change omp loop var to bst_omp_uint, add XGB_DLL to wrapper

R-package/src/xgboost_R.cpp

@@ -3,10 +3,10 @@
 #include <utility>
 #include <cstring>
 #include "xgboost_R.h"
-#include "../../wrapper/xgboost_wrapper.h"
+#include "xgboost_wrapper.h"
-#include "../../src/utils/utils.h"
+#include "../src/utils/utils.h"
-#include "../../src/utils/omp.h"
+#include "../src/utils/omp.h"
-#include "../../src/utils/matrix_csr.h"
+#include "../src/utils/matrix_csr.h"
 
 using namespace xgboost;
 // implements error handling
@@ -119,7 +119,7 @@ extern "C" {
     }
   }
   SEXP XGDMatrixGetInfo_R(SEXP handle, SEXP field) {
-    size_t olen;
+    uint64_t olen;
     const float *res = XGDMatrixGetFloatInfo(R_ExternalPtrAddr(handle),
                                              CHAR(asChar(field)), &olen);
     SEXP ret = PROTECT(allocVector(REALSXP, olen));
@@ -188,7 +188,7 @@ extern "C" {
                                          &vec_dmats[0], &vec_sptr[0], len));
   }
   SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP output_margin) {
-    size_t olen;
+    uint64_t olen;
     const float *res = XGBoosterPredict(R_ExternalPtrAddr(handle),
                                         R_ExternalPtrAddr(dmat),
                                         asInteger(output_margin),
@@ -207,13 +207,13 @@ extern "C" {
     XGBoosterSaveModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname)));
   }
   void XGBoosterDumpModel_R(SEXP handle, SEXP fname, SEXP fmap) {
-    size_t olen;
+    uint64_t olen;
     const char **res = XGBoosterDumpModel(R_ExternalPtrAddr(handle),
                                           CHAR(asChar(fmap)),
                                           &olen);
     FILE *fo = utils::FopenCheck(CHAR(asChar(fname)), "w");
     for (size_t i = 0; i < olen; ++i) {
-      fprintf(fo, "booster[%u]:\n", static_cast<unsigned>(i));
+      fprintf(fo, "booster[%lu]:\n", i);
       fprintf(fo, "%s", res[i]);
     }
     fclose(fo);

src/data.h

@@ -12,6 +12,7 @@
 #include <cstring>
 #include <algorithm>
 #include "utils/io.h"
+#include "utils/omp.h"
 #include "utils/utils.h"
 #include "utils/iterator.h"
 #include "utils/random.h"
@@ -370,9 +371,9 @@ class FMatrixS : public FMatrixInterface<FMatrixS>{
     }
 
     // sort columns
-    unsigned ncol = static_cast<unsigned>(this->NumCol());
+    bst_omp_uint ncol = static_cast<bst_omp_uint>(this->NumCol());
     #pragma omp parallel for schedule(static)
-    for (unsigned i = 0; i < ncol; ++i) {
+    for (bst_omp_uint i = 0; i < ncol; ++i) {
       std::sort(&col_data_[0] + col_ptr_[i],
                 &col_data_[0] + col_ptr_[i + 1], Entry::CmpValue);
     }

