代码实现高性能异构分布式图上网页节点评分系统

Python 代码实现高性能异构分布式图上网页节点评分系统

数据读取与预处理模块

功能： 从数据源读取图数据，预处理数据，构建初始图结构。

import networkx as nx
import numpy as np
import dask.dataframe as dd

def read_and_preprocess_data(file_path):
    # 读取数据文件，假设为CSV格式
    df = dd.read_csv(file_path, header=None, names=['source', 'target'])
    df = df.compute()

    # 创建NetworkX图
    G = nx.from_pandas_edgelist(df, 'source', 'target', create_using=nx.DiGraph)

    # 节点初始化
    for node in G.nodes:
        G.nodes[node]['rank'] = 1.0 / G.number_of_nodes()
        
    return G

file_path = 'path_to_your_data.csv'
graph = read_and_preprocess_data(file_path)

图划分与分配模块

功能： 将图划分为多个子图，并分配到不同的计算节点。

import metis
import dask.distributed as dask_distributed

def partition_graph(graph, num_partitions):
    _, parts = metis.part_graph(graph, nparts=num_partitions)
    
    subgraphs = {i: nx.DiGraph() for i in range(num_partitions)}
    
    for node, part in enumerate(parts):
        subgraphs[part].add_node(node, rank=graph.nodes[node]['rank'])
        for neighbor in graph.neighbors(node):
            if parts[neighbor] == part:
                subgraphs[part].add_edge(node, neighbor)
    
    return subgraphs, parts

def distribute_subgraphs(subgraphs, client):
    future_subgraphs = {i: client.scatter(subgraphs[i]) for i in subgraphs}
    return future_subgraphs

num_partitions = 4
subgraphs, parts = partition_graph(graph, num_partitions)

client = dask_distributed.Client()
future_subgraphs = distribute_subgraphs(subgraphs, client)

评分计算模块（PageRank）

功能： 计算每个子图的网页排名。

def compute_pagerank(subgraph, damping_factor=0.85, max_iter=100, tol=1.0e-6):
    num_nodes = subgraph.number_of_nodes()
    ranks = {node: 1.0 / num_nodes for node in subgraph.nodes()}
    
    for _ in range(max_iter):
        prev_ranks = ranks.copy()
        for node in subgraph.nodes():
            rank_sum = sum(prev_ranks[neighbor] / subgraph.out_degree(neighbor) 
                           for neighbor in subgraph.predecessors(node))
            ranks[node] = (1 - damping_factor) / num_nodes + damping_factor * rank_sum
        
        error = sum(abs(ranks[node] - prev_ranks[node]) for node in subgraph.nodes())
        if error < tol:
            break
    
    return ranks

def distributed_pagerank(future_subgraph, client):
    return client.submit(compute_pagerank, future_subgraph)

future_ranks = {i: distributed_pagerank(future_subgraphs[i], client) for i in future_subgraphs}

分布式通信与同步模块

功能： 在各个计算节点之间进行通信和同步。

def synchronize_ranks(future_ranks, parts, client):
    ranks = client.gather(future_ranks)
    
    global_ranks = {node: 0.0 for node in parts}
    for i, part_ranks in ranks.items():
        for node, rank in part_ranks.items():
            global_ranks[node] = rank
    
    return global_ranks

global_ranks = synchronize_ranks(future_ranks, parts, client)

结果收集与合并模块

功能： 收集各节点的计算结果，并合并为全局排名。

def merge_results(global_ranks):
    sorted_ranks = sorted(global_ranks.items(), key=lambda item: item[1], reverse=True)
    return sorted_ranks

final_ranking = merge_results(global_ranks)

# 保存结果
with open('pagerank_results.txt', 'w') as f:
    for node, rank in final_ranking:
        f.write(f'Node: {node}, Rank: {rank}\n')

print("PageRank calculation and merging complete. Results saved to 'pagerank_results.txt'.")

