/usr/include/trilinos/KokkosKernels_Utils.hpp is in libtrilinos-kokkos-kernels-dev 12.12.1-5.
This file is owned by root:root, with mode 0o644.
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1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 | /*
//@HEADER
// ************************************************************************
//
// KokkosKernels 0.9: Linear Algebra and Graph Kernels
// Copyright 2017 Sandia Corporation
//
// Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
// the U.S. Government retains certain rights in this software.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// 3. Neither the name of the Corporation nor the names of the
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// Questions? Contact Siva Rajamanickam (srajama@sandia.gov)
//
// ************************************************************************
//@HEADER
*/
#include "Kokkos_Core.hpp"
#include "Kokkos_Atomic.hpp"
#include "impl/Kokkos_Timer.hpp"
#include "Kokkos_MemoryTraits.hpp"
#include "Kokkos_ArithTraits.hpp"
#include "Kokkos_UnorderedMap.hpp"
#include <iostream>
#include <limits>
#include "KokkosKernels_ExecSpaceUtils.hpp"
#include "KokkosKernels_SimpleUtils.hpp"
#include "KokkosKernels_SparseUtils.hpp"
#include "KokkosKernels_PrintUtils.hpp"
#include "KokkosKernels_VectorUtils.hpp"
#ifndef _KOKKOSKERNELSUTILS_HPP
#define _KOKKOSKERNELSUTILS_HPP
namespace KokkosKernels{
namespace Experimental{
namespace Util{
template <typename ExecutionSpace>
ExecSpaceType get_exec_space_type(){
return kk_get_exec_space_type<ExecutionSpace>();
}
inline int get_suggested_vector__size(
size_t nr, size_t nnz, ExecSpaceType exec_space){
return kk_get_suggested_vector_size(nr,nnz, exec_space);
}
template <typename in_lno_view_t,
typename out_lno_view_t,
typename MyExecSpace>
void get_histogram(
typename in_lno_view_t::size_type in_elements,
in_lno_view_t in_view,
out_lno_view_t histogram /*must be initialized with 0s*/){
kk_get_histogram<in_lno_view_t, out_lno_view_t, MyExecSpace>(in_elements, in_view, histogram);
}
template <typename idx, typename ExecutionSpace>
void get_suggested_vector_team_size(
int max_allowed_team_size,
int &suggested_vector_size_,
int &suggested_team_size_,
idx nr, idx nnz){
#if defined( KOKKOS_HAVE_SERIAL )
if (Kokkos::Impl::is_same< Kokkos::Serial , ExecutionSpace >::value){
suggested_vector_size_ = 1;
suggested_team_size_ = 1;
return;
}
#endif
#if defined( KOKKOS_HAVE_PTHREAD )
if (Kokkos::Impl::is_same< Kokkos::Threads , ExecutionSpace >::value){
suggested_vector_size_ = 1;
suggested_team_size_ = 1;
return;
}
#endif
#if defined( KOKKOS_HAVE_OPENMP )
if (Kokkos::Impl::is_same< Kokkos::OpenMP, ExecutionSpace >::value){
suggested_vector_size_ = 1;
suggested_team_size_ = 1;
}
#endif
#if defined( KOKKOS_HAVE_CUDA )
if (Kokkos::Impl::is_same<Kokkos::Cuda, ExecutionSpace >::value){
suggested_vector_size_ = nnz / double (nr) + 0.5;
if (suggested_vector_size_ <= 3){
suggested_vector_size_ = 2;
}
else if (suggested_vector_size_ <= 6){
suggested_vector_size_ = 4;
}
else if (suggested_vector_size_ <= 12){
suggested_vector_size_ = 8;
}
else if (suggested_vector_size_ <= 24){
suggested_vector_size_ = 16;
}
else {
suggested_vector_size_ = 32;
}
suggested_team_size_ = max_allowed_team_size / suggested_vector_size_;
}
#endif
#if defined( KOKKOS_HAVE_QTHREAD)
if (Kokkos::Impl::is_same< Kokkos::Qthread, ExecutionSpace >::value){
suggested_vector_size_ = 1;
suggested_team_size_ = 1;
}
#endif
}
template <typename idx_array_type,
typename idx_edge_array_type,
typename idx_out_edge_array_type,
typename team_member>
struct FillSymmetricEdges{
typedef typename idx_array_type::value_type idx;
idx num_rows;
idx nnz;
idx_array_type xadj;
idx_edge_array_type adj;
idx_out_edge_array_type srcs;
idx_out_edge_array_type dsts;
FillSymmetricEdges(
typename idx_array_type::value_type num_rows_,
idx_array_type xadj_,
idx_edge_array_type adj_,
idx_out_edge_array_type srcs_,
idx_out_edge_array_type dsts_
):num_rows(num_rows_),nnz(adj_.dimension_0()), xadj(xadj_), adj(adj_), srcs(srcs_), dsts(dsts_){}
KOKKOS_INLINE_FUNCTION
void operator()(const team_member & teamMember) const {
idx ii = teamMember.league_rank() * teamMember.team_size()+ teamMember.team_rank();
if (ii >= num_rows) return;
idx row_begin = xadj[ii];
idx row_end = xadj[ii + 1];
Kokkos::parallel_for(
Kokkos::ThreadVectorRange(teamMember, row_end - row_begin),
[&] (idx i) {
idx adjind = i + row_begin;
idx colIndex = adj[adjind];
if (colIndex < num_rows){
srcs[adjind] = ii + 1;
dsts[adjind] = colIndex + 1;
if (colIndex != ii){
srcs[adjind + nnz] = colIndex + 1;
dsts[adjind + nnz] = ii + 1;
}
}
});
}
};
template <typename in_lno_row_view_t,
typename in_lno_nnz_view_t,
typename hashmap_t,
typename out_lno_row_view_t,
typename team_member>
struct FillSymmetricEdgesHashMap{
typedef typename in_lno_row_view_t::value_type idx;
idx num_rows;
idx nnz;
in_lno_row_view_t xadj;
in_lno_nnz_view_t adj;
hashmap_t umap;
out_lno_row_view_t pre_pps;
bool lower_only;
FillSymmetricEdgesHashMap(
idx num_rows_,
in_lno_row_view_t xadj_,
in_lno_nnz_view_t adj_,
hashmap_t hashmap_,
out_lno_row_view_t pre_pps_
):num_rows(num_rows_),nnz(adj_.dimension_0()), xadj(xadj_), adj(adj_),
umap(hashmap_), pre_pps(pre_pps_){}
KOKKOS_INLINE_FUNCTION
void operator()(const team_member & teamMember/*, idx &nnz*/) const {
idx ii = teamMember.league_rank() * teamMember.team_size()+ teamMember.team_rank();
if (ii >= num_rows) {
return;
}
idx row_begin = xadj[ii];
idx row_end = xadj[ii + 1];
Kokkos::parallel_for(
Kokkos::ThreadVectorRange(teamMember, row_end - row_begin),
[&] (idx i) {
idx adjind = i + row_begin;
idx colIndex = adj[adjind];
if (colIndex < num_rows){
if (colIndex < ii){
Kokkos::UnorderedMapInsertResult r = umap.insert(Kokkos::pair<idx, idx>(colIndex, ii));
if (r.success()){
Kokkos::atomic_fetch_add(&(pre_pps(ii)),1);
Kokkos::atomic_fetch_add(&(pre_pps(colIndex)),1);
}
}
else if (colIndex > ii){
Kokkos::UnorderedMapInsertResult r = umap.insert(Kokkos::pair<idx, idx>(ii, colIndex));
if (r.success()){
Kokkos::atomic_fetch_add(&(pre_pps(colIndex)),1);
Kokkos::atomic_fetch_add(&(pre_pps(ii)),1);
}
}
else {
Kokkos::atomic_fetch_add(&(pre_pps(ii)),1);
}
}
});
}
