load_balance.hpp

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#ifndef LOAD_BALANCE_HPP
#define LOAD_BALANCE_HPP
 
#include "timer_macro.hpp"
#include "shift.hpp"
#include "count_ptl_per_node.hpp"
#include "view_arithmetic.hpp"
#include "f0_redistribute.hpp"
 
// Used for original fortran load balancer
extern "C" void calculate_load_imbalance(int istep, int gstep, double f0_cost);
extern "C" int assess_whether_to_rebalance_load(int istep);
extern "C" void reset_cost_trackers();
extern "C" void set_weights(int* gvid0_pid, double* ptl_count, double* f0_node_cost);
 
class LoadRegion{
    public:
    enum class UpdateMethod{
        NoHistory=0,
        ExpHistory
    };
 
    private:
 
    View<double*,HostType> estimated_time_per_vertex;
    std::string region_name;
    std::vector<std::string> timer_names;
    bool verbose;
    bool model_is_initialized;
    bool model_has_history;
    double predicted_max_region_time;
    double observed_max_region_time;
    double observed_load_imbalance;
    double prediction_undershoot;
 
#ifdef USE_MPI
    MPI_Comm inter_period_comm;
    MPI_Comm period_comm;
#endif
    int n_periods;  
    int n_unique_ranks; 
    int my_period_rank;
 
    double time_accumulated;
 
    UpdateMethod update_method;
 
    void update_model_no_history(const View<int*,CLayout,HostType>& current_partition, const View<double*, HostType>& all_periods_timings){
        // Loop over ranks' timers
        for(int i=0; i<all_periods_timings.size(); i++){
            int node_offset = current_partition(i) - 1;
            int next_node_offset = current_partition(i+1) - 1;
            int nnodes = next_node_offset - node_offset;
            // Best 0th order estimate is to assume all vertices took the same time
            double time_per_node = all_periods_timings(i)/nnodes;
            for(int j=0; j<nnodes; j++){
                estimated_time_per_vertex(j + node_offset) = time_per_node;
            }
        }
    }
 
    void update_model_exp_history(const View<int*,CLayout,HostType>& current_partition, const View<double*, HostType>& all_periods_timings){
        // Loop over ranks' timers
        for(int i=0; i<all_periods_timings.size(); i++){
            // Get partition owned by this rank
            int node_offset = current_partition(i) - 1;
            int next_node_offset = current_partition(i+1) - 1;
            int nnodes = next_node_offset - node_offset;
 
            // Get average time per node for this rank
            double avg_time_per_node = all_periods_timings(i)/nnodes;
 
            // Get expected time based on existing model
            double expected_time = 0.0;
            for(int j=0; j<nnodes; j++){
                expected_time += estimated_time_per_vertex(j + node_offset);
            }
            double expected_time_per_node = expected_time/nnodes;
 
            // Difference between observed time and expected time
            double extra_time_per_node = avg_time_per_node - expected_time_per_node;
 
            for(int j=0; j<nnodes; j++){
                // Distribute extra time evenly
                constexpr double adjustment_rate = 0.5;
 
                estimated_time_per_vertex(j + node_offset) += extra_time_per_node*adjustment_rate;
            }
        }
    }
 
    double get_time_since_previous_call(){
        // Get time from camtimers (accumulated)
        double new_accumulated_time = 0.0;
 
        // Loop through all timers associated with this region, and add the accumulated time for each
        for(int i=0; i<timer_names.size(); i++){
            double accumulated_single_timer = 0;
            int ierr = GPTLget_wallclock(timer_names[i].c_str(), -1, &accumulated_single_timer);
 
            // If ierr != 0, timer is not yet defined, so the accumulated time is zero
            if(ierr != 0) accumulated_single_timer = 0.0;
 
            new_accumulated_time += accumulated_single_timer;
        }
 
        // Subtract previous accumulated time to get time spent since last update
        double time_spent = new_accumulated_time - time_accumulated;
 
        // Save new accumulated time
        time_accumulated = new_accumulated_time;
 
        return time_spent;
    }
 
    void touch_timers(){
        for(int i=0; i<timer_names.size(); i++){
            GPTLstart(timer_names[i].c_str());
            GPTLstop(timer_names[i].c_str());
        }
    }
 
    public:
 
