lpt.cpp

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/*
# =============================================================================
# Copyright (c) 2016 - 2021 Blue Brain Project/EPFL
#
# See top-level LICENSE file for details.
# =============================================================================
*/
 
#include <algorithm>
#include <functional>
#include <numeric>
#include <queue>
 
#if CORENRN_BUILD
#include "coreneuron/nrnconf.h"  // for size_t
#include "coreneuron/utils/nrn_assert.h"
#else
#include "oc/nrnassrt.h"
#endif
 
#include "coreneuron/utils/lpt.hpp"
 
using P = std::pair<size_t, size_t>;
 
// lpt Least Processing Time algorithm.
// Largest piece goes into least size bag.
// in: number of bags, vector of sizes
// return: a new vector of bag indices parallel to the vector of sizes.
 
std::vector<std::size_t> lpt(std::size_t nbag, std::vector<std::size_t>& pieces, double* bal) {
    nrn_assert(nbag > 0);
    nrn_assert(!pieces.empty());
 
    std::vector<P> pvec;
    for (size_t i = 0; i < pieces.size(); ++i) {
        pvec.push_back(P(i, pieces[i]));
    }
 
    auto P_comp = [](const P& a, const P& b) { return a.second > b.second; };
 
    std::sort(pvec.begin(), pvec.end(), P_comp);
 
    std::vector<std::size_t> bagindices(pieces.size());
 
    std::priority_queue<P, std::vector<P>, decltype(P_comp)> bagq(P_comp);
    for (size_t i = 0; i < nbag; ++i) {
        bagq.push(P(i, 0));
    }
 
    for (const auto& p: pvec) {
        P bagqitem = bagq.top();
        bagq.pop();
        bagindices[p.first] = bagqitem.first;
        bagqitem.second += p.second;
        bagq.push(bagqitem);
    }
 
    // load balance average/max (1.0 is perfect)
    std::vector<size_t> v(bagq.size());
    for (size_t i = 1; i < nbag; ++i) {
        v[i] = bagq.top().second;
        bagq.pop();
    }
    double b = load_balance(v);
    if (bal) {
        *bal = b;
    } else {
        printf("load balance = %g for %ld pieces in %ld bags\n", b, pieces.size(), nbag);
    }
 
    return bagindices;
}
 
double load_balance(std::vector<size_t>& v) {
    nrn_assert(!v.empty());
    std::size_t sum = std::accumulate(v.begin(), v.end(), 0);
    std::size_t max = *std::max_element(v.begin(), v.end());
    return (double(sum) / v.size()) / max;
}