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我上運行SLURM和我的Mac相同的升壓MPI C++程序段錯誤。兩者都使用1節點16核心運行....但是，在我的Mac上，我在SLURM超級計算系統上從來沒有遇到分段故障錯誤，如果我運行在許多內核上，幾乎總會遇到分段故障（如果我只運行4個核，錯誤不會發生，如果在32個核心，那麼它幾乎總是發生）（它只是看起來像，每次）C++ MPI上SLURM
[c558-403.stampede.tacc.utexas.edu:mpi_rank_0][error_sighandler] Caught error: Segmentation fault (signal 11)
分割故障是由隨機發生的事情寫的每一個執行在屏幕上線，我意識到賽格故障主發生在：
mpi_world.recv(dst_rank, 1,spin_tmp);
和spin_tmp是：
map<tuple<int,int,int,int>,int> spin_tmp;
其中的元組是從boost元組。我真的很掙扎，不知道地球上正在發生的事情....
我想在這裏給最少的代碼版本：
的main.cpp
int main (void){
mpi::environment mpi_env;
mpi::communicator mpi_world;
if (mpi_world.rank()==0)
{
read_from_file(id, max_sites, J, prec, num_loops, translation_algorithm,
basic_exact_mode, pseudo_mode, pseudo_mode_with_proof,
verbose, very_verbose, obscenely_verbose,input_PRIM_output_PRIMOUT_mode,limit_dimension,constant,mu,mu_translation_algorithm,mu_constant,work_with_mu,scan_chemical_potential,new_cluster_algorithm,use_new_pair_terms,use_new_triplet_terms, output_more_states,output_states_below_hull,how_much_lower,output_states_how_sparse,use_level_method,use_weighted_dual_average,global_parameters);
}
else
{
bool restart_signal=true;
periodic_slave();
return 0;
}
corecode(components, x_range, y_range, z_range, max_sites, num_loops,
Ji,
lowerboundclustertype, upperboundclustertype, cellrepresentation,
lower_bound, upper_bound, unitcell, periodicity, J_for_proof,
id, pseudo_mode, pseudo_mode_with_proof, basic_exact_mode,
very_verbose, obscenely_verbose,limit_dimension,new_cluster_algorithm,use_new_pair_terms,use_new_triplet_terms,map_periodicity_to_spin, use_level_method, use_weighted_dual_average,global_parameters);
}
。 CPP（具體爲從站）
void periodic_slave()
{
mpi::communicator mpi_world;
cout<<"\n hello I am slave: "<<mpi_world.rank()<<" received the global parameters"<<endl;
bool kill_all_slave=false;
broadcast(mpi_world,kill_all_slave,0);
while (kill_all_slave==false) {
vector<tuple<int,int,int,int,int,int> > periodicity_vector;
std::cout << "[SLAVE: " << mpi_world.rank()<< "] I am waiting to receive periodicity vector"<<endl;
periodicity_vector.clear();
broadcast(mpi_world,periodicity_vector,0);
std::cout << "[SLAVE: " << mpi_world.rank()<< "] I am waiting to J "<<endl;
// J.clear();
map<set<tuple<int,int,int,int,int> >, double> J;
broadcast(mpi_world,J,0);
int x_range,y_range,z_range;
double min_bound;
map<int,int> component;
cout<< "[SLAVE: " << mpi_world.rank()<<"] hello I am slave: "<<mpi_world.rank()<<" received 3rd set of the global parameters"<<endl;
broadcast(mpi_world,x_range,0);
broadcast(mpi_world,y_range,0);
cout<< "[SLAVE: " << mpi_world.rank()<<"] hello I am slave: "<<mpi_world.rank()<<" received 4th set of the global parameters"<<endl;
broadcast(mpi_world,z_range,0);
broadcast(mpi_world,component,0);
cout<< "[SLAVE: " << mpi_world.rank()<<"] hello I am slave: "<<mpi_world.rank()<<" received 5th set of the global parameters"<<endl;
broadcast(mpi_world,min_bound,0);
cout<< "[SLAVE: " << mpi_world.rank()<<"] hello I am slave: "<<mpi_world.rank()<<" received 6th set of the global parameters"<<endl;
bool stop = false;
string status="initial";
mpi_world.send(0, 0, status);
//
cout<< "[SLAVE: " << mpi_world.rank()<<"] hello I am slave: "<<mpi_world.rank()<<" I am going to send to root status"<<endl;
mpi_world.recv(0, 0, stop);
cout<< "[SLAVE: " << mpi_world.rank()<<"] hello I am slave: "<<mpi_world.rank()<<" I am going to receive stop signal from root"<<endl;
while(!stop) {
map< tuple<int,int,int,int,int,int>, map<set<tuple<int,int,int,int,int> >, double> > clustertype_periodic;
// Wait for new job
unsigned int job_id = 0;