};
template <typename in_lno_row_view_t,
typename in_lno_nnz_view_t,
typename hashmap_t,
typename out_lno_row_view_t,
typename team_member>
struct FillSymmetricLowerEdgesHashMap{
typedef typename in_lno_row_view_t::value_type idx;
idx num_rows;
idx nnz;
in_lno_row_view_t xadj;
in_lno_nnz_view_t adj;
hashmap_t umap;
out_lno_row_view_t pre_pps;
FillSymmetricLowerEdgesHashMap(
idx num_rows_,
in_lno_row_view_t xadj_,
in_lno_nnz_view_t adj_,
hashmap_t hashmap_,
out_lno_row_view_t pre_pps_,
bool lower_only_ = false
):num_rows(num_rows_),nnz(adj_.dimension_0()), xadj(xadj_), adj(adj_),
umap(hashmap_), pre_pps(pre_pps_){}
KOKKOS_INLINE_FUNCTION
void operator()(const team_member & teamMember/*, idx &nnz*/) const {
idx ii = teamMember.league_rank() * teamMember.team_size()+ teamMember.team_rank();
if (ii >= num_rows) {
return;
}
idx row_begin = xadj[ii];
idx row_end = xadj[ii + 1];
Kokkos::parallel_for(
Kokkos::ThreadVectorRange(teamMember, row_end - row_begin),
[&] (idx i) {
idx adjind = i + row_begin;
idx colIndex = adj[adjind];
if (colIndex < num_rows){
if (colIndex < ii){
Kokkos::UnorderedMapInsertResult r = umap.insert(Kokkos::pair<idx, idx>(colIndex, ii));
if (r.success()){
Kokkos::atomic_fetch_add(&(pre_pps(colIndex)),1);
}
}
else if (colIndex > ii){
Kokkos::UnorderedMapInsertResult r = umap.insert(Kokkos::pair<idx, idx>(ii, colIndex));
if (r.success()){
Kokkos::atomic_fetch_add(&(pre_pps(ii)),1);
}
}
}
});
}
};
template <typename in_lno_row_view_t,
typename in_lno_nnz_view_t,
typename hashmap_t,
typename out_lno_row_view_t,
typename out_lno_nnz_view_t,
typename team_member_t>
struct FillSymmetricCRS_HashMap{
typedef typename in_lno_row_view_t::value_type idx;
idx num_rows;
idx nnz;
in_lno_row_view_t xadj;
in_lno_nnz_view_t adj;
hashmap_t umap;
out_lno_row_view_t pre_pps;
out_lno_nnz_view_t sym_adj;
FillSymmetricCRS_HashMap(idx num_rows_,
in_lno_row_view_t xadj_,
in_lno_nnz_view_t adj_,
hashmap_t hashmap_,
out_lno_row_view_t pre_pps_,
out_lno_nnz_view_t sym_adj_):
num_rows(num_rows_),nnz(adj_.dimension_0()),
xadj(xadj_), adj(adj_),
umap(hashmap_), pre_pps(pre_pps_), sym_adj(sym_adj_){}
KOKKOS_INLINE_FUNCTION
void operator()(const team_member_t & teamMember) const {
idx ii = teamMember.league_rank() * teamMember.team_size()+ teamMember.team_rank();
if (ii >= num_rows) {
return;
}
idx row_begin = xadj[ii];
idx row_end = xadj[ii + 1];
Kokkos::parallel_for(
Kokkos::ThreadVectorRange(teamMember, row_end - row_begin),
[&] (idx i) {
idx adjind = i + row_begin;
idx colIndex = adj[adjind];
if (colIndex < num_rows){
if (colIndex < ii){
if (umap.insert(Kokkos::pair<idx, idx>(colIndex, ii)).success()){
idx cAdjInd = Kokkos::atomic_fetch_add(&(pre_pps(colIndex)),1);
idx iAdjInd = Kokkos::atomic_fetch_add(&(pre_pps(ii)),1);
sym_adj[cAdjInd] = ii;
sym_adj[iAdjInd] = colIndex;
}
}
else if (colIndex > ii){
if (umap.insert(Kokkos::pair<idx, idx>(ii, colIndex)).success()){
idx cAdjInd = Kokkos::atomic_fetch_add(&(pre_pps(colIndex)),1);
idx iAdjInd = Kokkos::atomic_fetch_add(&(pre_pps(ii)),1);
sym_adj[cAdjInd] = ii;
sym_adj[iAdjInd] = colIndex;
}
}
else {
idx cAdjInd = Kokkos::atomic_fetch_add(&(pre_pps(colIndex)),1);
sym_adj[cAdjInd] = ii;
}
}
});
}
};
template <typename in_lno_row_view_t,
typename in_lno_nnz_view_t,
typename hashmap_t,
typename out_lno_nnz_view_t,
typename out_lno_row_view_t,
typename team_member_t>
struct FillSymmetricEdgeList_HashMap{
typedef typename in_lno_row_view_t::value_type idx;
idx num_rows;
idx nnz;
in_lno_row_view_t xadj;
in_lno_nnz_view_t adj;
hashmap_t umap;
out_lno_nnz_view_t sym_src;
out_lno_nnz_view_t sym_dst;
out_lno_row_view_t pps;
FillSymmetricEdgeList_HashMap(
idx num_rows_,
in_lno_row_view_t xadj_,
in_lno_nnz_view_t adj_,
hashmap_t hashmap_,
out_lno_nnz_view_t sym_src_,
out_lno_nnz_view_t sym_dst_,
out_lno_row_view_t pps_):
num_rows(num_rows_),nnz(adj_.dimension_0()),
xadj(xadj_), adj(adj_),
umap(hashmap_), sym_src(sym_src_), sym_dst(sym_dst_), pps(pps_){}
KOKKOS_INLINE_FUNCTION
void operator()(const team_member_t & teamMember) const {
idx ii = teamMember.league_rank() * teamMember.team_size()+ teamMember.team_rank();
if (ii >= num_rows) {
return;
}
idx row_begin = xadj[ii];
idx row_end = xadj[ii + 1];
Kokkos::parallel_for(
Kokkos::ThreadVectorRange(teamMember, row_end - row_begin),
[&] (idx i) {
idx adjind = i + row_begin;
idx colIndex = adj[adjind];
if (colIndex < num_rows){
if (colIndex < ii){
if (umap.insert(Kokkos::pair<idx, idx>(colIndex, ii)).success()){
idx cAdjInd = Kokkos::atomic_fetch_add(&(pps(colIndex)),1);
sym_src[cAdjInd] = colIndex;
sym_dst[cAdjInd] = ii;
}
}
else if (colIndex > ii){
if (umap.insert(Kokkos::pair<idx, idx>(ii, colIndex)).success()){
idx cAdjInd = Kokkos::atomic_fetch_add(&(pps(ii)),1);
sym_src[cAdjInd] = ii;
sym_dst[cAdjInd] = colIndex;
}
}
}
});
}
};
template <typename idx_array_type>
void print_1Dview(idx_array_type view, bool print_all = false){
kk_print_1Dview(view, print_all);
}
template <typename forward_map_type, typename reverse_map_type>
struct Reverse_Map_Init{
typedef typename forward_map_type::value_type forward_type;
typedef typename reverse_map_type::value_type reverse_type;
forward_map_type forward_map;
reverse_map_type reverse_map_xadj;
Reverse_Map_Init(
forward_map_type forward_map_,
reverse_map_type reverse_xadj_):
forward_map(forward_map_), reverse_map_xadj(reverse_xadj_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t &ii) const {
forward_type fm = forward_map[ii];
Kokkos::atomic_fetch_add( &(reverse_map_xadj(fm)), 1);
}
/*
KOKKOS_INLINE_FUNCTION
void operator()(const forward_type ii, size_t& update, const bool final) const {
update += reverse_map_xadj(ii);
if (final) {
reverse_map_xadj(ii) = reverse_type (update);
}
}
*/
};
template <typename forward_map_type, typename reverse_map_type>
struct Fill_Reverse_Map{
typedef typename forward_map_type::value_type forward_type;
typedef typename reverse_map_type::value_type reverse_type;
forward_map_type forward_map;
reverse_map_type reverse_map_xadj;
reverse_map_type reverse_map_adj;
Fill_Reverse_Map(
forward_map_type forward_map_,
reverse_map_type reverse_map_xadj_,
reverse_map_type reverse_map_adj_):
