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bench.cpp
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#include <iostream>
#include <chrono>
#include <stdexcept>
#include <typeinfo>
#include <string>
#include <fstream>
#include <tuple>
#include <vector>
#include <array>
#include <algorithm>
// std::filesystem was added in C++17, but was still experimental in C++14
#if __cplusplus >= 201700L
#include <filesystem>
namespace fs = std::filesystem;
#else
#include <experimental/filesystem>
namespace fs = std::experimental::filesystem;
#endif
#include "json.hpp"
#include "graph_io.h"
#ifdef GRIDCUT_IS_AVAILABLE
#include "grid_cut/GridGraph_2D_4C.h"
#include "grid_cut/GridGraph_2D_8C.h"
#include "grid_cut/GridGraph_3D_6C.h"
#include "grid_cut/GridGraph_3D_26C.h"
#include "grid_cut/GridGraph_2D_4C_MT.h"
#include "grid_cut/GridGraph_3D_6C_MT.h"
#endif
#include "bk/graph.h"
#include "nbk/graph.h"
#include "reimpls/mbk.h"
#include "reimpls/mbk_r.h"
#include "reimpls/liusun.h"
#include "reimpls/parallel_ibfs.h"
#include "reimpls/eibfs_i.h"
#include "reimpls/eibfs_i_nr.h"
#include "reimpls/parallel_pr.h"
#include "reimpls/strandmarkkahl.h"
#include "ibfs/ibfs.h"
#include "reimpls/hpf.h"
#include "hi_pr/hi_pr.h"
#include "sppr/maxFlow.h"
#if PARD_IS_AVAILABLE
#include <cstdlib>
#include "d_maxflow/parallel_ARD1.h"
#include "d_maxflow/dimacs_parser.h"
#include "d_maxflow/region_graph.h"
#include "d_maxflow/region_splitter2.h"
#endif
#include "reimpls/robin_hood.h"
using Duration = std::chrono::duration<double>;
static const auto now = std::chrono::steady_clock::now;
using json = nlohmann::json;
struct Vec3i {
int x;
int y;
int z;
Vec3i() = default;
Vec3i(int x, int y, int z) : x(x), y(y), z(z) {}
};
Vec3i id2vec(int i, int width, int height)
{
return Vec3i(
(i % (width * height)) % width,
(i % (width * height)) / width,
i / (width * height)
);
};
enum Algorithm {
// Serial algorithms
ALGO_BK,
ALGO_NBK,
ALGO_MBK,
ALGO_MBK2,
ALGO_EIBFS,
ALGO_EIBFS2,
ALGO_EIBFS_OLD,
ALGO_HPF,
ALGO_HPF_HF,
ALGO_HPF_HL,
ALGO_HPF_LF,
ALGO_HPF_LL,
ALGO_HI_PR,
ALGO_GRIDCUT,
// Parallel algorithms
ALGO_PMBK,
ALGO_PPR,
ALGO_PSK,
ALGO_PARD,
ALGO_PEIBFS,
ALGO_GRIDCUT_MT,
// Dummy just to check loading and output
ALGO_DUMMY
};
enum FileType {
FTYPE_DIMACS,
FTYPE_BBK,
FTYPE_BQ
};
enum GridType {
GRID_TYPE_NO_GRID = 0,
GRID_TYPE_2D_4C,
GRID_TYPE_2D_8C,
GRID_TYPE_3D_6C,
GRID_TYPE_3D_26C
};
struct BenchConfig {
std::string bench_name;
TypeCode cap_type;
TypeCode term_type;
TypeCode flow_type;
TypeCode index_type;
Algorithm algo;
int num_run;
int num_threads;
};
struct DataConfig {
std::string bench_name;
TypeCode nbor_cap_type;
TypeCode term_cap_type;
std::string file_name;
FileType file_type;
GridType grid_type;
// These values should only be read if grid_type is not GRID_TYPE_NO_GRID
size_t grid_width;
size_t grid_height;
size_t grid_depth;
};
#define SWITCH_ON_SIGNED_TYPE(type, name, ...) switch (type) { \
/*case TYPE_INT8: { using name = int8_t; __VA_ARGS__ } break;*/ \
/*case TYPE_INT16: { using name = int16_t; __VA_ARGS__ } break;*/ \
case TYPE_INT32: { using name = int32_t; __VA_ARGS__ } break; \
case TYPE_INT64: { using name = int64_t; __VA_ARGS__ } break; \
/*case TYPE_FLOAT: { using name = float; __VA_ARGS__ } break;*/ \
/*case TYPE_DOUBLE: { using name = double; __VA_ARGS__ } break;*/ \
default: throw std::runtime_error("Invalid type code for signed type."); \
}
#define SWITCH_ON_INDEX_TYPE(type, name, ...) switch (type) { \
/*case TYPE_UINT16: { using name = uint16_t; __VA_ARGS__ } break;*/ \
/*case TYPE_INT16: { using name = int16_t; __VA_ARGS__ } break;*/ \
case TYPE_UINT32: { using name = uint32_t; __VA_ARGS__ } break; \
case TYPE_INT32: { using name = int32_t; __VA_ARGS__ } break; \
case TYPE_INT64: { using name = int64_t; __VA_ARGS__ } break; \
