代码实现高性能异构分布式图计算任务解析系统

Python 代码实现高性能异构分布式图计算任务解析系统

任务分解模块 (Task Decomposition Module)

这个模块的作用是将一个大的图计算任务分解为多个子任务。

class TaskDecomposition:
    def __init__(self, graph):
        self.graph = graph

    def decompose(self):
        # 假设我们使用图划分算法将图划分为多个子图
        subgraphs = self.partition_graph(self.graph)
        return subgraphs

    def partition_graph(self, graph):
        # 这里可以使用METIS或其他图划分工具
        subgraphs = []  # 伪代码：划分后的子图列表
        return subgraphs

任务分配模块 (Task Allocation Module)

这个模块负责将分解后的子任务分配到不同的计算节点上。

class TaskAllocation:
    def __init__(self, subgraphs, nodes):
        self.subgraphs = subgraphs
        self.nodes = nodes

    def allocate(self):
        allocation = {}
        for i, subgraph in enumerate(self.subgraphs):
            node = self.nodes[i % len(self.nodes)]
            if node not in allocation:
                allocation[node] = []
            allocation[node].append(subgraph)
        return allocation

通信模块 (Communication Module)

这个模块负责不同计算节点之间的通信。

import socket

class Communication:
    def __init__(self):
        self.sockets = {}

    def connect(self, node):
        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        s.connect((node['host'], node['port']))
        self.sockets[node['id']] = s

    def send(self, node, data):
        s = self.sockets[node['id']]
        s.sendall(data.encode('utf-8'))

    def receive(self, node):
        s = self.sockets[node['id']]
        data = s.recv(1024)
        return data.decode('utf-8')

执行模块 (Execution Module)

这个模块负责在各个计算节点上执行分配的子任务。

class Execution:
    def __init__(self, subgraph):
        self.subgraph = subgraph

    def execute(self):
        result = self.perform_computation(self.subgraph)
        return result

    def perform_computation(self, subgraph):
        # 伪代码：在子图上执行计算
        result = {}  # 计算结果
        return result

结果汇总模块 (Result Aggregation Module)

这个模块负责将各个计算节点返回的结果汇总。

class ResultAggregation:
    def __init__(self):
        self.results = []

    def add_result(self, result):
        self.results.append(result)

    def aggregate(self):
        final_result = self.combine_results(self.results)
        return final_result

    def combine_results(self, results):
        # 伪代码：将多个结果合并
        combined_result = {}  # 合并后的最终结果
        return combined_result

主程序

最后，将各个模块整合到主程序中。

def main(graph, nodes):
    # 任务分解
    task_decomposition = TaskDecomposition(graph)
    subgraphs = task_decomposition.decompose()

    # 任务分配
    task_allocation = TaskAllocation(subgraphs, nodes)
    allocation = task_allocation.allocate()

    # 通信
    communication = Communication()
    for node in nodes:
        communication.connect(node)

    # 执行任务
    results = []
    for node, subgraphs in allocation.items():
        for subgraph in subgraphs:
            execution = Execution(subgraph)
            result = execution.execute()
            communication.send(node, result)
            received_result = communication.receive(node)
            results.append(received_result)

    # 结果汇总
    result_aggregation = ResultAggregation()
    for result in results:
        result_aggregation.add_result(result)

    final_result = result_aggregation.aggregate()
    print(final_result)

if __name__ == "__main__":
    graph = ...  # 你的图数据
    nodes = [
        {'id': 0, 'host': '127.0.0.1', 'port': 5000},
        {'id': 1, 'host': '127.0.0.1', 'port': 5001},
        # 添加更多节点
    ]
    main(graph, nodes)

C++ 代码实现高性能异构分布式图计算任务解析系统

任务分解模块 (Task Decomposition Module)

这个模块的作用是将一个大的图计算任务分解为多个子任务。

#include <vector>
#include <graph.h> // 假设存在一个图的头文件

class TaskDecomposition {
public:
    TaskDecomposition(Graph graph) : graph(graph) {}

    std::vector<Graph> decompose() {
        return partitionGraph(graph);
    }

private:
    Graph graph;

    std::vector<Graph> partitionGraph(Graph graph) {
        std::vector<Graph> subgraphs;
        // 伪代码：使用图划分算法将图划分为多个子图
        return subgraphs;
    }
};