forward_map(forward_map_), reverse_map_xadj(reverse_map_xadj_), reverse_map_adj(reverse_map_adj_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t &ii) const {
forward_type c = forward_map[ii];
const reverse_type future_index = Kokkos::atomic_fetch_add( &(reverse_map_xadj(c - 1)), 1);
reverse_map_adj(future_index) = ii;
}
};
template <typename forward_array_type, typename MyExecSpace>
void inclusive_parallel_prefix_sum(typename forward_array_type::value_type num_elements, forward_array_type arr){
kk_inclusive_parallel_prefix_sum<forward_array_type, MyExecSpace>(num_elements, arr);
}
template <typename forward_array_type, typename MyExecSpace>
void exclusive_parallel_prefix_sum(typename forward_array_type::value_type num_elements, forward_array_type arr){
kk_exclusive_parallel_prefix_sum<forward_array_type, MyExecSpace>(num_elements, arr);
}
template <typename array_type>
struct PropogataMaxValstoZeros{
typedef typename array_type::value_type idx;
array_type array_sum;
PropogataMaxValstoZeros(array_type arr_): array_sum(arr_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t ii, idx& update, const bool final) const {
idx value = array_sum(ii);
if (value != 0) {
update = value;
}
else if (final ){
array_sum(ii) = idx (update);
}
}
KOKKOS_INLINE_FUNCTION
void join( volatile idx & update
, volatile const idx & input ) const {
if (input > update) update = input;
}
};
template <typename out_array_t, typename in_array_t, typename scalar_1, typename scalar_2, typename MyExecSpace>
void a_times_x_plus_b(typename in_array_t::value_type num_elements,
in_array_t out_arr, in_array_t in_arr,
scalar_1 a, scalar_2 b){
kk_a_times_x_plus_b<out_array_t, in_array_t, scalar_1, scalar_2, MyExecSpace>
(num_elements, out_arr, in_arr,a, b);
}
template <typename out_array_type, typename in_array_type, typename MyExecSpace>
void modular_view(typename in_array_type::value_type num_elements, out_array_type out_arr, in_array_type in_arr, int mod_factor_){
kk_modular_view<out_array_type, in_array_type, MyExecSpace>
(num_elements, out_arr, in_arr, mod_factor_);
}
template <typename array_type>
struct LinearInitialization{
typedef typename array_type::value_type idx;
array_type array_sum;
LinearInitialization(array_type arr_): array_sum(arr_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t ii) const {
array_sum(ii) = ii;
}
};
template <typename array_type, typename MyExecSpace>
void linear_init(typename array_type::value_type num_elements, array_type arr){
typedef Kokkos::RangePolicy<MyExecSpace> my_exec_space;
Kokkos::parallel_for( my_exec_space(0, num_elements), LinearInitialization<array_type>(arr));
}
template <typename forward_array_type, typename MyExecSpace>
void remove_zeros_in_xadj_vector(typename forward_array_type::value_type num_elements, forward_array_type arr){
typedef Kokkos::RangePolicy<MyExecSpace> my_exec_space;
Kokkos::parallel_scan( my_exec_space(0, num_elements), PropogataMaxValstoZeros<forward_array_type>(arr));
}
template <typename forward_array_type, typename reverse_array_type>
struct FillReverseBegins{
const forward_array_type &forward_map; //vertex to colors
reverse_array_type &reverse_map_xadj; // colors to vertex xadj
FillReverseBegins(
const forward_array_type &forward_map_, //vertex to colors
reverse_array_type &reverse_map_xadj_ // colors to vertex xadj
):
forward_map(forward_map_), reverse_map_xadj(reverse_map_xadj_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t ii) const {
typename forward_array_type::value_type prev_col = forward_map(ii - 1);
typename forward_array_type::value_type cur_col = forward_map(ii);
while (prev_col < cur_col){
prev_col += 1;
forward_map(prev_col) = ii + 1;
}
}
};
template <typename forward_map_type, typename reverse_map_type>
struct Reverse_Map_Scale_Init{
typedef typename forward_map_type::value_type forward_type;
typedef typename reverse_map_type::value_type reverse_type;
forward_map_type forward_map;
reverse_map_type reverse_map_xadj;
const reverse_type multiply_shift_for_scale;
const reverse_type division_shift_for_bucket;
Reverse_Map_Scale_Init(
forward_map_type forward_map_,
reverse_map_type reverse_xadj_,
reverse_type multiply_shift_for_scale_,
reverse_type division_shift_for_bucket_):
forward_map(forward_map_), reverse_map_xadj(reverse_xadj_),
multiply_shift_for_scale(multiply_shift_for_scale_),
division_shift_for_bucket(division_shift_for_bucket_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t &ii) const {
forward_type fm = forward_map[ii];
fm = fm << multiply_shift_for_scale;
fm += ii >> division_shift_for_bucket;
Kokkos::atomic_fetch_add( &(reverse_map_xadj(fm)), 1);
}
};
template <typename forward_map_type, typename reverse_map_type>
struct Fill_Reverse_Scale_Map{
typedef typename forward_map_type::value_type forward_type;
typedef typename reverse_map_type::value_type reverse_type;
forward_map_type forward_map;
reverse_map_type reverse_map_xadj;
reverse_map_type reverse_map_adj;
const reverse_type multiply_shift_for_scale;
const reverse_type division_shift_for_bucket;
Fill_Reverse_Scale_Map(
forward_map_type forward_map_,
reverse_map_type reverse_map_xadj_,
reverse_map_type reverse_map_adj_,
reverse_type multiply_shift_for_scale_,
reverse_type division_shift_for_bucket_):
forward_map(forward_map_), reverse_map_xadj(reverse_map_xadj_), reverse_map_adj(reverse_map_adj_),
multiply_shift_for_scale(multiply_shift_for_scale_),
division_shift_for_bucket(division_shift_for_bucket_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t &ii) const {
forward_type fm = forward_map[ii];
fm = fm << multiply_shift_for_scale;
fm += ii >> division_shift_for_bucket;
const reverse_type future_index = Kokkos::atomic_fetch_add( &(reverse_map_xadj(fm - 1)), 1);
reverse_map_adj(future_index) = ii;
}
};
template <typename from_view_t, typename to_view_t>
struct StridedCopy{
const from_view_t from;
to_view_t to;
const size_t stride;
StridedCopy(
const from_view_t from_,
to_view_t to_,
size_t stride_):from(from_), to (to_), stride(stride_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t &ii) const {
//std::cout << "ii:" << ii << " ii * stride:" << ii * stride << std::endl;
to[ii] = from[(ii + 1) * stride - 1];
}
};
/**
* \brief Utility function to obtain a reverse map given a map.