结语

以上代码实现了一个高性能异构分布式图上网页节点评分系统的基本框架。它通过读取和预处理数据，将图划分为多个子图，分配到不同的计算节点，并通过分布式计算和同步实现网页排名的计算。最后，结果收集并合并为全局排名。该系统利用了Dask进行分布式计算，并使用Metis进行图划分，实现了高效的网页排名计算。

C++ 代码实现高性能异构分布式图上网页节点评分系统

数据读取与预处理模块

功能： 从数据源读取图数据，预处理数据，构建初始图结构。

#include <iostream>
#include <fstream>
#include <vector>
#include <unordered_map>
#include <mpi.h>

using namespace std;

struct Edge {
    int source;
    int target;
};

void read_and_preprocess_data(const string& file_path, vector<Edge>& edges, int& num_nodes) {
    ifstream file(file_path);
    int source, target;
    unordered_map<int, int> node_map;
    int node_count = 0;

    while (file >> source >> target) {
        edges.push_back({source, target});
        if (node_map.find(source) == node_map.end()) {
            node_map[source] = node_count++;
        }
        if (node_map.find(target) == node_map.end()) {
            node_map[target] = node_count++;
        }
    }
    num_nodes = node_count;
    file.close();
}

int main(int argc, char** argv) {
    MPI_Init(&argc, &argv);

    string file_path = "path_to_your_data.csv";
    vector<Edge> edges;
    int num_nodes;

    read_and_preprocess_data(file_path, edges, num_nodes);

    // 后续处理...
    
    MPI_Finalize();
    return 0;
}

图划分与分配模块

功能： 将图划分为多个子图，并分配到不同的计算节点。

#include <metis.h>
#include <vector>

void partition_graph(int num_nodes, const vector<Edge>& edges, int num_partitions, vector<int>& partition) {
    idx_t n = num_nodes;
    idx_t ncon = 1;
    idx_t nparts = num_partitions;
    idx_t objval;
    vector<idx_t> xadj(n + 1, 0);
    vector<idx_t> adjncy(edges.size() * 2);
    vector<idx_t> part(n);
    
    for (const auto& edge : edges) {
        xadj[edge.source + 1]++;
        xadj[edge.target + 1]++;
    }
    for (int i = 1; i <= n; i++) {
        xadj[i] += xadj[i - 1];
    }
    int idx = 0;
    for (const auto& edge : edges) {
        adjncy[xadj[edge.source]++] = edge.target;
        adjncy[xadj[edge.target]++] = edge.source;
    }
    for (int i = n; i > 0; i--) {
        xadj[i] = xadj[i - 1];
    }
    xadj[0] = 0;

    METIS_PartGraphKway(&n, &ncon, xadj.data(), adjncy.data(), nullptr, nullptr, nullptr, &nparts, nullptr, nullptr, nullptr, &objval, part.data());
    partition.assign(part.begin(), part.end());
}

评分计算模块（PageRank）

功能： 计算每个子图的网页排名。

#include <vector>
#include <unordered_map>
#include <cmath>

const double damping_factor = 0.85;
const int max_iter = 100;
const double tol = 1e-6;

void compute_pagerank(const vector<Edge>& edges, int num_nodes, vector<double>& ranks) {
    vector<double> prev_ranks(num_nodes, 1.0 / num_nodes);
    ranks = prev_ranks;
    unordered_map<int, vector<int>> adj_list;

    for (const auto& edge : edges) {
        adj_list[edge.source].push_back(edge.target);
    }

    for (int iter = 0; iter < max_iter; iter++) {
        double error = 0.0;
        for (int i = 0; i < num_nodes; i++) {
            double rank_sum = 0.0;
            for (int neighbor : adj_list[i]) {
                rank_sum += prev_ranks[neighbor] / adj_list[neighbor].size();
            }
            ranks[i] = (1 - damping_factor) / num_nodes + damping_factor * rank_sum;
            error += fabs(ranks[i] - prev_ranks[i]);
        }
        if (error < tol) break;
        prev_ranks = ranks;
    }
}