任务分配模块 (Task Allocation Module)

这个模块负责将分解后的子任务分配到不同的计算节点上。

#include <vector>
#include <unordered_map>

class TaskAllocation {
public:
    TaskAllocation(std::vector<Graph> subgraphs, std::vector<Node> nodes)
        : subgraphs(subgraphs), nodes(nodes) {}

    std::unordered_map<Node, std::vector<Graph>> allocate() {
        std::unordered_map<Node, std::vector<Graph>> allocation;
        for (size_t i = 0; i < subgraphs.size(); ++i) {
            Node node = nodes[i % nodes.size()];
            allocation[node].push_back(subgraphs[i]);
        }
        return allocation;
    }

private:
    std::vector<Graph> subgraphs;
    std::vector<Node> nodes;
};

通信模块 (Communication Module)

这个模块负责不同计算节点之间的通信。

#include <unordered_map>
#include <string>
#include <asio.hpp>

class Communication {
public:
    Communication() : io_context() {}

    void connect(Node node) {
        asio::ip::tcp::socket socket(io_context);
        socket.connect(asio::ip::tcp::endpoint(asio::ip::address::from_string(node.host), node.port));
        sockets[node.id] = std::move(socket);
    }

    void send(Node node, const std::string& data) {
        asio::write(sockets[node.id], asio::buffer(data));
    }

    std::string receive(Node node) {
        asio::streambuf buffer;
        asio::read_until(sockets[node.id], buffer, "\n");
        std::istream stream(&buffer);
        std::string data;
        std::getline(stream, data);
        return data;
    }

private:
    asio::io_context io_context;
    std::unordered_map<int, asio::ip::tcp::socket> sockets;
};

执行模块 (Execution Module)

这个模块负责在各个计算节点上执行分配的子任务。

class Execution {
public:
    Execution(Graph subgraph) : subgraph(subgraph) {}

    std::string execute() {
        return performComputation(subgraph);
    }

private:
    Graph subgraph;

    std::string performComputation(Graph subgraph) {
        std::string result; // 伪代码：在子图上执行计算并生成结果
        return result;
    }
};

结果汇总模块 (Result Aggregation Module)

这个模块负责将各个计算节点返回的结果汇总。

#include <vector>
#include <string>

class ResultAggregation {
public:
    void addResult(const std::string& result) {
        results.push_back(result);
    }

    std::string aggregate() {
        return combineResults(results);
    }

private:
    std::vector<std::string> results;

    std::string combineResults(const std::vector<std::string>& results) {
        std::string combinedResult; // 伪代码：合并多个结果
        return combinedResult;
    }
};

主程序

最后，将各个模块整合到主程序中。

#include <iostream>
#include <vector>
#include <unordered_map>

// 假设存在Graph和Node的定义

void mainProgram(Graph graph, std::vector<Node> nodes) {
    // 任务分解
    TaskDecomposition taskDecomposition(graph);
    std::vector<Graph> subgraphs = taskDecomposition.decompose();

    // 任务分配
    TaskAllocation taskAllocation(subgraphs, nodes);
    std::unordered_map<Node, std::vector<Graph>> allocation = taskAllocation.allocate();

    // 通信
    Communication communication;
    for (Node& node : nodes) {
        communication.connect(node);
    }

    // 执行任务
    std::vector<std::string> results;
    for (auto& [node, subgraphs] : allocation) {
        for (Graph& subgraph : subgraphs) {
            Execution execution(subgraph);
            std::string result = execution.execute();
            communication.send(node, result);
            std::string receivedResult = communication.receive(node);
            results.push_back(receivedResult);
        }
    }

    // 结果汇总
    ResultAggregation resultAggregation;
    for (const std::string& result : results) {
        resultAggregation.addResult(result);
    }

    std::string finalResult = resultAggregation.aggregate();
    std::cout << finalResult << std::endl;
}

int main() {
    Graph graph; // 你的图数据
    std::vector<Node> nodes = {
        {0, "127.0.0.1", 5000},
        {1, "127.0.0.1", 5001},
        // 添加更多节点
    };

    mainProgram(graph, nodes);
    return 0;
}

这个代码实现了一个高性能异构分布式图计算任务解析系统的基本框架。根据具体的需求和性能要求，可以进一步优化和扩展各个模块的功能。