* Input is a map with the number of elements within the map.
* forward_map[c] = i, where c is a forward elements and forward_map has a size of num_forward_elements.
* i is the value that c is mapped in the forward map, and the range of that is num_reverse_elements.
* Output is the reverse_map_xadj and reverse_map_adj such that,
* all c, forward_map[c] = i, will appear in reverse_map_adj[ reverse_map_xadj[i]: reverse_map_xadj[i+1])
* \param: num_forward_elements: the number of elements in the forward map, the size of the forward map.
* \param: num_reverse_elements: the number of elements that forward map is mapped to. It is the value of max i.
* \param: forward_map: input forward_map, where forward_map[c] = i.
* \param: reverse_map_xadj: reverse map xadj, that is it will hold the beginning and
* end indices on reverse_map_adj such that all values mapped to i will be [ reverse_map_xadj[i]: reverse_map_xadj[i+1])
* its size will be num_reverse_elements + 1.
* \param: reverse_map_adj: reverse map adj, holds the values of reverse maps. Its size will be num_forward_elements.
*
*/
template <typename forward_array_type, typename reverse_array_type, typename MyExecSpace>
void create_reverse_map(
const typename reverse_array_type::value_type &num_forward_elements, //num_vertices
const typename forward_array_type::value_type &num_reverse_elements, //num_colors
const forward_array_type &forward_map, //vertex to colors
reverse_array_type &reverse_map_xadj, // colors to vertex xadj
reverse_array_type &reverse_map_adj){ //colros to vertex adj
typedef typename reverse_array_type::value_type lno_t;
typedef typename forward_array_type::value_type reverse_lno_t;
const lno_t MINIMUM_TO_ATOMIC = 64;
typedef Kokkos::RangePolicy<MyExecSpace> my_exec_space;
reverse_map_xadj = reverse_array_type("Reverse Map Xadj", num_reverse_elements + 1);
reverse_map_adj = reverse_array_type(Kokkos::ViewAllocateWithoutInitializing("REVERSE_ADJ"), num_forward_elements);
if (num_reverse_elements < MINIMUM_TO_ATOMIC){
const lno_t scale_size = 1024;
const lno_t multiply_shift_for_scale = 10;
const lno_t division_shift_for_bucket =
lno_t (ceil(log(double (num_forward_elements) / scale_size)/log(2)));
//const lno_t bucket_range_size = pow(2, division_shift_for_bucket);
//coloring indices are base-1. we end up using not using element 1.
const reverse_lno_t tmp_reverse_size = (num_reverse_elements + 1) << multiply_shift_for_scale;
reverse_array_type tmp_color_xadj ("TMP_REVERSE_XADJ",
tmp_reverse_size + 1);
Reverse_Map_Scale_Init<forward_array_type, reverse_array_type> rmi(
forward_map,
tmp_color_xadj,
multiply_shift_for_scale,
division_shift_for_bucket);
Kokkos::parallel_for (my_exec_space (0, num_forward_elements) , rmi);
MyExecSpace::fence();
inclusive_parallel_prefix_sum<reverse_array_type, MyExecSpace>(tmp_reverse_size + 1, tmp_color_xadj);
MyExecSpace::fence();
Kokkos::parallel_for (my_exec_space (0, num_reverse_elements + 1) , StridedCopy<reverse_array_type, reverse_array_type>(tmp_color_xadj, reverse_map_xadj, scale_size));
MyExecSpace::fence();
Fill_Reverse_Scale_Map<forward_array_type, reverse_array_type> frm (forward_map, tmp_color_xadj, reverse_map_adj,
multiply_shift_for_scale, division_shift_for_bucket);
Kokkos::parallel_for (my_exec_space (0, num_forward_elements) , frm);
MyExecSpace::fence();
}
else
//atomic implementation.
{
reverse_array_type tmp_color_xadj (Kokkos::ViewAllocateWithoutInitializing("TMP_REVERSE_XADJ"), num_reverse_elements + 1);
Reverse_Map_Init<forward_array_type, reverse_array_type> rmi(forward_map, reverse_map_xadj);
Kokkos::parallel_for (my_exec_space (0, num_forward_elements) , rmi);
MyExecSpace::fence();
//print_1Dview(reverse_map_xadj);
inclusive_parallel_prefix_sum<reverse_array_type, MyExecSpace>(num_reverse_elements + 1, reverse_map_xadj);
MyExecSpace::fence();
Kokkos::deep_copy (tmp_color_xadj, reverse_map_xadj);
MyExecSpace::fence();
Fill_Reverse_Map<forward_array_type, reverse_array_type> frm (forward_map, tmp_color_xadj, reverse_map_adj);
Kokkos::parallel_for (my_exec_space (0, num_forward_elements) , frm);
MyExecSpace::fence();
}
}
template <typename value_array_type, typename out_value_array_type, typename idx_array_type>
struct PermuteVector{
typedef typename idx_array_type::value_type idx;
value_array_type old_vector;
out_value_array_type new_vector;
idx_array_type old_to_new_mapping;
idx mapping_size;
PermuteVector(
value_array_type old_vector_,
out_value_array_type new_vector_,
idx_array_type old_to_new_mapping_):
old_vector(old_vector_), new_vector(new_vector_),old_to_new_mapping(old_to_new_mapping_), mapping_size(old_to_new_mapping_.dimension_0()){}
KOKKOS_INLINE_FUNCTION
void operator()(const idx &ii) const {
idx mapping = ii;
if (ii < mapping_size) mapping = old_to_new_mapping[ii];
new_vector[mapping] = old_vector[ii];
}
};
template <typename value_array_type, typename out_value_array_type, typename idx_array_type, typename MyExecSpace>
void permute_vector(
typename idx_array_type::value_type num_elements,
idx_array_type &old_to_new_index_map,
value_array_type &old_vector,
out_value_array_type &new_vector
){
typedef Kokkos::RangePolicy<MyExecSpace> my_exec_space;
Kokkos::parallel_for( my_exec_space(0,num_elements),
PermuteVector<value_array_type, out_value_array_type, idx_array_type>(old_vector, new_vector, old_to_new_index_map));
}
template <typename value_array_type, typename MyExecSpace>
void zero_vector(
typename value_array_type::value_type num_elements,
value_array_type &vector
){
typedef typename value_array_type::non_const_value_type val_type;
Kokkos::deep_copy (vector, Kokkos::Details::ArithTraits<val_type>::zero ());
}