src/gbm/gblinear-inl.hpp

@@ -51,9 +51,9 @@ class GBLinear : public IGradBooster<FMatrix> {
     // for all the output group
     for (int gid = 0; gid < ngroup; ++gid) {
       double sum_grad = 0.0, sum_hess = 0.0;
-      const unsigned ndata = static_cast<unsigned>(rowset.size());
+      const bst_omp_uint ndata = static_cast<bst_omp_uint>(rowset.size());
       #pragma omp parallel for schedule(static) reduction(+: sum_grad, sum_hess)
-      for (unsigned i = 0; i < ndata; ++i) {
+      for (bst_omp_uint i = 0; i < ndata; ++i) {
         bst_gpair &p = gpair[rowset[i] * ngroup + gid];
         if (p.hess >= 0.0f) {
           sum_grad += p.grad; sum_hess += p.hess;
@@ -65,7 +65,7 @@ class GBLinear : public IGradBooster<FMatrix> {
       model.bias()[gid] += dw;
       // update grad value
       #pragma omp parallel for schedule(static)
-      for (unsigned i = 0; i < ndata; ++i) {
+      for (bst_omp_uint i = 0; i < ndata; ++i) {
         bst_gpair &p = gpair[rowset[i] * ngroup + gid];
         if (p.hess >= 0.0f) {
           p.grad += p.hess * dw;
@@ -73,9 +73,9 @@ class GBLinear : public IGradBooster<FMatrix> {
       }
     }
     // number of features
-    const unsigned nfeat = static_cast<unsigned>(feat_index.size());
+    const bst_omp_uint nfeat = static_cast<bst_omp_uint>(feat_index.size());
     #pragma omp parallel for schedule(static)
-    for (unsigned i = 0; i < nfeat; ++i) {
+    for (bst_omp_uint i = 0; i < nfeat; ++i) {
       const bst_uint fid = feat_index[i];
       for (int gid = 0; gid < ngroup; ++gid) {
         double sum_grad = 0.0, sum_hess = 0.0;
@@ -117,9 +117,9 @@ class GBLinear : public IGradBooster<FMatrix> {
       // k is number of group
       preds.resize(preds.size() + batch.size * ngroup);
       // parallel over local batch
-      const unsigned nsize = static_cast<unsigned>(batch.size);
+      const bst_omp_uint nsize = static_cast<bst_omp_uint>(batch.size);
       #pragma omp parallel for schedule(static)
-      for (unsigned i = 0; i < nsize; ++i) {
+      for (bst_omp_uint i = 0; i < nsize; ++i) {
         const size_t ridx = batch.base_rowid + i;
         // loop over output groups
         for (int gid = 0; gid < ngroup; ++gid) {

src/gbm/gbtree-inl.hpp

@@ -94,8 +94,9 @@ class GBTree : public IGradBooster<FMatrix> {
                    "must have exactly ngroup*nrow gpairs");
       std::vector<bst_gpair> tmp(gpair.size()/ngroup);
       for (int gid = 0; gid < ngroup; ++gid) {
+        bst_omp_uint nsize = static_cast<bst_omp_uint>(tmp.size());
         #pragma omp parallel for schedule(static)
-        for (size_t i = 0; i < tmp.size(); ++i) {
+        for (bst_omp_uint i = 0; i < nsize; ++i) {
           tmp[i] = gpair[i * ngroup + gid];
         }
         this->BoostNewTrees(tmp, fmat, info, gid);
@@ -129,9 +130,9 @@ class GBTree : public IGradBooster<FMatrix> {
       // k is number of group
       preds.resize(preds.size() + batch.size * mparam.num_output_group);
       // parallel over local batch
-      const unsigned nsize = static_cast<unsigned>(batch.size);
+      const bst_omp_uint nsize = static_cast<bst_omp_uint>(batch.size);
       #pragma omp parallel for schedule(static)
-      for (unsigned i = 0; i < nsize; ++i) {
+      for (bst_omp_uint i = 0; i < nsize; ++i) {
         const int tid = omp_get_thread_num();
         tree::RegTree::FVec &feats = thread_temp[tid];
         int64_t ridx = static_cast<int64_t>(batch.base_rowid + i);

src/learner/evaluation-inl.hpp

@@ -26,10 +26,10 @@ struct EvalEWiseBase : public IEvaluator {
                      const MetaInfo &info) const {
     utils::Check(preds.size() == info.labels.size(),
                  "label and prediction size not match");
-    const unsigned ndata = static_cast<unsigned>(preds.size());
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
     float sum = 0.0, wsum = 0.0;
     #pragma omp parallel for reduction(+: sum, wsum) schedule(static)
-    for (unsigned i = 0; i < ndata; ++i) {
+    for (bst_omp_uint i = 0; i < ndata; ++i) {
       const float wt = info.GetWeight(i);
       sum += Derived::EvalRow(info.labels[i], preds[i]) * wt;
       wsum += wt;
@@ -109,12 +109,12 @@ struct EvalAMS : public IEvaluator {
   }
   virtual float Eval(const std::vector<float> &preds,
                      const MetaInfo &info) const {
-    const unsigned ndata = static_cast<unsigned>(preds.size());
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
     utils::Check(info.weights.size() == ndata, "we need weight to evaluate ams");
     std::vector< std::pair<float, unsigned> > rec(ndata);
 