case TYPE_UINT64: { using name = int64_t; __VA_ARGS__ } break; \
default: throw std::runtime_error("Invalid type code for index."); \
}
#define SWITCH_ON_FLOW_TYPE(type, name, ...) switch (type) { \
case TYPE_INT64: { using name = int64_t; __VA_ARGS__ } break; \
/*case TYPE_DOUBLE: { using name = double; __VA_ARGS__ } break;*/ \
default: throw std::runtime_error("Invalid type code for flow."); \
}
#define RUN_BENCH_FUNC(data_config, bench_config, data, func) do { \
SWITCH_ON_SIGNED_TYPE(bench_config.cap_type, CapType, \
SWITCH_ON_FLOW_TYPE(bench_config.flow_type, FlowType, \
SWITCH_ON_INDEX_TYPE(bench_config.index_type, IndexType, \
func<CapType, CapType, FlowType, IndexType, decltype(data)>(data_config, bench_config, data);))) \
} while (false)
TypeCode code_from_string(const std::string& str);
Algorithm algo_from_string(const std::string& str);
const char* algo_to_string(Algorithm algo);
FileType ftype_from_string(const std::string& str);
bool algo_is_parallel(Algorithm algo);
bool algo_requires_grid(Algorithm algo);
std::vector<BenchConfig> gen_bench_configs(json config);
std::vector<DataConfig> gen_data_configs(json config);
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double> bench_bk(BenchConfig config, const Data& data)
{
// Build graph.
auto build_begin = now();
bk::Graph<Cap, Term, Flow> graph(data.num_nodes, data.neighbor_arcs.size());
graph.add_node(data.num_nodes);
for (const auto& tarc : data.terminal_arcs) {
graph.add_tweights(tarc.node, tarc.source_cap, tarc.sink_cap);
}
for (const auto& narc : data.neighbor_arcs) {
graph.add_edge(narc.i, narc.j, narc.cap, narc.rev_cap);
}
Duration build_dur = now() - build_begin;
// Solve graph.
auto solve_begin = now();
auto flow = graph.maxflow();
Duration solve_dur = now() - solve_begin;
return std::make_tuple(flow, build_dur.count(), solve_dur.count());
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double> bench_nbk(BenchConfig config, const Data& data)
{
// Build graph.
auto build_begin = now();
nbk::Graph<Cap, Term, Flow> graph(data.num_nodes, data.neighbor_arcs.size());
graph.add_node(data.num_nodes);
for (const auto& tarc : data.terminal_arcs) {
graph.add_tweights(tarc.node, tarc.source_cap, tarc.sink_cap);
}
for (const auto& narc : data.neighbor_arcs) {
graph.add_edge(narc.i, narc.j, narc.cap, narc.rev_cap);
}
Duration build_dur = now() - build_begin;
// Solve graph.
auto solve_begin = now();
auto flow = graph.maxflow();
Duration solve_dur = now() - solve_begin;
return std::make_tuple(flow, build_dur.count(), solve_dur.count());
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double> bench_mbk(BenchConfig config, const Data& data)
{
// Build graph.
auto build_begin = now();
reimpls::Graph<Cap, Term, Flow, Index, Index> graph(data.num_nodes, data.neighbor_arcs.size());
graph.add_node(data.num_nodes);
for (const auto& tarc : data.terminal_arcs) {
graph.add_tweights(tarc.node, tarc.source_cap, tarc.sink_cap);
}
for (const auto& narc : data.neighbor_arcs) {
graph.add_edge(narc.i, narc.j, narc.cap, narc.rev_cap, false);
}
Duration build_dur = now() - build_begin;
// Solve graph.
auto solve_begin = now();
auto flow = graph.maxflow();
Duration solve_dur = now() - solve_begin;
return std::make_tuple(flow, build_dur.count(), solve_dur.count());
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double> bench_mbk2(BenchConfig config, const Data& data)
{
// Build graph.
auto build_begin = now();
reimpls::Graph2<Cap, Term, Flow, Index, Index> graph(data.num_nodes, data.neighbor_arcs.size());
graph.add_node(data.num_nodes);
for (const auto& tarc : data.terminal_arcs) {
graph.add_tweights(tarc.node, tarc.source_cap, tarc.sink_cap);
}
for (const auto& narc : data.neighbor_arcs) {
graph.add_edge(narc.i, narc.j, narc.cap, narc.rev_cap, false);
}
graph.init_maxflow();
Duration build_dur = now() - build_begin;
// Solve graph.