template <typename v1, typename v2, typename v3>
struct MarkDuplicateSortedKeyValuePairs{
v1 keys;
v2 vals;
v3 prefix_sum;
typename v1::size_type overall_size;
MarkDuplicateSortedKeyValuePairs(v1 keys_,v2 vals_, v3 prefix_sum_, typename v1::size_type overall_size_):
keys(keys_), vals(vals_), prefix_sum(prefix_sum_), overall_size(overall_size_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t &i, typename v3::value_type &num_result) const {
typename v1::value_type my_key = keys(i);
typename v2::value_type my_val = vals(i);
if ((my_key != 0 && my_val != 0) && ((i + 1 >= overall_size) || (my_key != keys(i + 1) || my_val != vals(i + 1)))){
prefix_sum(i) = 1;
num_result += 1;
}
}
};
template <typename v1, typename v2, typename v3, typename v4, typename v5>
struct FillSymmetricCSR{
v1 keys;
v2 vals;
v3 prefix_sum;
typename v3::size_type array_size;
v4 out_xadj;
v5 out_adj;
FillSymmetricCSR(
v1 keys_,v2 vals_, v3 prefix_sum_, typename v3::size_type array_size_,
v4 out_xadj_, v5 out_adj_):
keys(keys_), vals(vals_), prefix_sum(prefix_sum_), array_size(array_size_),
out_xadj(out_xadj_), out_adj(out_adj_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t &i) const {
typename v3::value_type my_pos = prefix_sum(i);
if (i + 1 >= array_size){
typename v2::value_type my_val = vals(i);
typename v1::value_type my_key = keys(i);
out_adj(my_pos) = my_val - 1;
out_xadj(my_key) = my_pos + 1;
}
else {
typename v3::value_type next_pos = prefix_sum(i + 1);
if (my_pos != next_pos){
typename v2::value_type my_val = vals(i);
typename v1::value_type my_key = keys(i);
typename v1::value_type next_key = keys(i + 1);
out_adj(my_pos) = my_val - 1;
if (my_key != next_key){
out_xadj(my_key) = my_pos + 1;
}
}
}
}
};
template <typename in_lno_row_view_t,
typename in_lno_nnz_view_t,
typename out_lno_nnz_view_t,
typename MyExecSpace>
void symmetrize_and_get_lower_diagonal_edge_list(
typename in_lno_nnz_view_t::value_type num_rows_to_symmetrize,
in_lno_row_view_t xadj,
in_lno_nnz_view_t adj,
out_lno_nnz_view_t &sym_srcs,
out_lno_nnz_view_t &sym_dsts_
){
typedef typename in_lno_row_view_t::non_const_value_type idx;
idx nnz = adj.dimension_0();
//idx_out_edge_array_type tmp_srcs("tmpsrc", nnz * 2);
//idx_out_edge_array_type tmp_dsts("tmpdst",nnz * 2);
typedef Kokkos::TeamPolicy<MyExecSpace> team_policy ;
typedef typename team_policy::member_type team_member_t ;
//typedef Kokkos::RangePolicy<MyExecSpace> my_exec_space;
//TODO: Should change this to temporary memory space?
typedef Kokkos::UnorderedMap< Kokkos::pair<idx, idx> , void , MyExecSpace> hashmap_t;
out_lno_nnz_view_t pre_pps_("pre_pps", num_rows_to_symmetrize + 1);
idx num_symmetric_edges = 0;
{
hashmap_t umap(nnz);
umap.clear();
umap.end_erase ();
FillSymmetricLowerEdgesHashMap <in_lno_row_view_t, in_lno_nnz_view_t,
hashmap_t, out_lno_nnz_view_t, team_member_t> fse(
num_rows_to_symmetrize,
xadj,
adj,
umap,
pre_pps_
);
int teamSizeMax = 0;
int vector_size = 0;
int max_allowed_team_size = team_policy::team_size_max(fse);
get_suggested_vector_team_size<idx, MyExecSpace>(
max_allowed_team_size,
vector_size,
teamSizeMax,
xadj.dimension_0() - 1, nnz);
//std::cout << "max_allowed_team_size:" << max_allowed_team_size << " vs:" << vector_size << " tsm:" << teamSizeMax<< std::endl;
Kokkos::parallel_for(
team_policy(num_rows_to_symmetrize / teamSizeMax + 1 , teamSizeMax, vector_size),
fse/*, num_symmetric_edges*/);
MyExecSpace::fence();
}
if (num_rows_to_symmetrize > 0)
exclusive_parallel_prefix_sum<out_lno_nnz_view_t, MyExecSpace>(
num_rows_to_symmetrize + 1,
pre_pps_);
MyExecSpace::fence();
auto d_sym_edge_size = Kokkos::subview(pre_pps_, num_rows_to_symmetrize);
auto h_sym_edge_size = Kokkos::create_mirror_view (d_sym_edge_size);
Kokkos::deep_copy (h_sym_edge_size, d_sym_edge_size);
num_symmetric_edges = h_sym_edge_size();
/*
typename out_lno_nnz_view_t::HostMirror h_sym_edge_size = Kokkos::create_mirror_view (pre_pps_);
Kokkos::deep_copy (h_sym_edge_size , pre_pps_);
num_symmetric_edges = h_sym_edge_size(h_sym_edge_size.dimension_0() - 1);
*/
sym_srcs = out_lno_nnz_view_t(Kokkos::ViewAllocateWithoutInitializing("sym_srcs"), num_symmetric_edges);
sym_dsts_ = out_lno_nnz_view_t(Kokkos::ViewAllocateWithoutInitializing("sym_dsts_"), num_symmetric_edges);
MyExecSpace::fence();
{
hashmap_t umap (nnz);
FillSymmetricEdgeList_HashMap <in_lno_row_view_t, in_lno_nnz_view_t,
hashmap_t, out_lno_nnz_view_t, out_lno_nnz_view_t, team_member_t>
FSCH (num_rows_to_symmetrize, xadj, adj, umap, sym_srcs, sym_dsts_, pre_pps_);
int teamSizeMax = 0;
int vector_size = 0;
int max_allowed_team_size = team_policy::team_size_max(FSCH);
get_suggested_vector_team_size<idx, MyExecSpace>(
max_allowed_team_size,
vector_size,
teamSizeMax,
xadj.dimension_0() - 1, nnz);
Kokkos::parallel_for(
team_policy(num_rows_to_symmetrize / teamSizeMax + 1 , teamSizeMax, vector_size),
FSCH);
MyExecSpace::fence();
}
MyExecSpace::fence();
}
template <typename in_lno_row_view_t,
typename in_lno_nnz_view_t,
typename out_lno_row_view_t,
typename out_lno_nnz_view_t,
typename MyExecSpace>
void symmetrize_graph_symbolic_hashmap(
typename in_lno_row_view_t::value_type num_rows_to_symmetrize,
in_lno_row_view_t xadj,
in_lno_nnz_view_t adj,
out_lno_row_view_t &sym_xadj,
out_lno_nnz_view_t &sym_adj
){
typedef typename in_lno_row_view_t::non_const_value_type idx;
idx nnz = adj.dimension_0();
//idx_out_edge_array_type tmp_srcs("tmpsrc", nnz * 2);
//idx_out_edge_array_type tmp_dsts("tmpdst",nnz * 2);
typedef Kokkos::TeamPolicy<MyExecSpace> team_policy ;
typedef typename team_policy::member_type team_member_t ;
//typedef Kokkos::RangePolicy<MyExecSpace> my_exec_space;
//TODO: Should change this to temporary memory space?