#ifdef USE_MPI
    LoadRegion(int n_vertices, const MPI_Comm& inter_period_comm, const MPI_Comm& period_comm, UpdateMethod update_method, bool verbose, std::string region_name, std::vector<std::string> timer_names)
        : model_is_initialized(false),
          verbose(verbose),
          time_accumulated(0.0),
          inter_period_comm(inter_period_comm),
          period_comm(period_comm),
          estimated_time_per_vertex("estimated_time_per_vertex",n_vertices),
          prediction_undershoot(1.0),
          update_method(update_method),
          model_has_history(false),
          region_name(region_name),
          timer_names(timer_names)
    {
        // Touch GPTL timers to be sure they exist
        touch_timers();
 
        // Initialize timer
        reset_timer();
 
        // Get problem size from comms
        MPI_Comm_rank(period_comm, &my_period_rank);
        MPI_Comm_size(period_comm, &n_unique_ranks);
        MPI_Comm_size(inter_period_comm, &n_periods);
    }
#endif
 
    void reset_timer(){
        // Reset timer by getting the time since the previous call and suppressing the output
        double time_spent = get_time_since_previous_call();
    }
 
    double get_estimated_time_per_vertex(int i) const{
        return estimated_time_per_vertex(i);
    }
 
    View<double*,HostType> get_estimated_time_per_vertex() const{
        return estimated_time_per_vertex;
    }
 
    double get_prediction_undershoot() const{
        return prediction_undershoot;
    }
 
    double get_observed_max_region_time() const{
        return observed_max_region_time;
    }
 
    double get_observed_load_imbalance() const{
        return observed_load_imbalance;
    }
 
    bool get_model_is_initialized() const{
        return model_is_initialized;
    }
 
    // Predict performance of new partition based on model
    double get_largest_predicted_time(const View<int*,CLayout,HostType>& proposed_partition) const{
        double largest_time = 0.0;
        //int largest_time_ind = 0;
        for(int i=0; i<(proposed_partition.size()-1); i++){
            double proc_time = 0.0;
            for(int i_node=proposed_partition(i)-1; i_node<proposed_partition(i+1)-1; i_node++){
                proc_time += estimated_time_per_vertex(i_node);
            }
            if(proc_time>largest_time){
                //largest_time_ind = i;
                largest_time = proc_time;
            }
        }
        return largest_time;
    }
 
    void initialize_model(){
        reset_timer();
 
        // Model can now be used
        model_is_initialized = true;
    }
 
    void update_model(const View<int*,CLayout,HostType>& current_partition, double manual_time=-1.0){
        if(update_method==UpdateMethod::NoHistory || update_method==UpdateMethod::ExpHistory){
 
            // Allocate for timing of each
            View<double*, HostType> all_periods_timings(NoInit("all_periods_timings"), n_unique_ranks);
 
#ifdef USE_MPI
            // Get time spent since last model update
            // Time can be entered manually; it's used if positive (i.e. the input was provided)
            double time_spent_this_rank = (manual_time<0.0 ? get_time_since_previous_call()
                                                           : manual_time);
 
            // Reduce onto plane 0
            // Could try MPI_SUM rather than MPI_MAX
            double time_spent;
            if(n_periods>1){
                MPI_Reduce(&time_spent_this_rank, &time_spent, 1, MPI_DOUBLE, MPI_MAX, 0, inter_period_comm);
            }else{
                time_spent = time_spent_this_rank;
            }
 
            // Gather from all ranks in plane 0
            MPI_Gather(&time_spent, 1, MPI_DOUBLE, all_periods_timings.data(), 1, MPI_DOUBLE, 0, period_comm);
#endif
 
            if (is_rank_zero()){
                // Look at the timing prediction made last time
                predicted_max_region_time = get_largest_predicted_time(current_partition);
                if(verbose) printf("\nPredicted max time in this region was %1.5e", predicted_max_region_time);
 
                // Get max region time
                observed_max_region_time = 0.0;
                double observed_sum_region_time = 0.0;
                for(int i=0; i<all_periods_timings.size(); i++){
                    observed_max_region_time = std::max(observed_max_region_time, all_periods_timings(i));
                    observed_sum_region_time += all_periods_timings(i);
                }
                double observed_idle_region_time = observed_max_region_time*all_periods_timings.size() - observed_sum_region_time;
                observed_load_imbalance = observed_idle_region_time/observed_sum_region_time;
                if(verbose) printf("\nObserved max time in this region was %1.5e", observed_max_region_time);
                if(verbose) printf("\n - Load imbalance (T_idle/T_work): %1.2f%%", observed_load_imbalance*100);
 