auto solve_begin = now();
auto flow = graph.maxflow();
Duration solve_dur = now() - solve_begin;
return std::make_tuple(flow, build_dur.count(), solve_dur.count());
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double> bench_eibfs(BenchConfig config, const Data& data)
{
using Ibfs = reimpls::IBFSGraph<Cap, Term, Flow, uint32_t, Index>;
// Build graph.
auto build_begin = now();
Ibfs graph(data.num_nodes, data.neighbor_arcs.size());
for (const auto& tarc : data.terminal_arcs) {
graph.addNode(tarc.node, tarc.source_cap, tarc.sink_cap);
}
for (const auto& narc : data.neighbor_arcs) {
graph.addEdge(narc.i, narc.j, narc.cap, narc.rev_cap);
}
graph.initGraph();
Duration build_dur = now() - build_begin;
// Solve graph.
auto solve_begin = now();
auto flow = graph.computeMaxFlow();
Duration solve_dur = now() - solve_begin;
return std::make_tuple(flow, build_dur.count(), solve_dur.count());
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double> bench_eibfs2(BenchConfig config, const Data& data)
{
using Ibfs = reimpls::IBFSGraph2<Cap, Term, Flow>;
// Build graph.
auto build_begin = now();
Ibfs graph(data.num_nodes, data.neighbor_arcs.size());
for (const auto& tarc : data.terminal_arcs) {
graph.addNode(tarc.node, tarc.source_cap, tarc.sink_cap);
}
for (const auto& narc : data.neighbor_arcs) {
graph.addEdge(narc.i, narc.j, narc.cap, narc.rev_cap);
}
graph.initGraph();
Duration build_dur = now() - build_begin;
// Solve graph.
auto solve_begin = now();
auto flow = graph.computeMaxFlow();
Duration solve_dur = now() - solve_begin;
return std::make_tuple(flow, build_dur.count(), solve_dur.count());
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double> bench_eibfs_old(BenchConfig config, const Data& data)
{
using Ibfs = ibfs::IBFSGraph<Cap, Term, Flow>;
// Build graph.
auto build_begin = now();
Ibfs graph(Ibfs::IB_INIT_FAST);
graph.initSize(data.num_nodes, data.neighbor_arcs.size());
for (const auto& tarc : data.terminal_arcs) {
graph.addNode(tarc.node, tarc.source_cap, tarc.sink_cap);
}
for (const auto& narc : data.neighbor_arcs) {
graph.addEdge(narc.i, narc.j, narc.cap, narc.rev_cap);
}
graph.initGraph();
Duration build_dur = now() - build_begin;
// Solve graph.
auto solve_begin = now();
auto flow = graph.computeMaxFlow();
Duration solve_dur = now() - solve_begin;
return std::make_tuple(flow, build_dur.count(), solve_dur.count());
}
template <class Cap, class Term, class Flow, class Index, class Data, reimpls::LabelOrder LO, reimpls::RootOrder RO>
std::tuple<Flow, double, double> bench_hpf(BenchConfig config, const Data& data)
{
// Build graph.
auto build_begin = now();
// reimpls::Hpf<Cap, mbk::LabelOrder::HIGHEST_FIRST, reimpls::RootOrder::FIFO> graph(
reimpls::Hpf<Cap, LO, RO> graph(
data.num_nodes + 2, data.neighbor_arcs.size() + data.terminal_arcs.size());;
graph.set_source(0);
graph.set_sink(1);
graph.add_node(data.num_nodes + 2);
for (const auto& tarc : data.terminal_arcs) {
graph.add_edge(0, tarc.node + 2, tarc.source_cap);
graph.add_edge(tarc.node + 2, 1, tarc.sink_cap);
}
for (const auto& narc : data.neighbor_arcs) {
if (narc.cap) {
graph.add_edge(narc.i + 2, narc.j + 2, narc.cap);
}
if (narc.rev_cap) {
graph.add_edge(narc.j + 2, narc.i + 2, narc.rev_cap);
}
}
Duration build_dur = now() - build_begin;
// Solve graph.
auto solve_begin = now();
graph.mincut();
Duration solve_dur = now() - solve_begin;
auto flow = graph.compute_maxflow();
return std::make_tuple(flow, build_dur.count(), solve_dur.count());
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double> bench_hi_pr(BenchConfig config, const Data& data)
{
hi_pr::HiPr graph;
// Reorder data to comply with HI_PR implementation
std::vector<int> endpoints; // [from, to, from, to, ...]
std::vector<int> capacities; // [cap, rev_cap, cap, rev_cap, ...]