分布式通信与同步模块

功能： 在各个计算节点之间进行通信和同步。

void synchronize_ranks(MPI_Comm comm, vector<double>& local_ranks, vector<double>& global_ranks, int num_nodes) {
    MPI_Allreduce(local_ranks.data(), global_ranks.data(), num_nodes, MPI_DOUBLE, MPI_SUM, comm);
}

int main(int argc, char** argv) {
    MPI_Init(&argc, &argv);

    int rank, size;
    MPI_Comm_rank(MPI_COMM_WORLD, &rank);
    MPI_Comm_size(MPI_COMM_WORLD, &size);

    string file_path = "path_to_your_data.csv";
    vector<Edge> edges;
    int num_nodes;

    read_and_preprocess_data(file_path, edges, num_nodes);

    int num_partitions = size;
    vector<int> partition;
    partition_graph(num_nodes, edges, num_partitions, partition);

    vector<Edge> local_edges;
    for (const auto& edge : edges) {
        if (partition[edge.source] == rank) {
            local_edges.push_back(edge);
        }
    }

    vector<double> local_ranks;
    compute_pagerank(local_edges, num_nodes, local_ranks);

    vector<double> global_ranks(num_nodes, 0.0);
    synchronize_ranks(MPI_COMM_WORLD, local_ranks, global_ranks, num_nodes);

    // 保存或处理最终结果

    MPI_Finalize();
    return 0;
}

结果收集与合并模块

功能： 收集各节点的计算结果，并合并为全局排名。

#include <algorithm>
#include <fstream>

void merge_results(const vector<double>& global_ranks, vector<pair<int, double>>& sorted_ranks) {
    for (int i = 0; i < global_ranks.size(); i++) {
        sorted_ranks.push_back({i, global_ranks[i]});
    }
    sort(sorted_ranks.begin(), sorted_ranks.end(), [](const pair<int, double>& a, const pair<int, double>& b) {
        return a.second > b.second;
    });
}

void save_results(const vector<pair<int, double>>& sorted_ranks, const string& file_path) {
    ofstream file(file_path);
    for (const auto& rank : sorted_ranks) {
        file << "Node: " << rank.first << ", Rank: " << rank.second << endl;
    }
    file.close();
}

int main(int argc, char** argv) {
    MPI_Init(&argc, &argv);

    int rank, size;
    MPI_Comm_rank(MPI_COMM_WORLD, &rank);
    MPI_Comm_size(MPI_COMM_WORLD, &size);

    string file_path = "path_to_your_data.csv";
    vector<Edge> edges;
    int num_nodes;

    read_and_preprocess_data(file_path, edges, num_nodes);

    int num_partitions = size;
    vector<int> partition;
    partition_graph(num_nodes, edges, num_partitions, partition);

    vector<Edge> local_edges;
    for (const auto& edge : edges) {
        if (partition[edge.source] == rank) {
            local_edges.push_back(edge);
        }
    }

    vector<double> local_ranks;
    compute_pagerank(local_edges, num_nodes, local_ranks);

    vector<double> global_ranks(num_nodes, 0.0);
    synchronize_ranks(MPI_COMM_WORLD, local_ranks, global_ranks, num_nodes);

    vector<pair<int, double>> sorted_ranks;
    if (rank == 0) {
        merge_results(global_ranks, sorted_ranks);
        save_results(sorted_ranks, "pagerank_results.txt");
        cout << "PageRank calculation and merging complete. Results saved to 'pagerank_results.txt'." << endl;
    }

    MPI_Finalize();
    return 0;
}

结语

以上代码实现了一个高性能异构分布式图上网页节点评分系统的基本框架。它通过读取和预处理数据，将图划分为多个子图，分配到不同的计算节点，并通过分布式计算和同步实现网页排名的计算。最后，结果收集并合并为全局排名。该系统利用了Dask进行分布式计算，并使用Metis进行图划分，实现了高效的网页排名计算。