typedef Kokkos::UnorderedMap< Kokkos::pair<idx, idx> , void , MyExecSpace> hashmap_t;
out_lno_row_view_t pre_pps_("pre_pps", num_rows_to_symmetrize + 1);
idx num_symmetric_edges = 0;
{
hashmap_t umap(nnz);
umap.clear();
umap.end_erase ();
FillSymmetricEdgesHashMap <in_lno_row_view_t, in_lno_nnz_view_t,
hashmap_t, out_lno_row_view_t, team_member_t> fse(
num_rows_to_symmetrize,
xadj,
adj,
umap,
pre_pps_
);
int teamSizeMax = 0;
int vector_size = 0;
int max_allowed_team_size = team_policy::team_size_max(fse);
get_suggested_vector_team_size<idx, MyExecSpace>(
max_allowed_team_size,
vector_size,
teamSizeMax,
xadj.dimension_0() - 1, nnz);
Kokkos::parallel_for(
team_policy(num_rows_to_symmetrize / teamSizeMax + 1 , teamSizeMax, vector_size),
fse/*, num_symmetric_edges*/);
MyExecSpace::fence();
}
if (num_rows_to_symmetrize > 0)
exclusive_parallel_prefix_sum<out_lno_row_view_t, MyExecSpace>(
num_rows_to_symmetrize + 1,
pre_pps_);
MyExecSpace::fence();
//out_lno_row_view_t d_sym_edge_size = Kokkos::subview(pre_pps_, num_rows_to_symmetrize, num_rows_to_symmetrize );
typename out_lno_row_view_t::HostMirror h_sym_edge_size = Kokkos::create_mirror_view (pre_pps_);
Kokkos::deep_copy (h_sym_edge_size , pre_pps_);
num_symmetric_edges = h_sym_edge_size(h_sym_edge_size.dimension_0() - 1);
sym_adj = out_lno_nnz_view_t(Kokkos::ViewAllocateWithoutInitializing("sym_adj"), num_symmetric_edges);
MyExecSpace::fence();
sym_xadj = out_lno_row_view_t(Kokkos::ViewAllocateWithoutInitializing("sym_xadj"), num_rows_to_symmetrize + 1);
Kokkos::deep_copy(sym_xadj, pre_pps_);
{
hashmap_t umap (nnz);
FillSymmetricCRS_HashMap <in_lno_row_view_t, in_lno_nnz_view_t,
hashmap_t, out_lno_row_view_t, out_lno_nnz_view_t, team_member_t>
FSCH (num_rows_to_symmetrize, xadj, adj, umap, pre_pps_, sym_adj);
int teamSizeMax = 0;
int vector_size = 0;
int max_allowed_team_size = team_policy::team_size_max(FSCH);
get_suggested_vector_team_size<idx, MyExecSpace>(
max_allowed_team_size,
vector_size,
teamSizeMax,
xadj.dimension_0() - 1, nnz);
Kokkos::parallel_for(
team_policy(num_rows_to_symmetrize / teamSizeMax + 1 , teamSizeMax, vector_size),
FSCH);
MyExecSpace::fence();
}
MyExecSpace::fence();
}
template <typename from_vector, typename to_vector, typename MyExecSpace>
void copy_vector(
size_t num_elements,
from_vector from, to_vector to){
kk_copy_vector<from_vector, to_vector, MyExecSpace>
(num_elements, from, to);
}
template <typename from_vector, typename to_vector>
struct CopyView{
from_vector from;
to_vector to;
CopyView(from_vector &from_, to_vector to_): from(from_), to(to_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t &i) const {
to(i) = from(i);
}
};
template <typename from_vector, typename to_vector, typename MyExecSpace>
void copy_view(
size_t num_elements,
from_vector from, to_vector to){
typedef Kokkos::RangePolicy<MyExecSpace> my_exec_space;
Kokkos::parallel_for( my_exec_space(0,num_elements), CopyView<from_vector, to_vector>(from, to));
}
template<typename view_type>
struct ReduceSumFunctor{
view_type view_to_reduce;
ReduceSumFunctor(
view_type view_to_reduce_): view_to_reduce(view_to_reduce_){}
void operator()(const size_t &i, typename view_type::non_const_value_type &sum_reduction) const {
sum_reduction += view_to_reduce(i);
}
};
template <typename view_type , typename MyExecSpace>
void view_reduce_sum(size_t num_elements, view_type view_to_reduce, typename view_type::non_const_value_type &sum_reduction){
typedef Kokkos::RangePolicy<MyExecSpace> my_exec_space;
Kokkos::parallel_reduce( my_exec_space(0,num_elements), ReduceSumFunctor<view_type>(view_to_reduce), sum_reduction);
}
template<typename view_type>
struct ReduceMaxFunctor{
view_type view_to_reduce;
typedef typename view_type::non_const_value_type value_type;
const value_type min_val;
ReduceMaxFunctor(
view_type view_to_reduce_): view_to_reduce(view_to_reduce_),
min_val((std::numeric_limits<value_type>::lowest())){
}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t &i, value_type &max_reduction) const {
value_type val = view_to_reduce(i);
if (max_reduction < val) { max_reduction = val;}
}
KOKKOS_INLINE_FUNCTION
void join (volatile value_type& dst,const volatile value_type& src) const {
if (dst < src) { dst = src;}
}
KOKKOS_INLINE_FUNCTION
void init (value_type& dst) const
{
// The identity under max is -Inf.
// Kokkos does not come with a portable way to access
// floating -point Inf and NaN. Trilinos does , however;
// see Kokkos :: ArithTraits in the Tpetra package.
dst = min_val;
}
};
template <typename view_type , typename MyExecSpace>
void view_reduce_max(size_t num_elements, view_type view_to_reduce, typename view_type::non_const_value_type &max_reduction){
typedef Kokkos::RangePolicy<MyExecSpace> my_exec_space;
Kokkos::parallel_reduce( my_exec_space(0,num_elements), ReduceMaxFunctor<view_type>(view_to_reduce), max_reduction);
}
template<typename view_type>
struct ReduceMaxRowFunctor{
view_type rowmap_view;
typedef typename view_type::non_const_value_type value_type;
const value_type min_val;
ReduceMaxRowFunctor(
view_type rowmap_view_): rowmap_view(rowmap_view_),
min_val((std::numeric_limits<value_type>::lowest())){
}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t &i, value_type &max_reduction) const {
value_type val = rowmap_view(i+1) - rowmap_view(i) ;
if (max_reduction < val) { max_reduction = val;}
}
KOKKOS_INLINE_FUNCTION
void join (volatile value_type& dst,const volatile value_type& src) const {
if (dst < src) { dst = src;}
}
KOKKOS_INLINE_FUNCTION
void init (value_type& dst) const
{
// The identity under max is -Inf.