std::vector<int> excesses; // source_cap - sink_cap for all nodes
endpoints.reserve(2 * data.neighbor_arcs.size());
capacities.reserve(2 * data.neighbor_arcs.size());
excesses.resize(data.num_nodes, 0);
for (const auto& narc : data.neighbor_arcs) {
endpoints.push_back(narc.i);
endpoints.push_back(narc.j);
capacities.push_back(narc.cap);
capacities.push_back(narc.rev_cap);
}
for (const auto& tarc : data.terminal_arcs) {
excesses[tarc.node] = tarc.source_cap - tarc.sink_cap;
}
// Build graph.
auto build_begin = now();
graph.construct(data.num_nodes, data.neighbor_arcs.size(),
endpoints.data(), capacities.data(), excesses.data());
Duration build_dur = now() - build_begin;
// Solve graph.
auto solve_begin = now();
graph.stageOne();
Duration solve_dur = now() - solve_begin;
auto flow = graph.flow - graph.flow0;
return std::make_tuple(flow, build_dur.count(), solve_dur.count());
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double> bench_gridcut(
BenchConfig config, const Data& data, const DataConfig& data_config)
{
#ifdef GRIDCUT_IS_AVAILABLE
size_t width = data_config.grid_width;
size_t height = data_config.grid_height;
size_t depth = data_config.grid_depth;
assert(data.num_nodes == width * height * depth);
// Prepare arrays with terminal capacities
std::vector<Term> source_caps(data.num_nodes, 0);
std::vector<Term> sink_caps(data.num_nodes, 0);
for (const auto& tarc : data.terminal_arcs) {
source_caps[tarc.node] += tarc.source_cap;
sink_caps[tarc.node] += tarc.sink_cap;
}
// Prepare arrays with neighbor capacities
std::array<std::array<std::array<std::vector<Cap>, 3>, 3>, 3> nbor_cap_arrays;
switch (data_config.grid_type)
{
case GRID_TYPE_2D_4C:
nbor_cap_arrays[1][0][0].resize(data.num_nodes, 0);
nbor_cap_arrays[1][2][0].resize(data.num_nodes, 0);
nbor_cap_arrays[0][1][0].resize(data.num_nodes, 0);
nbor_cap_arrays[2][1][0].resize(data.num_nodes, 0);
break;
case GRID_TYPE_2D_8C:
for (size_t i = 0; i < 3; ++i) {
for (size_t j = 0; j < 3; ++j) {
nbor_cap_arrays[i][j][0].resize(data.num_nodes, 0);
}
}
break;
case GRID_TYPE_3D_6C:
nbor_cap_arrays[1][1][0].resize(data.num_nodes, 0);
nbor_cap_arrays[1][1][2].resize(data.num_nodes, 0);
nbor_cap_arrays[1][0][1].resize(data.num_nodes, 0);
nbor_cap_arrays[1][2][1].resize(data.num_nodes, 0);
nbor_cap_arrays[0][1][1].resize(data.num_nodes, 0);
nbor_cap_arrays[2][1][1].resize(data.num_nodes, 0);
break;
case GRID_TYPE_3D_26C:
for (size_t i = 0; i < 3; ++i) {
for (size_t j = 0; j < 3; ++j) {
for (size_t k = 0; k < 3; ++k) {
nbor_cap_arrays[i][j][k].resize(data.num_nodes, 0);
}
}
}
break;
default:
throw std::invalid_argument("Benching GridCut but data is not grid.");
}
for (const auto& narc : data.neighbor_arcs) {
Vec3i ci = id2vec(narc.i, width, height);
Vec3i cj = id2vec(narc.j, width, height);
Vec3i offset_i(cj.x - ci.x, cj.y - ci.y, cj.z - ci.z);
Vec3i offset_j(ci.x - cj.x, ci.y - cj.y, ci.z - cj.z);
// Sometimes graphs have edges that go from one end of the grid to the other
// GridCut can't handle this so we ignore these edges. From what we've seen, these
// edges to not affect the solution either. It's a little bit of an advantange to GridCut
// but what are you gonna do...