     #pragma omp parallel for schedule(static)
-    for (unsigned i = 0; i < ndata; ++i) {
+    for (bst_omp_uint i = 0; i < ndata; ++i) {
       rec[i] = std::make_pair(preds[i], i);
     }
     std::sort(rec.begin(), rec.end(), CmpFirst);
@@ -211,7 +211,7 @@ struct EvalAuc : public IEvaluator {
     const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
     utils::Check(gptr.back() == preds.size(),
                  "EvalAuc: group structure must match number of prediction");
-    const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
+    const bst_omp_uint ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
     // sum statictis
     double sum_auc = 0.0f;
     #pragma omp parallel reduction(+:sum_auc)
@@ -219,7 +219,7 @@ struct EvalAuc : public IEvaluator {
       // each thread takes a local rec
       std::vector< std::pair<float, unsigned> > rec;
       #pragma omp for schedule(static)
-      for (unsigned k = 0; k < ngroup; ++k) {
+      for (bst_omp_uint k = 0; k < ngroup; ++k) {
         rec.clear();
         for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
           rec.push_back(std::make_pair(preds[j], j));
@@ -269,7 +269,7 @@ struct EvalRankList : public IEvaluator {
     utils::Assert(gptr.size() != 0, "must specify group when constructing rank file");
     utils::Assert(gptr.back() == preds.size(),
                    "EvalRanklist: group structure must match number of prediction");
-    const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
+    const bst_omp_uint ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
     // sum statistics
     double sum_metric = 0.0f;
     #pragma omp parallel reduction(+:sum_metric)
@@ -277,7 +277,7 @@ struct EvalRankList : public IEvaluator {
       // each thread takes a local rec
       std::vector< std::pair<float, unsigned> > rec;
       #pragma omp for schedule(static)
-      for (unsigned k = 0; k < ngroup; ++k) {
+      for (bst_omp_uint k = 0; k < ngroup; ++k) {
         rec.clear();
         for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
           rec.push_back(std::make_pair(preds[j], static_cast<int>(info.labels[j])));

src/learner/learner-inl.hpp

@@ -253,17 +253,17 @@ class BoostLearner {
                   data.info.info, out_preds);
     // add base margin
     std::vector<float> &preds = *out_preds;
-    const unsigned ndata = static_cast<unsigned>(preds.size());
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
     if (data.info.base_margin.size() != 0) {
       utils::Check(preds.size() == data.info.base_margin.size(),
                    "base_margin.size does not match with prediction size");
       #pragma omp parallel for schedule(static)
-      for (unsigned j = 0; j < ndata; ++j) {
+      for (bst_omp_uint j = 0; j < ndata; ++j) {
         preds[j] += data.info.base_margin[j];
       }
     } else {
       #pragma omp parallel for schedule(static)
-      for (unsigned j = 0; j < ndata; ++j) {
+      for (bst_omp_uint j = 0; j < ndata; ++j) {
         preds[j] += mparam.base_score;
       }
     }