// Kokkos does not come with a portable way to access
// floating -point Inf and NaN. Trilinos does , however;
// see Kokkos :: ArithTraits in the Tpetra package.
dst = min_val;
}
};
//view has num_rows+1 elements.
template <typename view_type , typename MyExecSpace>
void view_reduce_maxsizerow(size_t num_rows, view_type rowmap_view, typename view_type::non_const_value_type &max_reduction){
typedef Kokkos::RangePolicy<MyExecSpace> my_exec_space;
Kokkos::parallel_reduce( my_exec_space(0,num_rows), ReduceMaxRowFunctor<view_type>(rowmap_view), max_reduction);
}
template<typename view_type1, typename view_type2>
struct IsEqualFunctor{
view_type1 view1;
view_type2 view2;
IsEqualFunctor(view_type1 view1_, view_type2 view2_): view1(view1_), view2(view2_){}
KOKKOS_INLINE_FUNCTION
void operator()(const size_t &i, int &is_equal) const {
if (view1(i) != view2(i)) {
//std::cout << "i:" << i << "view1:" << view1(i) << " view2:" << view2(i) << std::endl;
//printf("i:%d v1:")
is_equal = 0;
}
}
KOKKOS_INLINE_FUNCTION
void join (volatile int& dst,const volatile int& src) const {
dst = dst & src;
}
KOKKOS_INLINE_FUNCTION
void init (int& dst) const
{
dst = 1;
}
};
template <typename view_type1, typename view_type2, typename MyExecSpace>
bool isSame(size_t num_elements, view_type1 view1, view_type2 view2){
typedef Kokkos::RangePolicy<MyExecSpace> my_exec_space;
int issame = 1;
Kokkos::parallel_reduce( my_exec_space(0,num_elements), IsEqualFunctor<view_type1, view_type2>(view1, view2), issame);
MyExecSpace::fence();
return issame;
}
template <typename a_view_t, typename b_view_t, typename size_type>
struct MaxHeap{
a_view_t heap_keys;
b_view_t heap_values;
size_type max_size;
size_type current_size;
MaxHeap (
a_view_t heap_keys_,
b_view_t heap_values_,
size_type max_size_): heap_keys(heap_keys_), heap_values(heap_values_), max_size(max_size_), current_size(0){}
KOKKOS_INLINE_FUNCTION
void insert(typename a_view_t::value_type &key, typename b_view_t::value_type &val){
for (size_type i = 0; i < current_size; ++i){
if (key == heap_keys(i)){
heap_values(i) = heap_values(i) & val;
return;
}
}
heap_keys(current_size) = key;
heap_values(current_size++) = val;
}
};
template <typename in_row_view_t,
typename in_nnz_view_t,
typename in_scalar_view_t,
typename out_row_view_t,
typename out_nnz_view_t,
typename out_scalar_view_t,
typename tempwork_row_view_t,
typename MyExecSpace>
struct TransposeMatrix2{
struct CountTag{};
struct FillTag{};
typedef struct CountTag CountTag;
typedef struct FillTag FillTag;
typedef Kokkos::TeamPolicy<CountTag, MyExecSpace> team_count_policy_t ;
typedef Kokkos::TeamPolicy<FillTag, MyExecSpace> team_fill_policy_t ;
typedef typename team_count_policy_t::member_type team_count_member_t ;
typedef typename team_fill_policy_t::member_type team_fill_member_t ;
typedef typename in_nnz_view_t::non_const_value_type nnz_lno_t;
typedef typename in_row_view_t::non_const_value_type size_type;
typename in_nnz_view_t::non_const_value_type num_rows;
typename in_nnz_view_t::non_const_value_type num_cols;
in_row_view_t xadj;
in_nnz_view_t adj;
in_scalar_view_t vals;
out_row_view_t t_xadj; //allocated
out_nnz_view_t t_adj; //allocated
out_nnz_view_t t_vals; //allocated
tempwork_row_view_t tmp_txadj;
bool transpose_values;
TransposeMatrix2(
nnz_lno_t num_rows_,
nnz_lno_t num_cols_,
in_row_view_t xadj_,
in_nnz_view_t adj_,
in_scalar_view_t vals_,
out_row_view_t t_xadj_,
out_nnz_view_t t_adj_,
out_nnz_view_t t_vals_,
tempwork_row_view_t tmp_txadj_,
bool transpose_values_):
num_rows(num_rows_), num_cols(num_cols_),
xadj(xadj_), adj(adj_), vals(vals_),
t_xadj(t_xadj_), t_adj(t_adj_), t_vals(t_vals_),
tmp_txadj(tmp_txadj_), transpose_values(transpose_values_){}
KOKKOS_INLINE_FUNCTION
void operator()(const CountTag&, const team_count_member_t & teamMember) const {
const nnz_lno_t row_index = teamMember.league_rank() * teamMember.team_size() + teamMember.team_rank();
if (row_index >= num_rows) return;
const size_type col_begin = xadj[row_index];
const size_type col_end = xadj[row_index + 1];
const nnz_lno_t left_work = col_end - col_begin;
Kokkos::parallel_for(
Kokkos::ThreadVectorRange(teamMember, left_work),
[&] (nnz_lno_t i) {
const size_type adjind = i + col_begin;
const nnz_lno_t colIndex = adj[adjind];
Kokkos::atomic_fetch_add(&(t_xadj(colIndex)),1);
});
}
KOKKOS_INLINE_FUNCTION
void operator()(const FillTag&, const team_fill_member_t & teamMember) const {
const nnz_lno_t row_index = teamMember.league_rank() * teamMember.team_size() + teamMember.team_rank();
if (row_index >= num_rows) return;
const size_type col_begin = xadj[row_index];
const size_type col_end = xadj[row_index + 1];
const nnz_lno_t left_work = col_end - col_begin;
Kokkos::parallel_for(
Kokkos::ThreadVectorRange(teamMember, left_work),
[&] (nnz_lno_t i) {
const size_type adjind = i + col_begin;
const nnz_lno_t colIndex = adj[adjind];
const size_type pos = Kokkos::atomic_fetch_add(&(tmp_txadj(colIndex)),1);
t_adj(pos) = row_index;
if (transpose_values){
t_vals(pos) = vals[adjind];
}
});
}
};
template <typename in_row_view_t,
typename in_nnz_view_t,
typename in_scalar_view_t,
typename out_row_view_t,
typename out_nnz_view_t,
typename out_scalar_view_t,
typename tempwork_row_view_t,
typename MyExecSpace>
void transpose_matrix(
typename in_nnz_view_t::non_const_value_type num_rows,
typename in_nnz_view_t::non_const_value_type num_cols,
in_row_view_t xadj,
in_nnz_view_t adj,
in_scalar_view_t vals,
out_row_view_t t_xadj, //pre-allocated -- initialized with 0
out_nnz_view_t t_adj, //pre-allocated -- no need for initialize
out_nnz_view_t t_vals, //pre-allocated -- no need for initialize
