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/****************************************************************/
/* Parallel Combinatorial BLAS Library (for Graph Computations) */
/* version 1.6 -------------------------------------------------*/
/* date: 6/15/2017 ---------------------------------------------*/
/* authors: Ariful Azad, Aydin Buluc --------------------------*/
/****************************************************************/
#include <mpi.h>
#include <stdint.h>
#include <sys/time.h>
#include <iostream>
#include <fstream>
#include <string>
#include <sstream> // Required for stringstreams
#include <ctime>
#include <cmath>
#include "CombBLAS/CombBLAS.h"
namespace combblas {
class HipMCLClusterSaveHandler
{
public:
// no reader
template <typename c, typename t, typename VT>
void save(std::basic_ostream<c,t>& os, std::vector<VT> & strvec, int64_t index)
{
for (auto it = strvec.begin() ; it != strvec.end(); ++it)
os << *it << " ";
}
};
/**
* Write clusters to file: vertices belonging to a cluster are written in a single line separated by space.
* TODO: sort clusters by their sizes
* @param[in] ofName {output file name}
* @param[in] clustIdForVtx {the ith entry stores the cluster id of the ith vertex}
* @param[in] vtxLabels {labels of vertices}
*/
template <class IT>
void WriteMCLClusters(std::string ofName, FullyDistVec<IT, IT> clustIdForVtx, FullyDistVec<IT, std::array<char, MAXVERTNAME> > vtxLabels)
{
auto commGrid = clustIdForVtx.getcommgrid();
MPI_Comm World = commGrid->GetWorld();
int nprocs = commGrid->GetSize();
// find the number of clusters
IT nclusters = clustIdForVtx.Reduce(maximum<IT>(), (IT) 0 ) ;
nclusters ++; // because of zero based indexing for clusters
std::vector<int> rdispls(nprocs+1);
std::vector<int> recvcnt(nprocs);
std::vector<int> sendcnt(nprocs,0);
std::vector<int> sdispls(nprocs+1);
IT ploclen = clustIdForVtx.LocArrSize();
const IT* larr = clustIdForVtx.GetLocArr(); // local part of cluster ids for vertices
//just to get the destination processor
FullyDistVec<IT,IT> temp(commGrid, nclusters,0);
for(IT i=0; i < ploclen; ++i)
{
IT locind;
int owner = temp.Owner(larr[i], locind);
sendcnt[owner]++;
}
MPI_Alltoall(sendcnt.data(), 1, MPI_INT, recvcnt.data(), 1, MPI_INT, World);
sdispls[0] = 0;
rdispls[0] = 0;
for(int i=0; i<nprocs; ++i)
{
sdispls[i+1] = sdispls[i] + sendcnt[i];
rdispls[i+1] = rdispls[i] + recvcnt[i];
}
typedef std::array<char, MAXVERTNAME> STRASARRAY;
typedef std::pair< IT, STRASARRAY> TYPE2SEND;
const STRASARRAY* lVtxLabels = vtxLabels.GetLocArr();
std::vector<TYPE2SEND> senddata(ploclen);
// Pack cluster and vertex information to send
std::vector<int> count(nprocs, 0);
for(IT i=0; i < ploclen; ++i)
{
IT locind;
int owner = temp.Owner(larr[i], locind);
int idx = sdispls[owner] + count[owner];
count[owner]++;
senddata[idx] = TYPE2SEND(locind, lVtxLabels[i]); // sending local cluster ids for the destination processor
}
MPI_Datatype MPI_CLUST;
MPI_Type_contiguous(sizeof(TYPE2SEND), MPI_CHAR, &MPI_CLUST);
MPI_Type_commit(&MPI_CLUST);
IT totrecv = rdispls[nprocs];
std::vector<TYPE2SEND> recvdata(totrecv);
MPI_Alltoallv(senddata.data(), sendcnt.data(), sdispls.data(), MPI_CLUST, recvdata.data(), recvcnt.data(), rdispls.data(), MPI_CLUST, World);
// Receiver groups vertices by cluster ids
std::vector< std::vector<std::string> > vtxGroupbyCC(temp.LocArrSize());
for(int i=0; i<totrecv; ++i)
{
IT clusterID = recvdata[i].first;
auto locnull = std::find(recvdata[i].second.begin(), recvdata[i].second.end(), '\0'); // find the null character (or string::end)
std::string vtxstr(recvdata[i].second.begin(), locnull);
vtxGroupbyCC[clusterID].push_back(vtxstr);
}
// in each cluster sort vertex labels
#ifdef THREADED
#pragma omp parallel for
#endif
for(unsigned int i=0; i<vtxGroupbyCC.size(); ++i)
{
std::sort(vtxGroupbyCC[i].begin(), vtxGroupbyCC[i].end());
}
// Create a vector locally populate it
FullyDistVec<IT,std::vector<std::string> > clusters(commGrid, nclusters, std::vector<std::string>{});
for(int i=0; i<clusters.LocArrSize(); i++)
{
clusters.SetLocalElement(i, vtxGroupbyCC[i]);
}
// do not write header and 1-based
clusters.ParallelWrite(ofName, 1, HipMCLClusterSaveHandler(), false);
}
/**
* Write clusters to file: vertices belonging to a cluster are written in a single line separated by space.