cout<< "[SLAVE: " << mpi_world.rank()<<"] hello I am slave: "<<mpi_world.rank()<<" I am going to receive job id from root"<<endl;
mpi_world.recv(0, 0, job_id);
cout<< "[SLAVE: " << mpi_world.rank()<<"] hello I am slave: "<<mpi_world.rank()<<" I am going to receive stop min bound from root"<<endl;
mpi_world.recv(0, 1, min_bound);
std::cout << "[SLAVE: " << mpi_world.rank()
<< "] Received job " << job_id << " from MASTER.\n"<<endl;
// Perform "job"
map<tuple<int,int,int,int>,int> spin_tmp;
double energy_tmp=0;
tuple<int,int,int,int,int,int> periodicity_now;
{
int i=job_id;
periodicity_now=periodicity_vector[i];
int a0=periodicity_now.get<0>();
int a1=periodicity_now.get<1>();
int a2=periodicity_now.get<2>();
int a3=periodicity_now.get<3>();
int a4=periodicity_now.get<4>();
int a5=periodicity_now.get<5>();
{
if (pseudo_mode) {
periodic(a0, a1, a2, a3, a4, a5,
J, x_range, y_range, z_range, component,
spin_tmp,
energy_tmp,
clustertype_periodic, min_bound, id,
true, false, false,
obscenely_verbose,global_parameters);
// {
// spin_periodic[make_tuple(a0,a1,a2,a3,a4,a5)]=spin_tmp;
// energy_periodic[make_tuple(a0,a1,a2,a3,a4,a5)]=energy_tmp;
// }
}
else if (basic_exact_mode){
periodic(a0, a1, a2, a3, a4, a5,
J, x_range, y_range, z_range, component,
spin_tmp,
energy_tmp,
clustertype_periodic, min_bound, id,
false, true, false,
obscenely_verbose,global_parameters);
// {
// spin_periodic[make_tuple(a0,a1,a2,a3,a4,a5)]=spin_tmp;
// energy_periodic[make_tuple(a0,a1,a2,a3,a4,a5)]=energy_tmp;
// }
}
}
}
// Notify master that the job is done
std::cout << "[SLAVE: " << mpi_world.rank()
<< "] Done with job " << job_id << ". Notifying MASTER.\n"<<endl;
status="finish";
mpi_world.send(0, 0,status);
// std::cout << "[SLAVE: " << mpi_world.rank()<< "] I have sent status back to master"<<endl;
//note here, I deliberated exclude cluster_type_periodic which may be the culprit of a lot of bad computing performance for realistic system
//send periodicity, spin_tmp and energy_tmp
// and remember to send more detail;
mpi_world.send(0, 1,spin_tmp);
std::cout << "[SLAVE: " << mpi_world.rank()<< "] I have sent spin_tmp back to master"<<endl;
mpi_world.send(0, 2,energy_tmp);
std::cout << "[SLAVE: " << mpi_world.rank()<< "] I have sent energy_tmp back to master"<<endl;
mpi_world.send(0, 3,periodicity_now);
std::cout << "[SLAVE: " << mpi_world.rank()<< "] I have sent periodicity_now back to master"<<endl;
// Check if a new job is coming
mpi_world.recv(0, 0, stop);
std::cout << "[SLAVE: " << mpi_world.rank()<< "] I have receive stop from master and stop is "<<stop<<endl;
}
std::cout << "[SLAVE: " << mpi_world.rank()<< "] I am waiting for signal whether kill_all_slave"<<endl;
broadcast(mpi_world,kill_all_slave,0);
std::cout << "[SLAVE: " << mpi_world.rank()<< "] I receive kill_all_slave signal as "<<kill_all_slave<<endl;
}
std::cout << "~~~~~~~~ Rank " << mpi_world.rank() << " is exiting ~~~~~~~~~~~\n"<<endl;
}
對於solver.cpp（對於MASTER）
void corecode(map<int, int> component,
int x_range, int y_range, int z_range,
int max_sites,
int loopnumber,
map<set<tuple<int,int,int,int,int> >, double> &J,
map<set<tuple<int,int,int,int,int> >, double> &lowerboundclustertype,
map<set<tuple<int,int,int,int,int> >, double> &upperboundclustertype,
map<tuple<int,int,int,int>,int> &cellrepresentation,
double &lower_bound, double &upper_bound,
map<tuple<int,int,int,int>,int> &unitcell,
tuple<int,int,int,int,int,int> &periodicity,
map<set<tuple<int,int,int,int,int> >, double> &J_for_proof,
std::string id,
bool pseudo_mode,
bool pseudo_mode_with_proof,
bool basic_exact_mode,
bool very_verbose,
bool obscenely_verbose,