src/learner/objective-inl.hpp

@@ -116,9 +116,9 @@ class RegLossObj : public IObjFunction{
     gpair.resize(preds.size());
     // start calculating gradient
     const unsigned nstep = static_cast<unsigned>(info.labels.size());
-    const unsigned ndata = static_cast<unsigned>(preds.size());
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
     #pragma omp parallel for schedule(static)
-    for (unsigned i = 0; i < ndata; ++i) {
+    for (bst_omp_uint i = 0; i < ndata; ++i) {
       const unsigned j = i % nstep;
       float p = loss.PredTransform(preds[i]);
       float w = info.GetWeight(j);
@@ -132,9 +132,9 @@ class RegLossObj : public IObjFunction{
   }
   virtual void PredTransform(std::vector<float> *io_preds) {
     std::vector<float> &preds = *io_preds;
-    const unsigned ndata = static_cast<unsigned>(preds.size());
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
     #pragma omp parallel for schedule(static)
-    for (unsigned j = 0; j < ndata; ++j) {
+    for (bst_omp_uint j = 0; j < ndata; ++j) {
       preds[j] = loss.PredTransform(preds[j]);
     }
   }
@@ -169,12 +169,12 @@ class SoftmaxMultiClassObj : public IObjFunction {
     std::vector<bst_gpair> &gpair = *out_gpair;
     gpair.resize(preds.size());
     const unsigned nstep = static_cast<unsigned>(info.labels.size() * nclass);
-    const unsigned ndata = static_cast<unsigned>(preds.size() / nclass);
+    const unsigned ndata = static_cast<bst_omp_uint>(preds.size() / nclass);
     #pragma omp parallel
     {
       std::vector<float> rec(nclass);
       #pragma omp for schedule(static)
-      for (unsigned i = 0; i < ndata; ++i) {
+      for (bst_omp_uint i = 0; i < ndata; ++i) {
         for (int k = 0; k < nclass; ++k) {
           rec[k] = preds[i * nclass + k];
         }
@@ -210,13 +210,13 @@ class SoftmaxMultiClassObj : public IObjFunction {
     utils::Check(nclass != 0, "must set num_class to use softmax");
     std::vector<float> &preds = *io_preds;
     std::vector<float> tmp;
-    const unsigned ndata = static_cast<unsigned>(preds.size()/nclass);
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size()/nclass);
     if (prob == 0) tmp.resize(ndata);
     #pragma omp parallel
     {
       std::vector<float> rec(nclass);
       #pragma omp for schedule(static)
-      for (unsigned j = 0; j < ndata; ++j) {
+      for (bst_omp_uint j = 0; j < ndata; ++j) {
         for (int k = 0; k < nclass; ++k) {
           rec[k] = preds[j * nclass + k];
         }
@@ -263,7 +263,7 @@ class LambdaRankObj : public IObjFunction {
     const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
     utils::Check(gptr.size() != 0 && gptr.back() == info.labels.size(),
                  "group structure not consistent with #rows");
-    const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
+    const bst_omp_uint ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
     #pragma omp parallel
     {
       // parall construct, declare random number generator here, so that each
@@ -273,7 +273,7 @@ class LambdaRankObj : public IObjFunction {
       std::vector<ListEntry>  lst;
       std::vector< std::pair<float, unsigned> > rec;
       #pragma omp for schedule(static)
-      for (unsigned k = 0; k < ngroup; ++k) {
+      for (bst_omp_uint k = 0; k < ngroup; ++k) {
         lst.clear(); pairs.clear();
         for (unsigned j = gptr[k]; j < gptr[k+1]; ++j) {
           lst.push_back(ListEntry(preds[j], info.labels[j], j));

src/tree/updater_colmaker-inl.hpp

@@ -186,9 +186,9 @@ class ColMaker: public IUpdater<FMatrix> {
       }
       const std::vector<bst_uint> &rowset = fmat.buffered_rowset();
       // setup position
-      const unsigned ndata = static_cast<unsigned>(rowset.size());
+      const bst_omp_uint ndata = static_cast<bst_omp_uint>(rowset.size());
       #pragma omp parallel for schedule(static)
-      for (unsigned i = 0; i < ndata; ++i) {
+      for (bst_omp_uint i = 0; i < ndata; ++i) {
         const bst_uint ridx = rowset[i];
         const int tid = omp_get_thread_num();
         if (position[ridx] < 0) continue;
@@ -286,12 +286,12 @@ class ColMaker: public IUpdater<FMatrix> {
         feat_set.resize(n);
       }
       // start enumeration
-      const unsigned nsize = static_cast<unsigned>(feat_set.size());
+      const bst_omp_uint nsize = static_cast<bst_omp_uint>(feat_set.size());
       #if defined(_OPENMP)
       const int batch_size = std::max(static_cast<int>(nsize / this->nthread / 32), 1);
       #endif
       #pragma omp parallel for schedule(dynamic, batch_size)
-      for (unsigned i = 0; i < nsize; ++i) {
+      for (bst_omp_uint i = 0; i < nsize; ++i) {
         const unsigned fid = feat_set[i];
         const int tid = omp_get_thread_num();
         if (param.need_forward_search(fmat.GetColDensity(fid))) {
@@ -321,9 +321,9 @@ class ColMaker: public IUpdater<FMatrix> {
     inline void ResetPosition(const std::vector<int> &qexpand, const FMatrix &fmat, const RegTree &tree) {
       const std::vector<bst_uint> &rowset = fmat.buffered_rowset();
       // step 1, set default direct nodes to default, and leaf nodes to -1
-      const unsigned ndata = static_cast<unsigned>(rowset.size());
+      const bst_omp_uint ndata = static_cast<bst_omp_uint>(rowset.size());
       #pragma omp parallel for schedule(static)
-      for (unsigned i = 0; i < ndata; ++i) {
+      for (bst_omp_uint i = 0; i < ndata; ++i) {
         const bst_uint ridx = rowset[i];
         const int nid = position[ridx];
         if (nid >= 0) {
@@ -344,9 +344,9 @@ class ColMaker: public IUpdater<FMatrix> {
       std::sort(fsplits.begin(), fsplits.end());
       fsplits.resize(std::unique(fsplits.begin(), fsplits.end()) - fsplits.begin());
       // start put things into right place
-      const unsigned nfeats = static_cast<unsigned>(fsplits.size());
+      const bst_omp_uint nfeats = static_cast<bst_omp_uint>(fsplits.size());
       #pragma omp parallel for schedule(dynamic, 1)
-      for (unsigned i = 0; i < nfeats; ++i) {
+      for (bst_omp_uint i = 0; i < nfeats; ++i) {
         const unsigned fid = fsplits[i];
         for (typename FMatrix::ColIter it = fmat.GetSortedCol(fid); it.Next();) {
           const bst_uint ridx = it.rindex();