if (abs(offset_i.x) <= 1 && abs(offset_i.y) <= 1 && abs(offset_i.z) <= 1) {
nbor_cap_arrays[offset_i.x + 1][offset_i.y + 1][offset_i.z + 1][narc.i] += narc.cap;
}
if (abs(offset_j.x) <= 1 && abs(offset_j.y) <= 1 && abs(offset_j.z) <= 1) {
nbor_cap_arrays[offset_j.x + 1][offset_j.y + 1][offset_j.z + 1][narc.j] += narc.rev_cap;
}
}
// Build and time graphs
Flow flow;
Duration build_dur;
Duration solve_dur;
if (data_config.grid_type == GRID_TYPE_2D_4C) {
auto build_begin = now();
GridGraph_2D_4C<Term, Cap, Flow> graph(width, height);
graph.set_caps(
source_caps.data(),
sink_caps.data(),
nbor_cap_arrays[0][1][0].data(), // [-1, 0]
nbor_cap_arrays[2][1][0].data(), // [+1, 0]
nbor_cap_arrays[1][0][0].data(), // [ 0,-1]
nbor_cap_arrays[1][2][0].data() // [ 0,+1]
);
build_dur = now() - build_begin;
auto solve_begin = now();
graph.compute_maxflow();
flow = graph.get_flow();
solve_dur = now() - solve_begin;
} else if (data_config.grid_type == GRID_TYPE_2D_8C) {
auto build_begin = now();
GridGraph_2D_8C<Term, Cap, Flow> graph(width, height);
graph.set_caps(
source_caps.data(),
sink_caps.data(),
nbor_cap_arrays[0][1][0].data(), // [-1, 0]
nbor_cap_arrays[2][1][0].data(), // [+1, 0]
nbor_cap_arrays[1][0][0].data(), // [ 0,-1]
nbor_cap_arrays[1][2][0].data(), // [ 0,+1]
nbor_cap_arrays[0][0][0].data(), // [-1,-1]
nbor_cap_arrays[2][0][0].data(), // [+1,-1]
nbor_cap_arrays[0][2][0].data(), // [-1,+1]
nbor_cap_arrays[2][2][0].data() // [+1,+1]
);
build_dur = now() - build_begin;
auto solve_begin = now();
graph.compute_maxflow();
flow = graph.get_flow();
solve_dur = now() - solve_begin;
} else if (data_config.grid_type == GRID_TYPE_3D_6C) {
auto build_begin = now();
GridGraph_3D_6C<Term, Cap, Flow> graph(width, height, depth);
graph.set_caps(
source_caps.data(),
sink_caps.data(),
nbor_cap_arrays[0][1][1].data(), // [-1, 0, 0]
nbor_cap_arrays[2][1][1].data(), // [+1, 0, 0]
nbor_cap_arrays[1][0][1].data(), // [ 0,-1, 0]
nbor_cap_arrays[1][2][1].data(), // [ 0,+1, 0]
nbor_cap_arrays[1][1][0].data(), // [ 0, 0,-1]
nbor_cap_arrays[1][1][2].data() // [ 0, 0,+1]
);
build_dur = now() - build_begin;
auto solve_begin = now();
graph.compute_maxflow();
flow = graph.get_flow();
solve_dur = now() - solve_begin;
} else if (data_config.grid_type == GRID_TYPE_3D_26C) {
auto build_begin = now();
GridGraph_3D_26C<Term, Cap, Flow> graph(width, height, depth);
graph.set_caps(
source_caps.data(),
sink_caps.data(),
nbor_cap_arrays[0][1][1].data(), // [-1, 0, 0]
nbor_cap_arrays[2][1][1].data(), // [+1, 0, 0]
nbor_cap_arrays[1][0][1].data(), // [ 0,-1, 0]
nbor_cap_arrays[1][2][1].data(), // [ 0,+1, 0]
nbor_cap_arrays[1][1][0].data(), // [ 0, 0,-1]
nbor_cap_arrays[1][1][2].data(), // [ 0, 0,+1]
nbor_cap_arrays[0][0][1].data(), // [-1,-1, 0]
nbor_cap_arrays[2][0][1].data(), // [+1,-1, 0]
nbor_cap_arrays[0][2][1].data(), // [-1,+1, 0]
nbor_cap_arrays[2][2][1].data(), // [+1,+1, 0]
nbor_cap_arrays[1][0][0].data(), // [ 0,-1,-1]
nbor_cap_arrays[1][2][0].data(), // [ 0,+1,-1]
nbor_cap_arrays[1][0][2].data(), // [ 0,-1,+1]
nbor_cap_arrays[1][2][2].data(), // [ 0,+1,+1]
nbor_cap_arrays[0][1][0].data(), // [-1, 0,-1]
nbor_cap_arrays[0][1][2].data(), // [-1, 0,+1]
nbor_cap_arrays[2][1][0].data(), // [+1, 0,-1]
nbor_cap_arrays[2][1][2].data(), // [+1, 0,+1]
nbor_cap_arrays[0][0][0].data(), // [-1,-1,-1]
nbor_cap_arrays[2][0][0].data(), // [+1,-1,-1]
nbor_cap_arrays[0][2][0].data(), // [-1,+1,-1]
nbor_cap_arrays[2][2][0].data(), // [+1,+1,-1]
nbor_cap_arrays[0][0][2].data(), // [-1,-1,+1]
nbor_cap_arrays[2][0][2].data(), // [+1,-1,+1]
nbor_cap_arrays[0][2][2].data(), // [-1,+1,+1]
nbor_cap_arrays[2][2][2].data() // [+1,+1,+1]
);
build_dur = now() - build_begin;
auto solve_begin = now();
graph.compute_maxflow();
flow = graph.get_flow();
solve_dur = now() - solve_begin;
}
return std::make_tuple(flow, build_dur.count(), solve_dur.count());
#else
throw std::runtime_error("GridCut is not available.");
#endif
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double, uint16_t> bench_parallel_mbk(
BenchConfig config, const Data& data, std::vector<uint16_t> node_blocks, uint16_t num_blocks)
{
auto block_intervals = split_block_intervals(node_blocks);
// Build graph.
