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Remove omp_get_max_threads in tree updaters. (#7590)

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This commit is contained in:
Jiaming Yuan
2022-01-26 19:55:47 +08:00
committed by GitHub
parent 24789429fd
commit 5d7818e75d
19 changed files with 142 additions and 199 deletions
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18
src/common/column_matrix.h
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@@ -146,8 +146,7 @@ class ColumnMatrix {
}
// construct column matrix from GHistIndexMatrix
inline void Init(const GHistIndexMatrix& gmat,
double sparse_threshold) {
inline void Init(const GHistIndexMatrix& gmat, double sparse_threshold, int32_t n_threads) {
const int32_t nfeature = static_cast<int32_t>(gmat.cut.Ptrs().size() - 1);
const size_t nrow = gmat.row_ptr.size() - 1;
// identify type of each column
@@ -208,12 +207,15 @@ class ColumnMatrix {
if (all_dense) {
BinTypeSize gmat_bin_size = gmat.index.GetBinTypeSize();
if (gmat_bin_size == kUint8BinsTypeSize) {
SetIndexAllDense(gmat.index.data<uint8_t>(), gmat, nrow, nfeature, noMissingValues);
SetIndexAllDense(gmat.index.data<uint8_t>(), gmat, nrow, nfeature, noMissingValues,
n_threads);
} else if (gmat_bin_size == kUint16BinsTypeSize) {
SetIndexAllDense(gmat.index.data<uint16_t>(), gmat, nrow, nfeature, noMissingValues);
SetIndexAllDense(gmat.index.data<uint16_t>(), gmat, nrow, nfeature, noMissingValues,
n_threads);
} else {
CHECK_EQ(gmat_bin_size, kUint32BinsTypeSize);
SetIndexAllDense(gmat.index.data<uint32_t>(), gmat, nrow, nfeature, noMissingValues);
CHECK_EQ(gmat_bin_size, kUint32BinsTypeSize);
SetIndexAllDense(gmat.index.data<uint32_t>(), gmat, nrow, nfeature, noMissingValues,
n_threads);
}
/* For sparse DMatrix gmat.index.getBinTypeSize() returns always kUint32BinsTypeSize
but for ColumnMatrix we still have a chance to reduce the memory consumption */
@@ -266,13 +268,13 @@ class ColumnMatrix {
template <typename T>
inline void SetIndexAllDense(T *index, const GHistIndexMatrix &gmat,
const size_t nrow, const size_t nfeature,
const bool noMissingValues) {
const bool noMissingValues, int32_t n_threads) {
T* local_index = reinterpret_cast<T*>(&index_[0]);
/* missing values make sense only for column with type kDenseColumn,
and if no missing values were observed it could be handled much faster. */
if (noMissingValues) {
ParallelFor(omp_ulong(nrow), [&](omp_ulong rid) {
ParallelFor(nrow, n_threads, [&](auto rid) {
const size_t ibegin = rid*nfeature;
const size_t iend = (rid+1)*nfeature;
size_t j = 0;