src/tree/updater_refresh-inl.hpp

@@ -56,9 +56,9 @@ class TreeRefresher: public IUpdater<FMatrix> {
       const SparseBatch &batch = iter->Value();
       utils::Check(batch.size < std::numeric_limits<unsigned>::max(),
                    "too large batch size ");
-      const unsigned nbatch = static_cast<unsigned>(batch.size);
+      const bst_omp_uint nbatch = static_cast<bst_omp_uint>(batch.size);
       #pragma omp parallel for schedule(static)
-      for (unsigned i = 0; i < nbatch; ++i) {
+      for (bst_omp_uint i = 0; i < nbatch; ++i) {
         SparseBatch::Inst inst = batch[i];
         const int tid = omp_get_thread_num();
         const bst_uint ridx = static_cast<bst_uint>(batch.base_rowid + i);

src/utils/omp.h

@@ -7,6 +7,15 @@
  */
 #if defined(_OPENMP)
 #include <omp.h>
+namespace xgboost {
+// loop variable used in openmp
+#ifdef _MSC_VER
+typedef int bst_omp_uint;
+#else
+typedef unsigned bst_omp_uint;
+#endif
+} // namespace xgboost
+
 #else
 #ifndef DISABLE_OPENMP
 #ifndef _MSC_VER

wrapper/xgboost_R.cpp

@@ -213,7 +213,7 @@ extern "C" {
                                           &olen);
     FILE *fo = utils::FopenCheck(CHAR(asChar(fname)), "w");
     for (size_t i = 0; i < olen; ++i) {
-      fprintf(fo, "booster[%lu]:\n", i);
+      fprintf(fo, "booster[%u]:\n", static_cast<unsigned>(i));
       fprintf(fo, "%s", res[i]);
     }
     fclose(fo);

wrapper/xgboost_wrapper.cpp

@@ -32,9 +32,9 @@ class Booster: public learner::BoostLearner<FMatrixS> {
   inline void BoostOneIter(const DataMatrix &train,
                            float *grad, float *hess, uint64_t len) {
     this->gpair_.resize(len);
-    const unsigned ndata = static_cast<unsigned>(len);
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(len);
     #pragma omp parallel for schedule(static)
-    for (unsigned j = 0; j < ndata; ++j) {
+    for (bst_omp_uint j = 0; j < ndata; ++j) {
       gpair_[j] = bst_gpair(grad[j], hess[j]);
     }
     gbm_->DoBoost(train.fmat, train.info.info, &gpair_);

wrapper/xgboost_wrapper.h

@@ -9,13 +9,14 @@
 #include <cstdio>
 // define uint64_t to be unsigned long
 typedef unsigned long uint64_t;
+#define XGB_DLL
 