                // Take the max here so that there is never an assumed overshoot
                if(predicted_max_region_time!=0.0){ // If time is zero, there hasn't been a prediction yet
                    prediction_undershoot = std::max(observed_max_region_time/predicted_max_region_time, 1.0);
                }
                if(verbose) printf("\nThe prediction undershot by a factor of %1.5e", observed_max_region_time/predicted_max_region_time);
 
                if(update_method==UpdateMethod::NoHistory){
                    update_model_no_history(current_partition, all_periods_timings);
                }else if(update_method==UpdateMethod::ExpHistory){
                    if(model_has_history){
                        update_model_exp_history(current_partition, all_periods_timings);
                    }else{
                        update_model_no_history(current_partition, all_periods_timings);
                        model_has_history = true;
                    }
                }
            }
        }else{
            exit_XGC("Error: Update method not available\n");
        }
    }
};
 
class LoadBalance{
    public:
    enum class WeightingAlgorithm{
        SingleRegionBalance=0,
        ParticleBalance,
        Fortran,
        Default
    };
 
    enum class ConstraintOption{
        ParticleCount=0
    };
 
    enum class ReweightOption{
        Always=0,
        IfBetter
    };
 
    private:
 
    WeightingAlgorithm default_weighting_algorithm;
 
    std::vector<LoadRegion> regions;
 
    View<int*,CLayout,HostType> proposed_partition; 
 
#ifdef USE_MPI
    MPI_Comm comm;
#endif
 
    double threshold_to_rebalance;
    bool verbose;
 
    double constraint1_max;
 
    double get_even_division(const View<double*,HostType>& input, int n) const{
        double total = 0.0;
        Kokkos::parallel_reduce("sum", Kokkos::RangePolicy<HostExSpace>(0,input.size()), [=]( const int i, double& l_total){
            l_total += input(i);
        }, total);
 
        return (total/n);
    }
 
 
    bool greedily_fill_partition(const View<double*,HostType>& weight, const View<double*,HostType>& constraint1, double target_weight_per_rank){
        int nnode = weight.size();
        int nproc = proposed_partition.size()-1;
 
        // Start from rank 0 of the plane; assign nodes until it meets the desired weight per rank, then move to next rank
        double constraint1_in_this_rank = 0.0;
        double weight_in_this_rank = 0.0;
        proposed_partition(0) = 1; // 1-indexed
        int pid = 0;
        bool assign_one_node_per_proc = false;
        for(int i_node = 0; i_node<nnode; i_node++){
            // Since every rank needs at last one node, switch to assigning at every
            // iteration if there are only as many nodes left as ranks
            if(nnode-i_node==nproc-pid) assign_one_node_per_proc = true;
 
            // Check if a criterion is met to consider the rank sufficiently loaded
            bool rank_is_loaded = false;
 
            // Criterion 1: We are out of nodes
            if(assign_one_node_per_proc) rank_is_loaded = true;
 
            // Criterion 2: We have hit a constraint
            if(constraint1_in_this_rank>=constraint1_max) rank_is_loaded = true;
 
            // Criterion 3: We have loaded the rank with an even amount of work
            if(weight_in_this_rank>=target_weight_per_rank) rank_is_loaded = true;
 
            // If there are enough particles assigned to this rank, move to the next rank
            if(rank_is_loaded){
                proposed_partition(pid+1) = i_node+1; // 1-indexed
                if(verbose) printf("\nRank %d is loaded (Nodes %d-%d); weight=%1.4e, ptl*nplanes=%d", pid, proposed_partition(pid), proposed_partition(pid+1)-1, weight_in_this_rank, int(constraint1_in_this_rank));
                constraint1_in_this_rank = 0.0;
                weight_in_this_rank = 0.0;
                pid++;
                if(pid==nproc-1) break;
            }
            constraint1_in_this_rank += constraint1(i_node);
            weight_in_this_rank += weight(i_node);
        }
 
        // Last value will always be nnode+1
        proposed_partition(nproc) = nnode+1;
 
        // Check n_ptl in final rank
        constraint1_in_this_rank = 0.0;
        for(int i_node=proposed_partition(nproc-1)-1; i_node<nnode; i_node++){
            constraint1_in_this_rank += constraint1(i_node);
        }
        if(constraint1_in_this_rank>=constraint1_max){
            // Return false if the constraint was not met
            return false;
        }else{
            return true;
        }
    }
 