auto build_begin = now();
reimpls::ParallelGraph<Cap, Term, Flow> graph(data.num_nodes, data.neighbor_arcs.size(), num_blocks);
graph.set_num_threads(config.num_threads);
Index added_nodes = 0;
for (const auto& itv : block_intervals) {
// itv = { interval_length, block_index }
graph.add_node(itv.first, itv.second);
added_nodes += itv.first;
}
if (added_nodes < data.num_nodes) {
// Data was likely a .bq file so need to repeat blocks
for (const auto& itv : block_intervals) {
// itv = { interval_length, block_index }
graph.add_node(itv.first, itv.second);
}
}
for (const auto& tarc : data.terminal_arcs) {
graph.add_tweights(tarc.node, tarc.source_cap, tarc.sink_cap);
}
for (const auto& narc : data.neighbor_arcs) {
graph.add_edge(narc.i, narc.j, narc.cap, narc.rev_cap, false);
}
Duration build_dur = now() - build_begin;
// Solve graph.
auto solve_begin = now();
auto flow = graph.maxflow();
Duration solve_dur = now() - solve_begin;
return std::make_tuple(flow, build_dur.count(), solve_dur.count(), num_blocks);
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double, uint16_t> bench_parallel_pr(
BenchConfig config, const Data& data, std::vector<uint16_t> node_blocks, uint16_t num_blocks)
{
if (!std::is_same<Cap, int32_t>::value) {
throw std::runtime_error("Only int32_t cap is supported for ppr");
}
/*auto build_begin = now();
reimpls::ParallelPushRelabel<int32_t, int64_t, Index, Index> graph(
data.num_nodes + 2, data.neighbor_arcs.size() + data.terminal_arcs.size());
graph.set_source(0);
graph.set_sink(1);
for (const auto& tarc : data.terminal_arcs) {
graph.add_edge(0, tarc.node + 2, tarc.source_cap, 0);
graph.add_edge(tarc.node + 2, 1, tarc.sink_cap, 0);
}
for (const auto& narc : data.neighbor_arcs) {
graph.add_edge(narc.i + 2, narc.j + 2, narc.cap, narc.rev_cap);
}
Duration build_dur = now() - build_begin;
graph.set_num_threads(config.num_threads);
auto solve_begin = now();
graph.mincut();
Duration solve_dur = now() - solve_begin;
return std::make_tuple(graph.get_flow(), build_dur.count(), solve_dur.count(), data.num_nodes);*/
auto build_begin = now();
size_t num_nodes = data.num_nodes + 2;
size_t num_arcs = 2 * (data.terminal_arcs.size() + data.neighbor_arcs.size());
std::vector<wghVertex<int>> verts(num_arcs);
std::vector<int> neighbors(num_arcs, -1);
std::vector<int> arc_weights(num_arcs, 0);
int source = 0;
int sink = 1;
wghGraph<int> wg(verts.data(), num_nodes, num_arcs);
size_t total_arcs = 0;
for (const auto& tarc : data.terminal_arcs) {
if (tarc.source_cap > tarc.sink_cap) {
wg.V[source].degree++;
} else {
wg.V[sink].degree++;
}
wg.V[tarc.node].degree++;
}
for (const auto& narc : data.neighbor_arcs) {
wg.V[narc.i + 2].degree++;
wg.V[narc.j + 2].degree++;
}
std::vector<int> offsets(num_nodes + 2, 0);
for (size_t i = 1; i < num_nodes; ++i) {
offsets[i] = offsets[i - 1] + wg.V[i - 1].degree;
}
int64_t flow = 0;
for (const auto& tarc : data.terminal_arcs) {
if (tarc.source_cap > tarc.sink_cap) {
neighbors[offsets[source]] = tarc.node + 2;
neighbors[offsets[tarc.node + 2]] = source;
arc_weights[offsets[source]] = tarc.source_cap - tarc.sink_cap;
arc_weights[offsets[tarc.node + 2]] = 0;
flow += tarc.sink_cap;
offsets[source]++;
} else {
neighbors[offsets[sink]] = tarc.node + 2;
neighbors[offsets[tarc.node + 2]] = sink;
arc_weights[offsets[sink]] = 0;
arc_weights[offsets[tarc.node + 2]] = tarc.sink_cap - tarc.source_cap;
flow += tarc.source_cap;
offsets[sink]++;
}
offsets[tarc.node + 2]++;
}
for (const auto& narc : data.neighbor_arcs) {
neighbors[offsets[narc.i + 2]] = narc.j + 2;
neighbors[offsets[narc.j + 2]] = narc.i + 2;
arc_weights[offsets[narc.i + 2]] = narc.cap;
arc_weights[offsets[narc.j + 2]] = narc.rev_cap;
offsets[narc.i + 2]++;
offsets[narc.j + 2]++;
}
wg.V[0].Neighbors = neighbors.data();
wg.V[0].nghWeights = arc_weights.data();
for (size_t i = 1; i < num_nodes; ++i) {