 extern "C" {
   /*!
    * \brief load a data matrix 
    * \return a loaded data matrix
    */
-  void* XGDMatrixCreateFromFile(const char *fname, int silent);
+  XGB_DLL void* XGDMatrixCreateFromFile(const char *fname, int silent);
   /*! 
    * \brief create a matrix content from csr format
    * \param indptr pointer to row headers
@@ -25,11 +26,11 @@ extern "C" {
    * \param nelem number of nonzero elements in the matrix
    * \return created dmatrix
    */
-  void* XGDMatrixCreateFromCSR(const uint64_t *indptr,
+  XGB_DLL void* XGDMatrixCreateFromCSR(const uint64_t *indptr,
-                               const unsigned *indices,
+                                       const unsigned *indices,
-                               const float *data,
+                                       const float *data,
-                               uint64_t nindptr,
+                                       uint64_t nindptr,
-                               uint64_t nelem);
+                                       uint64_t nelem);
   /*!
    * \brief create matrix content from dense matrix
    * \param data pointer to the data space
@@ -38,10 +39,10 @@ extern "C" {
    * \param missing which value to represent missing value
    * \return created dmatrix
    */
-  void* XGDMatrixCreateFromMat(const float *data,
+  XGB_DLL void* XGDMatrixCreateFromMat(const float *data,
-                               uint64_t nrow,
+                                       uint64_t nrow,
-                               uint64_t ncol,
+                                       uint64_t ncol,
-                               float  missing);
+                                       float  missing);
   /*!
    * \brief create a new dmatrix from sliced content of existing matrix
    * \param handle instance of data matrix to be sliced
@@ -49,20 +50,20 @@ extern "C" {
    * \param len length of index set
    * \return a sliced new matrix
    */
-  void* XGDMatrixSliceDMatrix(void *handle,
+  XGB_DLL void* XGDMatrixSliceDMatrix(void *handle,
-                              const int *idxset,
+                                      const int *idxset,
-                              uint64_t len);
+                                      uint64_t len);
   /*!
    * \brief free space in data matrix
    */
-  void XGDMatrixFree(void *handle);
+  XGB_DLL void XGDMatrixFree(void *handle);
   /*!
    * \brief load a data matrix into binary file
    * \param handle a instance of data matrix
    * \param fname file name
    * \param silent print statistics when saving
    */
-  void XGDMatrixSaveBinary(void *handle, const char *fname, int silent);
+  XGB_DLL void XGDMatrixSaveBinary(void *handle, const char *fname, int silent);
   /*!
    * \brief set float vector to a content in info
    * \param handle a instance of data matrix
@@ -70,7 +71,7 @@ extern "C" {
    * \param array pointer to float vector
    * \param len length of array
    */
-  void XGDMatrixSetFloatInfo(void *handle, const char *field, const float *array, uint64_t len);
+  XGB_DLL void XGDMatrixSetFloatInfo(void *handle, const char *field, const float *array, uint64_t len);
   /*!
    * \brief set uint32 vector to a content in info
    * \param handle a instance of data matrix
@@ -78,14 +79,14 @@ extern "C" {
    * \param array pointer to float vector
    * \param len length of array
    */
-  void XGDMatrixSetUIntInfo(void *handle, const char *field, const unsigned *array, uint64_t len);
+  XGB_DLL void XGDMatrixSetUIntInfo(void *handle, const char *field, const unsigned *array, uint64_t len);
   /*!
    * \brief set label of the training matrix
    * \param handle a instance of data matrix
    * \param group pointer to group size
    * \param len length of array
    */
-  void XGDMatrixSetGroup(void *handle, const unsigned *group, uint64_t len);
+  XGB_DLL void XGDMatrixSetGroup(void *handle, const unsigned *group, uint64_t len);
   /*!
    * \brief get float info vector from matrix
    * \param handle a instance of data matrix
@@ -93,7 +94,7 @@ extern "C" {
    * \param out_len used to set result length
    * \return pointer to the result
    */
-  const float* XGDMatrixGetFloatInfo(const void *handle, const char *field, uint64_t* out_len);
+  XGB_DLL const float* XGDMatrixGetFloatInfo(const void *handle, const char *field, uint64_t* out_len);