    // Predict performance of new partition based on model
    double get_largest_predicted_time(const View<int*,CLayout,HostType>& partition, const View<double*,HostType>& weight) const{
        double largest_time = 0.0;
        //int largest_time_ind = 0;
        for(int i=0; i<(partition.size()-1); i++){
            double proc_time = 0.0;
            for(int i_node=partition(i)-1; i_node<partition(i+1)-1; i_node++){
                proc_time += weight(i_node);
            }
            if(proc_time>largest_time){
                //largest_time_ind = i;
                largest_time = proc_time;
            }
        }
        return largest_time;
    }
 
    void one_weight_balance(const View<double*,HostType>& weight, const View<double*,CLayout,HostType> constraint1){
        int nproc = proposed_partition.size()-1;
 
        // Ideally, each rank would get the same amount of work, i.e. the average
        double ideal_weight_per_rank = get_even_division(weight, nproc);
 
        // Make initial attempt, targeting even distribution of work
        bool meets_constraints = greedily_fill_partition(weight, constraint1, ideal_weight_per_rank);
 
        if(!meets_constraints){
            // An equal work distribution cannot be assigned due to a constraint.
            // First, determine the weights if only the constraint were followed
            meets_constraints = greedily_fill_partition(constraint1, constraint1, get_even_division(constraint1, nproc));
            if(!meets_constraints) exit_XGC("\nUnexpected issue in load balance: constraint-based partition doesn't satisfy constraints\n");
 
            // This partition meets the constraints, but the proposed timing is likely unacceptable
            double upper_limit_weight_per_rank = get_largest_predicted_time(proposed_partition, weight);
            if(verbose) printf("The ideal distribution (%1.3e) does not satisfy constraints. This distribution (%1.3e) does.\n", ideal_weight_per_rank, upper_limit_weight_per_rank);
 
            // Bisect the difference between the original target time and the upper limit, and see if that satisfies the constraints
            // Continue until the load balance has been minimized to a precision based on the the original desired time
            const double desired_precision = 0.01; // Fraction of imbalance that's worth even checking whether it can be removed
            double desired_step_size = ideal_weight_per_rank*desired_precision;
            double step_size = upper_limit_weight_per_rank - ideal_weight_per_rank;
            double compromise_weight_per_rank = upper_limit_weight_per_rank;
            if(verbose) printf("\nEmploying a binary search to narrow down on the best option.");
            while(step_size>desired_step_size){
                // Halve the size of the step
                step_size /= 2;
 
                if(meets_constraints){
                    // Try reducing the load imbalance since constraints were met
                    compromise_weight_per_rank -= step_size;
                }else{
                    // Try raising the load imbalance since constraints were broken
                    compromise_weight_per_rank += step_size;
                }
 
                // Get new partition
                meets_constraints = greedily_fill_partition(weight, constraint1, compromise_weight_per_rank);
                if(verbose) printf("\n  Stepped by %1.3e to %1.3e. The new partition does%s meet constraints.", step_size, compromise_weight_per_rank, meets_constraints?"" : "nt");
            }
            // In case we end at a partition that fails
            while(!meets_constraints){
                compromise_weight_per_rank += step_size;
                meets_constraints = greedily_fill_partition(weight, constraint1, compromise_weight_per_rank);
                if(verbose) printf("\n  Stepped by %1.3e UP to %1.3e. The new partition does%s meet constraints.", step_size, compromise_weight_per_rank, meets_constraints?"" : "nt");
            }
            if(verbose) printf("\n");
        }
    }
 
    // TODO
    // Evaluate overall model performance
    // MAIN_LOOP time vs sum of regions (% coverage)
    //
 
    /* This function assesses the proposed partition and decides whether it is worth switching to.
     * The simplest formulation is to recommend the new partition if the new partition is predicted to be faster
     * than the existing one. There is also a factor to account for historical inaccuracy of the previous estimate.
     * A future option would be to require a certain level of improvement before repartitioning. */
    bool recommend_proposed_partition(){
        // Total time spent in regions (assuming that global barriers demarcate them):
        double observed_max_all_rgns_time = 0.0;
        for(int i=0; i<regions.size(); i++){
            observed_max_all_rgns_time += regions[i].get_observed_max_region_time();
        }
 