double limit_dimension,bool new_cluster_algorithm,int use_new_pair_terms,int use_new_triplet_terms, map<tuple<int,int,int,int,int,int>, map<tuple<int,int,int,int>,int> > &map_periodicity_to_spin, bool use_level_method,bool use_weighted_dual_average ,solver_variable &global_parameters)
{
mpi::communicator mpi_world;
cout<<"\n what is global_parameters.dedicated1D: "<<global_parameters.dedicated1D<<" what is dimension==1:"<<(dimension==1)<<endl;
if (!(global_parameters.dedicated1D==true&&dimension==1)) {
// Obtain upper bound estimate on the energy
if (max_sites > 0){
vector<tuple<int,int,int,int,int,int> > periodicity_vector;
periodicity_vector.clear();
bool done=false;
cout<<"\n debug 02819374 periodicity_vector.size(): "<<periodicity_vector.size()<<endl;
bool kill_all_slave=false;
usleep(100000);
cout<<"[MASTER] I am broadcasting kill_all_slave"<<endl;
broadcast(mpi_world,kill_all_slave,0);
usleep(100000);
cout<<"[MASTER] I am broadcasting periodicity_vector"<<endl;
broadcast(mpi_world,periodicity_vector,0);
usleep(1000000);
cout<<"[MASTER] I am broadcasting J"<<endl;
broadcast(mpi_world,J,0);
usleep(1000000);
cout<<"[MASTER] I am broadcasting x_range"<<endl;
broadcast(mpi_world,x_range,0);
usleep(100000);
cout<<"[MASTER] I am broadcasting y_range"<<endl;
broadcast(mpi_world,y_range,0);
usleep(100000);
cout<<"[MASTER] I am broadcasting z_range"<<endl;
broadcast(mpi_world,z_range,0);
usleep(100000);
cout<<"[MASTER] I am broadcasting component"<<endl;
broadcast(mpi_world,component,0);
usleep(100000);
cout<<"[MASTER] I am broadcasting minbound"<<endl;
broadcast(mpi_world,min_bound,0);
usleep(100000);
// lets write something here, for loop starts here:
set<int> killed_slave;
map<int, string > status_of_slave;
// Initialize requests
unsigned int job_id = 0;
std::vector<mpi::request> reqs(mpi_world.size());
// cout<<"\n hello I am root: I am going to send to receieve status status"<<endl;
// Send initial jobs
for (unsigned int dst_rank = 1; dst_rank < mpi_world.size(); ++dst_rank) {
// Post receive request for new jobs requests by slave [nonblocking]
cout<<"[MASTER] I am i receving status_of_slave: "<<dst_rank<<endl;
reqs[dst_rank] = mpi_world.irecv(dst_rank, 0,status_of_slave[dst_rank]);
++job_id;
}
// cout<<"\n hello I am root: I finish status request"<<endl;
// Send jobs as long as there is job left
while(job_id < periodicity_vector.size()) {
bool stop;
for (unsigned int dst_rank = 1; dst_rank < mpi_world.size(); ++dst_rank) {
// Check if dst_rank is done
// cout<<"status_of_slave["<<dst_rank<<"] is "<<status_of_slave[dst_rank] <<endl;
if (reqs[dst_rank].test()) {
cout<<"[MASTER] I receive reqs[dst_rank].test() is: "<<1<<endl;
cout<<"status_of_slave["<<dst_rank<<"] is "<<status_of_slave[dst_rank] <<endl;
if(status_of_slave[dst_rank]=="finish") {
//note here, I deliberated exclude cluster_type_periodic which may be the culprit of a lot of bad computing performance for realistic system
cout<<"[MASTER] I receive finish status "<<endl;
// remember to receive more detail output;
// and update the things you want to update;
map<tuple<int,int,int,int>,int> spin_tmp;
double energy_tmp=0;
mpi_world.recv(dst_rank, 1,spin_tmp);
cout<<"[MASTER] I receive spin_tmp status "<<endl;
mpi_world.recv(dst_rank, 2,energy_tmp);
cout<<"[MASTER] I receive energy_tmp status "<<endl;
tuple<int,int,int,int,int,int> periodicity_now;
mpi_world.recv(dst_rank, 3,periodicity_now);
cout<<"[MASTER] I receive periodicity_now status "<<endl;
}
std::cout << "[MASTER] Rank " << dst_rank << " is done.\n"<<endl;
// Check if there is remaining jobs
if (job_id < periodicity_vector.size()) {
// Tell the slave that a new job is coming.
stop = false;
mpi_world.isend(dst_rank, 0, stop);
// Send the new job.
