* hist_data = reinterpret_cast<double*>(hist.data());
-  double* data = reinterpret_cast<double*>(data_.data());
-
-  const size_t block_size = 512;
-  size_t n_blocks = nrows/block_size;
-  n_blocks += !!(nrows - n_blocks*block_size);
-
-  const size_t nthread_to_process = std::min(nthread,  n_blocks);
-  memset(thread_init_.data(), '\0', nthread_to_process*sizeof(size_t));
+// used when data layout is kDenseDataZeroBased or kDenseDataOneBased
+// it means that "row_ptr" is not needed for hist computations
+void BuildHistLocalDense(size_t istart, size_t iend, size_t nrows, const size_t* rid,
+    const uint32_t* index, const GradientPair::ValueT* pgh, const size_t* row_ptr,
+    GradStatHist::GradType* data_local_hist, GradStatHist* grad_stat_global) {
+  GradStatHist grad_stat;  // make local var to prevent false sharing
 
+  const size_t n_features = row_ptr[rid[istart]+1] - row_ptr[rid[istart]];
   const size_t cache_line_size = 64;
+  const size_t prefetch_step = cache_line_size / sizeof(*index);
   const size_t prefetch_offset = 10;
+
   size_t no_prefetch_size = prefetch_offset + cache_line_size/sizeof(*rid);
   no_prefetch_size = no_prefetch_size > nrows ? nrows : no_prefetch_size;
 
-#pragma omp parallel for num_threads(nthread_to_process) schedule(guided)
-  for (bst_omp_uint iblock = 0; iblock < n_blocks; iblock++) {
-    dmlc::omp_uint tid = omp_get_thread_num();
-    double* data_local_hist = ((nthread_to_process == 1) ? hist_data :
-                               reinterpret_cast<double*>(data_.data() + tid * nbins_));
+  // if read each row in some block of bin-matrix - it's dense block
+  // and we dont need SW prefetch in this case
+  const bool denseBlock = (rid[iend-1] - rid[istart]) == (iend - istart - 1);
 
-    if (!thread_init_[tid]) {
-      memset(data_local_hist, '\0', 2*nbins_*sizeof(double));
-      thread_init_[tid] = true;
-    }
-
-    const size_t istart = iblock*block_size;
-    const size_t iend = (((iblock+1)*block_size > nrows) ? nrows : istart + block_size);
+  if (iend < nrows - no_prefetch_size && !denseBlock) {
     for (size_t i = istart; i < iend; ++i) {
-      const size_t icol_start = row_ptr[rid[i]];
-      const size_t icol_end = row_ptr[rid[i]+1];
+      const size_t icol_start = rid[i] * n_features;
+      const size_t icol_start_prefetch = rid[i+prefetch_offset] * n_features;
+      const size_t idx_gh = 2*rid[i];
 
-      if (i < nrows - no_prefetch_size) {
-        PREFETCH_READ_T0(row_ptr + rid[i + prefetch_offset]);
-        PREFETCH_READ_T0(pgh + 2*rid[i + prefetch_offset]);
+      PREFETCH_READ_T0(pgh + 2*rid[i + prefetch_offset]);
+
+      for (size_t j = icol_start_prefetch; j < icol_start_prefetch + n_features;
+          j += prefetch_step) {
+        PREFETCH_READ_T0(index + j);
       }
 
-      for (size_t j = icol_start; j < icol_end; ++j) {
-        const uint32_t idx_bin = 2*index[j];
-        const size_t idx_gh = 2*rid[i];
+      grad_stat.sum_grad += pgh[idx_gh];
+      grad_stat.sum_hess += pgh[idx_gh+1];
 
+      for (size_t j = icol_start; j < icol_start + n_features; ++j) {
+        const uint32_t idx_bin = 2*index[j];
         data_local_hist[idx_bin] += pgh[idx_gh];
         data_local_hist[idx_bin+1] += pgh[idx_gh+1];
       }
     }
+  } else {
+    for (size_t i = istart; i < iend; ++i) {
+      const size_t icol_start = rid[i] * n_features;
+      const size_t idx_gh = 2*rid[i];
+      grad_stat.sum_grad += pgh[idx_gh];
+      grad_stat.sum_hess += pgh[idx_gh+1];
+
+      for (size_t j = icol_start; j < icol_start + n_features; ++j) {
+        const uint32_t idx_bin      = 2*index[j];
+        data_local_hist[idx_bin]   += pgh[idx_gh];
+        data_local_hist[idx_bin+1] += pgh[idx_gh+1];
+      }
+    }
   }
+  grad_stat_global->Add(grad_stat);
+}
 