wg.V[i].Neighbors = wg.V[i - 1].Neighbors + wg.V[i - 1].degree;
wg.V[i].nghWeights = wg.V[i - 1].nghWeights + wg.V[i - 1].degree;
}
FlowGraph<int> g(wg, source, sink);
setWorkers(config.num_threads);
prepareMaxFlow(g);
Duration build_dur = now() - build_begin;
auto solve_begin = now();
flow += maxFlow();
Duration solve_dur = now() - solve_begin;
return std::make_tuple(flow, build_dur.count(), solve_dur.count(), data.num_nodes);
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double, uint16_t> bench_parallel_sk(
BenchConfig config, const Data& data, std::vector<uint16_t> node_blocks, uint16_t num_blocks)
{
if (node_blocks.size() < data.num_nodes) {
// Data was likely as .bq file so need to repeat blocks
auto old_size = node_blocks.size();
node_blocks.resize(2 * old_size);
std::copy_n(node_blocks.begin(), old_size, node_blocks.begin() + old_size);
}
unsigned int num_threads = config.num_threads;
uint16_t blocks_per_thread = num_blocks / num_threads;
if (blocks_per_thread == 0) {
// Have more threads than blocks, just use one thread per block
blocks_per_thread = 1;
num_threads = num_blocks;
}
// Update node_blocks so blocks have their correct block indices
for (auto& block : node_blocks) {
block = std::min<uint16_t>(block / blocks_per_thread, num_threads - 1);
}
// Find nodes we need to make shared across multiple blocks
std::vector<robin_hood::unordered_set<uint64_t>> extra_block_nodes(num_threads);
for (const auto& narc : data.neighbor_arcs) {
uint16_t bi = node_blocks[narc.i];
uint16_t bj = node_blocks[narc.j];
if (bi != bj && (narc.cap != 0 || narc.rev_cap != 0)) {
//if (bi < bj) {
extra_block_nodes[bi].insert(narc.j);
//} else {
extra_block_nodes[bj].insert(narc.i);
//}
}
}
auto block_intervals = split_block_intervals(node_blocks);
auto build_begin = now();
size_t edges_per_block = data.neighbor_arcs.size() / config.num_threads;
reimpls::ParallelSkGraph<Cap, Term, Flow, typename std::make_signed<Index>::type> graph(
data.num_nodes, edges_per_block + edges_per_block / 5);
graph.add_node(data.num_nodes);
Index added_nodes = 0;
for (const auto& itv : block_intervals) {
// itv = { interval_length, block_index }
graph.add_nodes_to_block(added_nodes, added_nodes + itv.first, itv.second);
added_nodes += itv.first;
}
for (uint16_t block = 0; block < num_threads; ++block) {
for (uint64_t i : extra_block_nodes[block]) {
graph.add_nodes_to_block(i, i+1, block);
}
}
for (const auto& tarc : data.terminal_arcs) {
if (tarc.source_cap != 0 || tarc.sink_cap != 0) {
graph.add_tweights(tarc.node, tarc.source_cap * 2, tarc.sink_cap * 2);
}
}
for (const auto& narc : data.neighbor_arcs) {
if (narc.cap != 0 || narc.rev_cap != 0) {
graph.add_edge(narc.i, narc.j, narc.cap * 2, narc.rev_cap * 2);
}
}
Duration build_dur = now() - build_begin;
auto solve_begin = now();
auto flow = graph.maxflow() / 2;
Duration solve_dur = now() - solve_begin;
return std::make_tuple(flow, build_dur.count(), solve_dur.count(), config.num_threads);
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double, uint16_t> bench_parallel_rd(
BenchConfig config, const Data& data, std::vector<uint16_t> node_blocks, uint16_t num_blocks)
{
#ifdef PARD_IS_AVAILABLE
constexpr int source = 0;
constexpr int sink = 1;
const std::string tstamp = std::to_string(std::chrono::system_clock::now().time_since_epoch().count());
const std::string splitter_file = "pard_tmp_splitter_t" + tstamp;
const int d = 1;
int size[] = { data.num_nodes };
// Remove previous output directory
std::string dir_name = splitter_file + "_reg";
fs::remove_all(dir_name);
if (node_blocks.size() < data.num_nodes) {
// Data was likely as .bq file so need to repeat blocks
auto old_size = node_blocks.size();
node_blocks.resize(2 * old_size);
std::copy_n(node_blocks.begin(), old_size, node_blocks.begin() + old_size);
}
unsigned int num_threads = config.num_threads;