        // Total predicted time, accounting for observed prediction undershoot for each region
        double predicted_max_all_rgns_time = 0.0;
        for(int i=0; i<regions.size(); i++){
            predicted_max_all_rgns_time += regions[i].get_largest_predicted_time(proposed_partition)*regions[i].get_prediction_undershoot();
        }
 
        double fractional_improvement = 1.0 - predicted_max_all_rgns_time/observed_max_all_rgns_time;
 
        if(verbose){
            printf("\nDetermining whether to adopt the proposed partition:");
            printf("\n Observed time with current partition was: %1.3e", observed_max_all_rgns_time);
            printf("\n Predicted time with proposed partition, adjusting for historical undershoot (of Rgn 0: %1.3e): %1.3e", regions[0].get_prediction_undershoot(), predicted_max_all_rgns_time);
            printf("\n Adopted if Fractional improvement of adjusted prediction (%1.3e) exceeds specified threshold (%1.3e)\n", fractional_improvement, threshold_to_rebalance);
        }
 
        // Recommend the new partition if the predicted time is less than the time observed with the current partition
        if(fractional_improvement > threshold_to_rebalance){
            return true;
        }else{
            return false;
        }
    }
 
    void print_new_partition(){
        // Print new partition
        for(int i = 0; i<proposed_partition.size()-1; i++){
            int nnodes_for_proc = proposed_partition(i+1) - proposed_partition(i);
            printf("process %d : nnodes = %d; range = (%d - %d)\n", i, nnodes_for_proc, proposed_partition(i), proposed_partition(i+1)-1);
        }
    }
 
    void update_model(const View<int*,CLayout,HostType>& current_partition){
        for(int i=0; i<regions.size(); i++){
            regions[i].update_model(current_partition);
        }
    }
 
    // Overload if providing timings manually rather than using camtimers. This is used for testing.
    void update_model(const View<int*,CLayout,HostType>& current_partition, const std::vector<double>& manual_times){
        for(int i=0; i<regions.size(); i++){
            regions[i].update_model(current_partition, manual_times[i]);
        }
    }
 
    void initialize_model(){
        for(int i=0; i<regions.size(); i++){
            regions[i].initialize_model();
        }
    }
 
    bool model_is_initialized(){
        bool is_initialized=true;
        for(int i=0; i<regions.size(); i++){
            is_initialized = (is_initialized && regions[i].get_model_is_initialized());
        }
        return is_initialized;
    }
 
    void propose_new_partition(const Kokkos::View<double*,Kokkos::LayoutRight,HostType>& ptl_count, WeightingAlgorithm weighting_algorithm){
        auto constraint1 = ptl_count; // Constrain based on particle count (memory)
        if(weighting_algorithm == WeightingAlgorithm::SingleRegionBalance){
            if(regions.size()!=1) exit_XGC("Error: Load balancing is currently single-region only\n");
            one_weight_balance(regions[0].get_estimated_time_per_vertex(), constraint1);
        }else if(weighting_algorithm == WeightingAlgorithm::ParticleBalance){
            one_weight_balance(ptl_count, constraint1);
        }
 
        if(verbose){
            printf("\nNewly PROPOSED partition:\n");
            print_new_partition();
        }
    }
 
    void set_new_partition(const Simulation<DeviceType>& sml, const Grid<DeviceType>& grid, const MagneticField<DeviceType>& magnetic_field,
                   const VelocityGrid& vgrid, Plasma& plasma, DomainDecomposition<DeviceType>& pol_decomp, WeightingAlgorithm weighting_algorithm){
        if(weighting_algorithm==WeightingAlgorithm::Fortran){
            // In the fortran algorithm the new distribution is calculated and set here
            // Count number of particles belonging to each node
            Kokkos::View<double**,Kokkos::LayoutRight,HostType> ptl_count = count_ptl_per_node_elec_main_ion(grid, magnetic_field, plasma, sml.electron_on);
 
            if(pol_decomp.gvid0_pid_h.size()==2){
                // Skip set_weights if there is only one rank per plane
                pol_decomp.gvid0_pid_h(0) = 1;
                pol_decomp.gvid0_pid_h(1) = pol_decomp.nnodes_on_plane + 1;
            }else{
#ifndef NO_FORTRAN_MODULES
                TIMER("SET_WEIGHTS",
                    set_weights(pol_decomp.gvid0_pid_h.data(), ptl_count.data(), plasma.f0_node_cost.data()) );
#endif
            }
        }else{
#ifdef USE_MPI
            // Broadcast proposal to all ranks
            MPI_Bcast(proposed_partition.data(), proposed_partition.size(), MPI_INT, 0, comm);
#endif
 