-  if (nthread_to_process > 1) {
-    const size_t size = (2*nbins_);
-    const size_t block_size = 1024;
-    size_t n_blocks = size/block_size;
-    n_blocks += !!(size - n_blocks*block_size);
+// used when data layout is kSparseData
+// it means that "row_ptr" is needed for hist computations
+void BuildHistLocalSparse(size_t istart, size_t iend, size_t nrows, const size_t* rid,
+    const uint32_t* index, const GradientPair::ValueT* pgh, const size_t* row_ptr,
+    GradStatHist::GradType* data_local_hist, GradStatHist* grad_stat_global) {
+  GradStatHist grad_stat;  // make local var to prevent false sharing
 
-    size_t n_worked_bins = 0;
-    for (size_t i = 0; i < nthread_to_process; ++i) {
-      if (thread_init_[i]) {
-        thread_init_[n_worked_bins++] = i;
-      }
-    }
+  const size_t cache_line_size = 64;
+  const size_t prefetch_step = cache_line_size / sizeof(index[0]);
+  const size_t prefetch_offset = 10;
 
-#pragma omp parallel for num_threads(std::min(nthread, n_blocks)) schedule(guided)
-    for (bst_omp_uint iblock = 0; iblock < n_blocks; iblock++) {
-      const size_t istart = iblock * block_size;
-      const size_t iend = (((iblock + 1) * block_size > size) ? size : istart + block_size);
+  size_t no_prefetch_size = prefetch_offset + cache_line_size/sizeof(*rid);
+  no_prefetch_size = no_prefetch_size > nrows ? nrows : no_prefetch_size;
 
-      const size_t bin = 2 * thread_init_[0] * nbins_;
-      memcpy(hist_data + istart, (data + bin + istart), sizeof(double) * (iend - istart));
+  // if read each row in some block of bin-matrix - it's dense block
+  // and we dont need SW prefetch in this case
+  const bool denseBlock = (rid[iend-1] - rid[istart]) == (iend - istart);
 
-      for (size_t i_bin_part = 1; i_bin_part < n_worked_bins; ++i_bin_part) {
-        const size_t bin = 2 * thread_init_[i_bin_part] * nbins_;
-        for (size_t i = istart; i < iend; i++) {
-          hist_data[i] += data[bin + i];
-        }
-      }
-    }
-  }
-}
+  if (iend < nrows - no_prefetch_size && !denseBlock) {
+    for (size_t i = istart; i < iend; ++i) {
+      const size_t icol_start = row_ptr[rid[i]];
+      const size_t icol_end = row_ptr[rid[i]+1];
+      const size_t idx_gh = 2*rid[i];
 