uint16_t blocks_per_thread = num_blocks / (2 * num_threads);
if (blocks_per_thread == 0) {
// Have more threads than blocks, just use one thread per block
blocks_per_thread = num_blocks == 1 ? 1 : 2;
num_threads = num_blocks;
}
// Update node_blocks so blocks have their correct block indices
uint16_t used_blocks = 0;
for (auto& block : node_blocks) {
block = std::min<uint16_t>(block / blocks_per_thread, 2*num_threads);
used_blocks = std::max<uint16_t>(used_blocks, block);
}
used_blocks++; // Used blocks holds the max. block index so add one to get number of blocks
auto build_begin = now();
region_graph G;
G.pth = dir_name;
region_splitter2 splitter(splitter_file, &G, used_blocks, node_blocks);
parallel_ARD1 pard;
pard.params.n_threads = num_threads;
pard.construct(&G);
splitter.allocate1(data.num_nodes + 2, 0, source, sink, d, size);
// Add edges
for (int loop = 0; loop < 2; ++loop) {
for (const auto& tarc : data.terminal_arcs) {
splitter.read_arc(loop, source, tarc.node + 2, tarc.source_cap, 0);
splitter.read_arc(loop, tarc.node + 2, sink, tarc.sink_cap, 0);
}
for (const auto& narc : data.neighbor_arcs) {
splitter.read_arc(loop, narc.i + 2, narc.j + 2, narc.cap, narc.rev_cap);
}
splitter.allocate2(loop);
}
splitter.allocate3();
Duration build_dur = now() - build_begin;
//auto solve_begin = now();
int flow = pard.maxflow();
//Duration solve_dur = now() - solve_begin;
// Remove output directory since we're done
fs::remove_all(dir_name);
// To avoid including the time to read in the problem from disk in the solve time, we rely on
// the internal timer of the implementation here. For the build time, our external timer seems
// to give a more fair assessment (although it's longer than needed since it does include some
// disk I/O).
return std::make_tuple(flow, build_dur.count(), pard.info.solve_t.time(), used_blocks);
#else
throw std::runtime_error("P-ARD is not available");
#endif
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double, uint16_t> bench_parallel_eibfs(
BenchConfig config, const Data& data, std::vector<uint16_t> node_blocks, uint16_t num_blocks)
{
using Ibfs = reimpls::ParallelIbfs<Cap, Term, Flow>;
auto block_intervals = split_block_intervals(node_blocks);
// Build graph.
auto build_begin = now();
Ibfs graph(data.num_nodes, data.neighbor_arcs.size());
graph.setNumThreads(config.num_threads);
Index added_nodes = 0;
for (const auto& itv : block_intervals) {
// itv = { interval_length, block_index }
graph.registerNodes(added_nodes, added_nodes + itv.first, itv.second);
added_nodes += itv.first;
}
if (added_nodes < data.num_nodes) {
// Data was likely a .bq file so need to repeat blocks
for (const auto& itv : block_intervals) {
// itv = { interval_length, block_index }
graph.registerNodes(added_nodes, added_nodes + itv.first, itv.second);
}
}
for (const auto& tarc : data.terminal_arcs) {
graph.addNode(tarc.node, tarc.source_cap, tarc.sink_cap);
}
for (const auto& narc : data.neighbor_arcs) {
graph.addEdge(narc.i, narc.j, narc.cap, narc.rev_cap);
}
graph.initGraph();
Duration build_dur = now() - build_begin;
// Solve graph.
auto solve_begin = now();
auto flow = graph.computeMaxFlow();
Duration solve_dur = now() - solve_begin;
return std::make_tuple(flow, build_dur.count(), solve_dur.count(), num_blocks);
}
template <class Cap, class Term, class Flow, class Index, class Data>
std::tuple<Flow, double, double, uint16_t> bench_parallel_gridcut(
BenchConfig config, const Data& data, const DataConfig& data_config, std::vector<uint16_t> node_blocks, uint16_t num_blocks)
{
#ifdef GRIDCUT_IS_AVAILABLE
size_t width = data_config.grid_width;
size_t height = data_config.grid_height;
size_t depth = data_config.grid_depth;
assert(data.num_nodes == width * height * depth);
// Prepare arrays with terminal capacities
std::vector<Term> source_caps(data.num_nodes, 0);
std::vector<Term> sink_caps(data.num_nodes, 0);