            // Copy proposal to pol_decomp
            Kokkos::deep_copy(pol_decomp.gvid0_pid_h, proposed_partition);
 
            if(is_rank_zero()){
                printf("\nNEW PARTITION:\n");
                print_new_partition();
            }
        }
 
        // Update bounds and device copy of the decomposition array
        pol_decomp.update_pol_decomp();
        GPTLstart("update_flux_surf");
        pol_decomp.update_flux_surf(grid.flux_surfaces_h);
        GPTLstop("update_flux_surf");
    }
 
    void redistribute_load(const Simulation<DeviceType>& sml, const Grid<DeviceType>& grid, const MagneticField<DeviceType>& magnetic_field,
                   const VelocityGrid& vgrid, Plasma& plasma, DomainDecomposition<DeviceType>& pol_decomp,
                   const View<int*,CLayout,HostType>& old_partition){
        if (plasma.f0_grid()){
            // Move f information to correct MPI rank after decomposition update
            TIMER("F0_REDISTRIBUTE",
                f0_redistribute(plasma, pol_decomp, grid, magnetic_field, vgrid, old_partition) );
        }
 
        // Shift particles to correct MPI rank after decomposition update
        TIMER("SHIFT_R",
            shift_all_species(plasma, grid, magnetic_field, pol_decomp) );
    }
 
    bool will_rebalance(ReweightOption reweight_option, WeightingAlgorithm weighting_algorithm, int istep, int gstep, double f0_cost){
        if(reweight_option==ReweightOption::Always){
            // No need to check if model is an improvement
            return true;
        }else{
            if(weighting_algorithm==WeightingAlgorithm::Fortran){
#ifndef NO_FORTRAN_MODULES
                // Calculate load imbalance
                calculate_load_imbalance(istep, gstep, f0_cost);
 
                // Assess if load imbalance merits a rebalancing
                int should_rebalance_int = assess_whether_to_rebalance_load(istep);
 
                // Reset fortran cost trackers
                reset_cost_trackers();
 
                return (should_rebalance_int==1);
#else
                return false;
#endif
            }else{
                // Evaluate the proposed partition on rank 0
                bool should_rebalance;
                if(weighting_algorithm==WeightingAlgorithm::ParticleBalance){
                    // Since ParticleBalance doesn't contain an internal model, cant evaluate the partition
                    // so just always approve, for now.
                    // Probably better to have the model updatable by something other than time so it can still
                    // be assessed in this mode
                    return true;
                }else{
                    if(is_rank_zero()) should_rebalance = recommend_proposed_partition();
                    // Convert to int because MPI_C_BOOL documentation is confusing
                    int should_rebalance_int = (should_rebalance ? 1 : 0);
#ifdef USE_MPI
                    MPI_Bcast(&should_rebalance_int, 1, MPI_INT, 0, comm);
#endif
                    return (should_rebalance_int==1);
                }
            }
        }
    }
 
    public:
 
    LoadBalance(){}
 
    LoadBalance(NLReader::NamelistReader& nlr, const DomainDecomposition<DeviceType>& pol_decomp, bool sync_planes = true)
      : proposed_partition(NoInit("proposed_partition"), pol_decomp.gvid0_pid_h.layout())
#ifdef USE_MPI
        , comm(sync_planes ? pol_decomp.mpi.comm : pol_decomp.mpi.plane_comm)
#endif
    {
 
        nlr.use_namelist("load_balance_param", NLReader::NamelistReader::Optional);
        double max_mem_redist_gb = nlr.get<double>("max_mem_redist_gb", 10.0); // Sets the maximum amount of memory per rank that can be allocated to particles, in gigabytes.
        std::string weighting_algorithm_str = nlr.get<std::string>("weighting_algorithm", "Fortran"); // Load balancing method. "Fortran" is the default since the newer methods are experimental. "ParticleBalance" does not incorporate timing data, but only tries to equally distribute particles. "SingleRegionBalance" currently tries to balance the push.
        threshold_to_rebalance = nlr.get<double>("threshold_to_rebalance", 0.02); // How much better the projected timing of a proposed load redistribution must be than the current distribution in order to adopt the new one. e.g. 0.02 sets a threshold of 2% better.
        verbose = nlr.get<bool>("verbose", false); // Verbose output of the internal load distribution model and load balancing decisions.
        std::string update_method_str = nlr.get<std::string>("update_method", "NoHistory"); // Methods for updating internal model of load distribution. "NoHistory" uses only the most recent time step, while "ExpHistory" averages the existing model with the new step's timing information. Does not apply when weighting_algorithm is "Fortran".
 