-void GHistBuilder::BuildBlockHist(const std::vector<GradientPair>& gpair,
-                                  const RowSetCollection::Elem row_indices,
-                                  const GHistIndexBlockMatrix& gmatb,
-                                  GHistRow hist) {
-  constexpr int kUnroll = 8;  // loop unrolling factor
-  const size_t nblock = gmatb.GetNumBlock();
-  const size_t nrows = row_indices.end - row_indices.begin;
-  const size_t rest = nrows % kUnroll;
-
-#if defined(_OPENMP)
-  const auto nthread = static_cast<bst_omp_uint>(this->nthread_);  // NOLINT
-#endif  // defined(_OPENMP)
-  tree::GradStats* p_hist = hist.data();
-
-#pragma omp parallel for num_threads(nthread) schedule(guided)
-  for (bst_omp_uint bid = 0; bid < nblock; ++bid) {
-    auto gmat = gmatb[bid];
-
-    for (size_t i = 0; i < nrows - rest; i += kUnroll) {
-      size_t rid[kUnroll];
-      size_t ibegin[kUnroll];
-      size_t iend[kUnroll];
-      GradientPair stat[kUnroll];
-
-      for (int k = 0; k < kUnroll; ++k) {
-        rid[k] = row_indices.begin[i + k];
-        ibegin[k] = gmat.row_ptr[rid[k]];
-        iend[k] = gmat.row_ptr[rid[k] + 1];
-        stat[k] = gpair[rid[k]];
+      const size_t icol_start10 = row_ptr[rid[i+prefetch_offset]];
+      const size_t icol_end10 = row_ptr[rid[i+prefetch_offset]+1];
+
+      PREFETCH_READ_T0(pgh + 2*rid[i + prefetch_offset]);
+
+      for (size_t j = icol_start10; j < icol_end10; j+=prefetch_step) {
+        PREFETCH_READ_T0(index + j);
       }
-      for (int k = 0; k < kUnroll; ++k) {
-        for (size_t j = ibegin[k]; j < iend[k]; ++j) {
-          const uint32_t bin = gmat.index[j];
-          p_hist[bin].Add(stat[k]);
-        }
+
+      grad_stat.sum_grad += pgh[idx_gh];
+      grad_stat.sum_hess += pgh[idx_gh+1];
+
+      for (size_t j = icol_start; j < icol_end; ++j) {
+        const uint32_t idx_bin      = 2*index[j];
+        data_local_hist[idx_bin]   += pgh[idx_gh];
+        data_local_hist[idx_bin+1] += pgh[idx_gh+1];
       }
     }
-    for (size_t i = nrows - rest; i < nrows; ++i) {
-      const size_t rid = row_indices.begin[i];
-      const size_t ibegin = gmat.row_ptr[rid];
-      const size_t iend = gmat.row_ptr[rid + 1];
-      const GradientPair stat = gpair[rid];
-      for (size_t j = ibegin; j < iend; ++j) {
-        const uint32_t bin = gmat.index[j];
-        p_hist[bin].Add(stat);
+  } else {
+    for (size_t i = istart; i < iend; ++i) {
+      const size_t icol_start = row_ptr[rid[i]];
+      const size_t icol_end = row_ptr[rid[i]+1];
+      const size_t idx_gh = 2*rid[i];
+
+      grad_stat.sum_grad += pgh[idx_gh];
+      grad_stat.sum_hess += pgh[idx_gh+1];
+
+      for (size_t j = icol_start; j < icol_end; ++j) {
+        const uint32_t idx_bin      = 2*index[j];
+        data_local_hist[idx_bin]   += pgh[idx_gh];
+        data_local_hist[idx_bin+1] += pgh[idx_gh+1];
       }
     }
   }
+  grad_stat_global->Add(grad_stat);
 }
 
-void GHistBuilder::SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent) {
-  const uint32_t nbins = static_cast<bst_omp_uint>(nbins_);
-  constexpr int kUnroll = 8;  // loop unrolling factor
-  const uint32_t rest = nbins % kUnroll;
-
-#if defined(_OPENMP)
-  const auto nthread = static_cast<bst_omp_uint>(this->nthread_);  // NOLINT
-#endif  // defined(_OPENMP)
-  tree::GradStats* p_self = self.data();
-  tree::GradStats* p_sibling = sibling.data();
-  tree::GradStats* p_parent = parent.data();
-
-#pragma omp parallel for num_threads(nthread) schedule(static)
-  for (bst_omp_uint bin_id = 0;
-       bin_id < static_cast<bst_omp_uint>(nbins - rest); bin_id += kUnroll) {
-    tree::GradStats pb[kUnroll];
-    tree::GradStats sb[kUnroll];
-    for (int k = 0; k < kUnroll; ++k) {
-      pb[k] = p_parent[bin_id + k];
-    }
-    for (int k = 0; k < kUnroll; ++k) {
-      sb[k] = p_sibling[bin_id + k];
-    }
-    for (int k = 0; k < kUnroll; ++k) {
-      p_self[bin_id + k].SetSubstract(pb[k], sb[k]);
+void SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent) {
+  GradStatHist* p_self = self.data();
+  GradStatHist* p_sibling = sibling.data();
+  GradStatHist* p_parent = parent.data();
+
+  const size_t size = self.size();
+  CHECK_EQ(sibling.size(), size);
+  CHECK_EQ(parent.size(), size);
+
+  const size_t block_size = 1024;  // aproximatly 1024 values per block
+  size_t n_blocks = size/block_size + !!(size%block_size);
+
+  #pragma omp parallel for
+  for (int iblock = 0; iblock < n_blocks; ++iblock) {
+    const size_t ibegin = iblock*block_size;
+    const size_t iend = (((iblock+1)*block_size > size) ? size : ibegin + block_size);
+    for (bst_omp_uint bin_id = ibegin; bin_id < iend; bin_id++) {
+      p_self[bin_id].SetSubstract(p_parent[bin_id], p_sibling[bin_id]);
     }
   }
-  for (uint32_t bin_id = nbins - rest; bin_id < nbins; ++bin_id) {
-    p_self[bin_id].SetSubstract(p_parent[bin_id], p_sibling[bin_id]);
-  }
 }
 
 }  // namespace common