// std::cout << "[MASTER] Sending new job (" << job_id
// << ") to SLAVE " << dst_rank << ".\n"<<endl;
mpi_world.isend(dst_rank, 0, job_id);
mpi_world.isend(dst_rank, 1, min_bound);
reqs[dst_rank] = mpi_world.irecv(dst_rank, 0,status_of_slave[dst_rank]);
++job_id;
}
else {
// Send stop message to slave.
stop = true;
mpi_world.send(dst_rank, 0, stop);
killed_slave.insert(dst_rank);
}
}
}
usleep(1000);
}
std::cout << "[MASTER] Sent all jobs.\n";
// Listen for the remaining jobs, and send stop messages on completion.
bool all_done = false;
while (!all_done) {
all_done = true;
for (unsigned int dst_rank = 1; dst_rank < mpi_world.size(); ++dst_rank) {
// cout<<"[MASTER] check if Slave "<<dst_rank<<" is killed?"<<endl;
if(killed_slave.count(dst_rank)==0)
{
// cout<<"[MASTER] Slave "<<dst_rank<<" is not yet killed"<<endl;
//
// cout<<"[MASTER] last round; checking status message from slave "<<dst_rank<<endl;
if (reqs[dst_rank].test()) {
cout<<"[MASTER] last round; checking status message from slave "<<dst_rank<<endl;
cout<<"status_of_slave["<<dst_rank<<"] is "<<status_of_slave[dst_rank] <<endl;
if(status_of_slave[dst_rank]=="finish") {
//note here, I deliberated exclude cluster_type_periodic which may be the culprit of a lot of bad computing performance for realistic system
cout<<"[MASTER] I receive finish status from slave "<<dst_rank<<endl;
// remember to receive more detail output;
// and update the things you want to update;
map<tuple<int,int,int,int>,int> spin_tmp;
double energy_tmp=0;
mpi_world.recv(dst_rank, 1,spin_tmp);
cout<<"[MASTER] I receive spin_tmp status "<<endl;
mpi_world.recv(dst_rank, 2,energy_tmp);
cout<<"[MASTER] I receive energy_tmp status "<<endl;
tuple<int,int,int,int,int,int> periodicity_now;
mpi_world.recv(dst_rank, 3,periodicity_now);
cout<<"[MASTER] I receive periodicity_now status "<<endl;
spin_periodic[periodicity_now]=spin_tmp;
energy_periodic[periodicity_now]=energy_tmp;
cout<<"[MASTER] I updated spin_periodic and energy_periodic "<<endl;
{
double dump1;
findminmax(energy_periodic, min_bound, dump1);
matchnumber(energy_periodic, min_bound, min_list);
if (very_verbose){
cout << "\nUB: " << min_bound << ", LB: " << lowerbound_from_compat << "; Periodicity: ";
printvector(min_list);
cout << endl;
}
if (min_bound <= lowerbound_from_compat+1e-5*max_value_of_J) {
done=true;
}
}
cout<<"[MASTER] I updated minlist and minbound "<<endl;
}
// Tell the slave that it can exit.
bool stop = true;
mpi_world.send(dst_rank, 0, stop);
killed_slave.insert(dst_rank);
cout<<"[MASTER] I stopped slave "<<dst_rank<<endl;
}
else {
all_done = false;
}
}
}
usleep(1000);
}
std::cout << "[MASTER] Handled all jobs, killed every process.\n";
}
}
}
代碼的邏輯不應有任何問題，因爲它運行在Mac上使用16個內核完全沒問題。不過，神祕的分割發生故障時，我低至8牛仔超級計算機上運行它（有時甚至核心）...這是很神祕......
基於兩條線從外部調試代碼是不可能的。踏踏實實地安裝了一個並行調試器。繼續 - 使用它。您也可以在這裏發佈[最小，完整和可驗證示例]（http://stackoverflow.com/help/mcve）。 – Zulan
我編輯並提供了一個最小版本的代碼 – user40780