* Ids of vertices are used as labels
* TODO: sort clusters by their sizes
* @param[in] ofName {output file name}
* @param[in] clustIdForVtx {the ith entry stores the cluster id of the ith vertex}
*/
template <class IT>
void WriteMCLClusters(std::string ofName, FullyDistVec<IT, IT> clustIdForVtx, int base)
{
auto commGrid = clustIdForVtx.getcommgrid();
MPI_Comm World = commGrid->GetWorld();
int nprocs = commGrid->GetSize();
IT lenuntil = clustIdForVtx.LengthUntil();
// find the number of clusters
IT nclusters = clustIdForVtx.Reduce(maximum<IT>(), (IT) 0 ) ;
nclusters ++; // because of zero based indexing for clusters
std::vector<int> rdispls(nprocs+1);
std::vector<int> recvcnt(nprocs);
std::vector<int> sendcnt(nprocs,0);
std::vector<int> sdispls(nprocs+1);
IT ploclen = clustIdForVtx.LocArrSize();
const IT* larr = clustIdForVtx.GetLocArr(); // local part of cluster ids for vertices
//just to get the destination processor
FullyDistVec<IT,IT> temp(commGrid, nclusters,0);
for(IT i=0; i < ploclen; ++i)
{
IT locind;
int owner = temp.Owner(larr[i], locind);
sendcnt[owner]++;
}
MPI_Alltoall(sendcnt.data(), 1, MPI_INT, recvcnt.data(), 1, MPI_INT, World);
sdispls[0] = 0;
rdispls[0] = 0;
for(int i=0; i<nprocs; ++i)
{
sdispls[i+1] = sdispls[i] + sendcnt[i];
rdispls[i+1] = rdispls[i] + recvcnt[i];
}
std::vector<std::pair<IT, IT>> senddata(ploclen);
// Pack cluster and vertex information to send
std::vector<int> count(nprocs, 0);
for(IT i=0; i < ploclen; ++i)
{
IT locind;
int owner = temp.Owner(larr[i], locind);
int idx = sdispls[owner] + count[owner];
count[owner]++;
senddata[idx] = std::make_pair(locind, i+lenuntil+base); // sending local cluster ids for the destination processor
}
MPI_Datatype MPI_CLUST;
MPI_Type_contiguous(sizeof(std::pair<IT, IT>), MPI_CHAR, &MPI_CLUST);
MPI_Type_commit(&MPI_CLUST);
IT totrecv = rdispls[nprocs];
std::vector<std::pair<IT, IT>> recvdata(totrecv);
MPI_Alltoallv(senddata.data(), sendcnt.data(), sdispls.data(), MPI_CLUST, recvdata.data(), recvcnt.data(), rdispls.data(), MPI_CLUST, World);
// Receiver groups vertices by cluster ids
std::vector< std::vector<IT> > vtxGroupbyCC(temp.LocArrSize());
for(int i=0; i<totrecv; ++i)
{
IT clusterID = recvdata[i].first;
vtxGroupbyCC[clusterID].push_back(recvdata[i].second);
}
// Create a vector locally populate it
FullyDistVec<IT,std::vector<IT> > clusters(commGrid, nclusters, std::vector<IT>{});
for(int i=0; i<clusters.LocArrSize(); i++)
{
clusters.SetLocalElement(i, vtxGroupbyCC[i]);
}
// do not write header and 1-based
clusters.ParallelWrite(ofName, 1, HipMCLClusterSaveHandler(), false);
}
}
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