   /*!
    * \brief get uint32 info vector from matrix
    * \param handle a instance of data matrix
@@ -101,37 +102,37 @@ extern "C" {
    * \param out_len used to set result length
    * \return pointer to the result
    */
-  const unsigned* XGDMatrixGetUIntInfo(const void *handle, const char *field, uint64_t* out_len);
+  XGB_DLL const unsigned* XGDMatrixGetUIntInfo(const void *handle, const char *field, uint64_t* out_len);
   /*!
    * \brief return number of rows
    */
-  uint64_t XGDMatrixNumRow(const void *handle);
+  XGB_DLL uint64_t XGDMatrixNumRow(const void *handle);
   // --- start XGBoost class
   /*! 
    * \brief create xgboost learner 
    * \param dmats matrices that are set to be cached
    * \param len length of dmats
    */
-  void *XGBoosterCreate(void* dmats[], uint64_t len);
+  XGB_DLL void *XGBoosterCreate(void* dmats[], uint64_t len);
   /*! 
    * \brief free obj in handle 
    * \param handle handle to be freed
    */
-  void XGBoosterFree(void* handle);
+  XGB_DLL void XGBoosterFree(void* handle);
   /*! 
    * \brief set parameters 
    * \param handle handle
    * \param name  parameter name
    * \param val value of parameter
    */    
-  void XGBoosterSetParam(void *handle, const char *name, const char *value);
+  XGB_DLL void XGBoosterSetParam(void *handle, const char *name, const char *value);
   /*! 
    * \brief update the model in one round using dtrain
    * \param handle handle
    * \param iter current iteration rounds
    * \param dtrain training data
    */
-  void XGBoosterUpdateOneIter(void *handle, int iter, void *dtrain);
+  XGB_DLL void XGBoosterUpdateOneIter(void *handle, int iter, void *dtrain);
   /*!
    * \brief update the model, by directly specify gradient and second order gradient,
    *        this can be used to replace UpdateOneIter, to support customized loss function
@@ -141,8 +142,8 @@ extern "C" {
    * \param hess second order gradient statistics
    * \param len length of grad/hess array
    */
-  void XGBoosterBoostOneIter(void *handle, void *dtrain,
+  XGB_DLL void XGBoosterBoostOneIter(void *handle, void *dtrain,
-                             float *grad, float *hess, uint64_t len);
+                                     float *grad, float *hess, uint64_t len);
   /*!
    * \brief get evaluation statistics for xgboost
    * \param handle handle
@@ -152,8 +153,8 @@ extern "C" {
    * \param len length of dmats
    * \return the string containing evaluation stati
    */
-  const char *XGBoosterEvalOneIter(void *handle, int iter, void *dmats[],
+  XGB_DLL const char *XGBoosterEvalOneIter(void *handle, int iter, void *dmats[],
-                                   const char *evnames[], uint64_t len);
+                                           const char *evnames[], uint64_t len);
   /*!
    * \brief make prediction based on dmat
    * \param handle handle
@@ -161,19 +162,19 @@ extern "C" {
    * \param output_margin whether only output raw margin value
    * \param len used to store length of returning result
    */
-  const float *XGBoosterPredict(void *handle, void *dmat, int output_margin, uint64_t *len);
+  XGB_DLL const float *XGBoosterPredict(void *handle, void *dmat, int output_margin, uint64_t *len);
   /*!
    * \brief load model from existing file
    * \param handle handle
    * \param fname file name
    */
-  void XGBoosterLoadModel(void *handle, const char *fname);
+  XGB_DLL void XGBoosterLoadModel(void *handle, const char *fname);
   /*!
    * \brief save model into existing file
    * \param handle handle
    * \param fname file name
    */
-  void XGBoosterSaveModel(const void *handle, const char *fname);
+  XGB_DLL void XGBoosterSaveModel(const void *handle, const char *fname);
   /*!
    * \brief dump model, return array of strings representing model dump
    * \param handle handle
@@ -181,7 +182,7 @@ extern "C" {
    * \param out_len length of output array
    * \return char *data[], representing dump of each model
    */
-  const char **XGBoosterDumpModel(void *handle, const char *fmap,
+  XGB_DLL const char **XGBoosterDumpModel(void *handle, const char *fmap,
-                                  uint64_t *out_len);
+                                          uint64_t *out_len);
 };
 #endif  // XGBOOST_WRAPPER_H_