        // Simple model for memory usage from load balance - just particles
        double max_n_ptl_on_rank = max_mem_redist_gb*1024*1024*1024/80.0; // ~ 80 B per particle
 
        if(weighting_algorithm_str=="Fortran"){
            default_weighting_algorithm = WeightingAlgorithm::Fortran;
        }else if(weighting_algorithm_str=="ParticleBalance"){
            default_weighting_algorithm = WeightingAlgorithm::ParticleBalance;
        }else if(weighting_algorithm_str=="SingleRegionBalance"){
            default_weighting_algorithm = WeightingAlgorithm::SingleRegionBalance;
        }else{
            exit_XGC("\nError: weighting_algorithm input not valid.");
        }
 
#ifdef USE_MPI
        // If planes are sync'd, use intpl_comm to coordinate them; otherwise, use MPI_COMM_SELF
        MPI_Comm inter_period_comm = sync_planes ? pol_decomp.mpi.intpl_comm : MPI_COMM_SELF;
 
        // Set up load balance regions
        if(default_weighting_algorithm == WeightingAlgorithm::SingleRegionBalance){
            LoadRegion::UpdateMethod update_method;
            if(update_method_str=="NoHistory"){
                update_method = LoadRegion::UpdateMethod::NoHistory;
            }else if(update_method_str=="ExpHistory"){
                update_method = LoadRegion::UpdateMethod::ExpHistory;
            }else{
                exit_XGC("\nError: update_method input not valid.");
            }
 
            regions.push_back(LoadRegion(pol_decomp.nnodes_on_plane, inter_period_comm, pol_decomp.mpi.plane_comm, update_method, verbose,
                                         "Push",
                                         {"ipc1:PUSHE",
                                          "ipc1:PUSHI",
                                          "ipc2:PUSHE",
                                          "ipc2:PUSHI"}));
        }
 
        // Hard-code for now
        ConstraintOption constraint_option = ConstraintOption::ParticleCount;
 
        if(constraint_option == ConstraintOption::ParticleCount){
            // Normalize by n_planes since we are using the sum of planes as the constraint
            int n_periods;
            MPI_Comm_size(inter_period_comm, &n_periods);
            constraint1_max = max_n_ptl_on_rank*n_periods;
        }else{
            exit_XGC("\nError: ParticleConstraint is only available ConstraintOption.\n");
        }
#endif
    }
 
    void rebalance(const Simulation<DeviceType>& sml, const Grid<DeviceType>& grid, const MagneticField<DeviceType>& magnetic_field,
                   const VelocityGrid& vgrid, Plasma& plasma, DomainDecomposition<DeviceType>& pol_decomp, int istep, int gstep, ReweightOption reweight_option,
                   WeightingAlgorithm weighting_algorithm = WeightingAlgorithm::Default){
 
        // Skip rebalancing if poloidal decomposition is turned off. Normally we should put the function in
        // an if statement rather than using return; kept here in case we might want some balancing for some reason
        // even without poloidal decomposition
        if(pol_decomp.pol_decomp==false) return;
 
        // Rebalance is trivial if there is only one rank on a plane (generalize this from no-MPI case)
#ifdef USE_MPI
 
        if(weighting_algorithm == WeightingAlgorithm::Default){
            weighting_algorithm = default_weighting_algorithm;
        }
 
        if(default_weighting_algorithm==WeightingAlgorithm::Fortran){
            // Override input algorithm; if the default is Fortran, it should always use the fortran load balancer
            weighting_algorithm = default_weighting_algorithm;
        }
 
        if(is_rank_zero() && verbose){
            if(weighting_algorithm==WeightingAlgorithm::Fortran) printf("\nLoad balance called with Fortran algorithm\n");
            else if(weighting_algorithm==WeightingAlgorithm::ParticleBalance) printf("\nLoad balance called with Ptl algorithm\n");
            else if(weighting_algorithm==WeightingAlgorithm::SingleRegionBalance) printf("\nLoad balance called with SingleRegion algorithm\n");
        }
 