typename in_nnz_view_t::non_const_value_type team_row_work_size = 256
){
//first count the number of entries in each column
tempwork_row_view_t tmp_row_view(Kokkos::ViewAllocateWithoutInitializing("tmp_row_view"), num_cols + 1);
typedef TransposeMatrix <in_row_view_t, in_nnz_view_t, in_scalar_view_t,
out_row_view_t, out_nnz_view_t, out_scalar_view_t,
tempwork_row_view_t, MyExecSpace> TransposeFunctor_t;
TransposeFunctor_t tm (num_rows, num_cols, xadj, adj, vals, t_xadj, t_adj,t_vals, tmp_row_view, true, team_row_work_size);
typedef typename TransposeFunctor_t::team_count_policy_t tcp_t;
typedef typename TransposeFunctor_t::team_fill_policy_t tfp_t;
typename in_row_view_t::non_const_value_type nnz = adj.dimension_0();
int vector_size = get_suggested_vector__size(num_rows, nnz, get_exec_space_type<MyExecSpace>());
Kokkos::Impl::Timer timer1;
Kokkos::parallel_for( tcp_t(num_rows / team_row_work_size + 1 , Kokkos::AUTO_t(), vector_size), tm);
MyExecSpace::fence();
exclusive_parallel_prefix_sum<out_row_view_t, MyExecSpace>(num_cols+1, t_xadj);
MyExecSpace::fence();
Kokkos::deep_copy(tmp_row_view, t_xadj);
MyExecSpace::fence();
timer1.reset();
Kokkos::parallel_for( tfp_t(num_rows / team_row_work_size + 1 , Kokkos::AUTO_t(), vector_size), tm);
MyExecSpace::fence();
}
template <typename in_row_view_t,
typename in_nnz_view_t,
typename out_row_view_t,
typename out_nnz_view_t,
typename tempwork_row_view_t,
typename MyExecSpace>
void transpose_graph(
typename in_nnz_view_t::non_const_value_type num_rows,
typename in_nnz_view_t::non_const_value_type num_cols,
in_row_view_t xadj,
in_nnz_view_t adj,
out_row_view_t t_xadj, //pre-allocated -- initialized with 0
out_nnz_view_t t_adj, //pre-allocated -- no need for initialize
int vector_size = -1,
int suggested_team_size = -1,
typename in_nnz_view_t::non_const_value_type team_row_chunk_size = 256
){
kk_transpose_graph<in_row_view_t,in_nnz_view_t,
out_row_view_t, out_nnz_view_t, tempwork_row_view_t,
MyExecSpace>(
num_rows,
num_cols,
xadj,
adj,
t_xadj, //pre-allocated -- initialized with 0
t_adj, //pre-allocated -- no need for initialize
vector_size,
suggested_team_size,
team_row_chunk_size
);
}
//TODO: DELETE this one, old version.
template <typename in_row_view_t,
typename in_nnz_view_t,
typename out_row_view_t,
typename out_nnz_view_t,
typename tempwork_row_view_t,
typename MyExecSpace>
void transpose_graph2(
typename in_nnz_view_t::non_const_value_type num_rows,
typename in_nnz_view_t::non_const_value_type num_cols,
in_row_view_t xadj,
in_nnz_view_t adj,
out_row_view_t t_xadj, //pre-allocated -- initialized with 0
out_nnz_view_t t_adj //pre-allocated -- no need for initialize
){
//first count the number of entries in each column
tempwork_row_view_t tmp_row_view(Kokkos::ViewAllocateWithoutInitializing("tmp_row_view"), num_cols + 1);
in_nnz_view_t tmp1;
out_nnz_view_t tmp2;
typedef TransposeMatrix2 <in_row_view_t, in_nnz_view_t, in_nnz_view_t,
out_row_view_t, out_nnz_view_t, out_nnz_view_t,
tempwork_row_view_t, MyExecSpace> TransposeFunctor_t;
TransposeFunctor_t tm (num_rows, num_cols, xadj, adj, tmp1, t_xadj, t_adj, tmp2, tmp_row_view, false);
typedef typename TransposeFunctor_t::team_count_policy_t tcp_t;
typedef typename TransposeFunctor_t::team_fill_policy_t tfp_t;
typename in_row_view_t::non_const_value_type nnz = adj.dimension_0();
int vector_size = get_suggested_vector__size(num_rows, nnz, get_exec_space_type<MyExecSpace>());
Kokkos::Impl::Timer timer1;
Kokkos::parallel_for( tcp_t(num_rows , Kokkos::AUTO_t(), vector_size), tm);
MyExecSpace::fence();
exclusive_parallel_prefix_sum<out_row_view_t, MyExecSpace>(num_cols+1, t_xadj);
MyExecSpace::fence();
Kokkos::deep_copy(tmp_row_view, t_xadj);
MyExecSpace::fence();
timer1.reset();
Kokkos::parallel_for( tfp_t(num_rows , Kokkos::AUTO_t(), vector_size), tm);
MyExecSpace::fence();
}
template <typename in_view_t,
typename MyExecSpace>
struct InitScalar{
typedef Kokkos::TeamPolicy<MyExecSpace> team_policy_t ;
typedef typename team_policy_t::member_type team_member_t ;
typedef typename in_view_t::non_const_value_type nnz_lno_t;
typedef typename in_view_t::size_type size_type;
in_view_t view_to_init;
size_type num_elements;
size_type team_row_chunk_size;
nnz_lno_t init_val;
InitScalar(
size_type num_elements_,
in_view_t view_to_init_,
size_type chunk_size_,
nnz_lno_t init_val_):
num_elements(num_elements_),
view_to_init(view_to_init_), team_row_chunk_size(chunk_size_), init_val (init_val_){}
KOKKOS_INLINE_FUNCTION
void operator()(const team_member_t & teamMember) const {
//const nnz_lno_t row_index = teamMember.league_rank() * team_row_chunk_size;
const nnz_lno_t team_row_begin = teamMember.league_rank() * team_row_chunk_size;
const nnz_lno_t team_row_end = KOKKOSKERNELS_MACRO_MIN(team_row_begin + team_row_chunk_size, num_elements);
Kokkos::parallel_for(Kokkos::TeamThreadRange(teamMember, team_row_begin, team_row_end), [&] (const nnz_lno_t& row_ind){
view_to_init [row_ind] = init_val;
});
}
};
template <typename in_row_view_t, typename MyExecSpace>
void init_view_withscalar(typename in_row_view_t::size_type num_elements, in_row_view_t arr,
typename in_row_view_t::size_type team_size,
typename in_row_view_t::non_const_value_type init_val){
typename in_row_view_t::size_type chunk_size = num_elements / team_size;
typedef InitScalar <in_row_view_t, MyExecSpace> InitScalar_t;
InitScalar_t tm (num_elements, arr, chunk_size, init_val);
typedef typename InitScalar_t::team_policy_t tcp_t;
int vector_size = 1;
Kokkos::Impl::Timer timer1;
Kokkos::parallel_for( tcp_t(num_elements / chunk_size + 1 , team_size, vector_size), tm);
MyExecSpace::fence();
}
}
}
}
#endif
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