        GPTLstart("REBALANCE");
        if(weighting_algorithm!=WeightingAlgorithm::Fortran){
            GPTLstart("PTL_COUNT");
            plasma.for_all_nonadiabatic_species([&](Species<DeviceType>& species){
                long long int total_n_ptl = species.get_total_n_ptl();
                int max_n_ptl = species.get_max_n_ptl();
                double avg_n_ptl = (double)(total_n_ptl)/pol_decomp.mpi.nranks;
                double max_ratio = max_n_ptl/avg_n_ptl;
                if(is_rank_zero()) printf("  Species %d: (max/avg ptl per rank = %1.2f); total n_ptl = %lld\n", species.idx, max_ratio, total_n_ptl);
            }, Plasma::NoDevicePtl);
            GPTLstop("PTL_COUNT");
 
            GPTLstart("PTL_COUNT_PER_NODE");
            auto ptl_count = count_all_ptl_per_node(grid, magnetic_field, plasma);
            GPTLstop("PTL_COUNT_PER_NODE");
 
            if(weighting_algorithm!=WeightingAlgorithm::ParticleBalance){
                if(model_is_initialized()){
                    // Update the model with the latest timing
                    TIMER("LOAD_BAL_UPDATE",
                        update_model(pol_decomp.gvid0_pid_h) );
                }else{
                    if(is_rank_zero() && verbose) printf("Initializing timing model, no partition proposal yet.");
                    initialize_model();
                    GPTLstop("REBALANCE");
                    return;
                }
            }
 
            // With the updated model, proposed a new partition
            GPTLstart("LOAD_BAL_NEW_PART");
            if (is_rank_zero()) propose_new_partition(ptl_count, weighting_algorithm);
            GPTLstop("LOAD_BAL_NEW_PART");
        }
 
        GPTLstart("LOAD_BAL_REBAL");
        double f0_cost = plasma.f0_grid() ? sum_view(plasma.f0_node_cost) : 0.0;
        if(will_rebalance(reweight_option, weighting_algorithm, istep, gstep, f0_cost)){
            // Save the old partition, it is used to save some communication in the f0 redistribution
            View<int*,CLayout,HostType> old_partition(NoInit("old_partition"), pol_decomp.gvid0_pid_h.layout());
            Kokkos::deep_copy(old_partition, pol_decomp.gvid0_pid_h);
 
            // Update pol_decomp
            TIMER("LOAD_BAL_SET_NEW",
                set_new_partition(sml, grid, magnetic_field, vgrid, plasma, pol_decomp, weighting_algorithm) );
 
            // Update particles and f0
            TIMER("LOAD_BAL_REDIST",
                redistribute_load(sml, grid, magnetic_field, vgrid, plasma, pol_decomp, old_partition) );
        }
        GPTLstop("LOAD_BAL_REBAL");
        GPTLstop("REBALANCE");
#endif
    }
 
 
    // More generic rebalance
    void rebalance(DomainDecomposition<DeviceType>& pol_decomp, const View<double*,CLayout,HostType>& constraint, const std::vector<double>& timings, double& load_imbalance, View<double*,HostType>& model_belief){
#ifdef USE_MPI
        update_model(pol_decomp.gvid0_pid_h, timings);
 
        load_imbalance = regions[0].get_observed_load_imbalance();
        model_belief = regions[0].get_estimated_time_per_vertex();
 
        // Propose new partition
        if (is_rank_zero()) propose_new_partition(constraint, default_weighting_algorithm);
 
        double f0_cost = 0.0;
        int istep = 0;
        int gstep = 0;
        if(will_rebalance(ReweightOption::IfBetter, default_weighting_algorithm, istep, gstep, f0_cost)){
            // Broadcast proposal to all ranks
            MPI_Bcast(proposed_partition.data(), proposed_partition.size(), MPI_INT, 0, comm);
 
            // Copy proposal to pol_decomp
            Kokkos::deep_copy(pol_decomp.gvid0_pid_h, proposed_partition);
 
            // Update bounds and device copy of the decomposition array
            pol_decomp.update_pol_decomp();
 
            // If there were a real load, redistribute it here
        }
#endif
    }
};
 
#endif