代码实现高性能异构分布式数学符号解析系统

Python 代码实现高性能异构分布式数学符号解析系统

任务调度和管理模块

import multiprocessing
from mpi4py import MPI

class TaskScheduler:
    def __init__(self):
        self.comm = MPI.COMM_WORLD
        self.rank = self.comm.Get_rank()
        self.size = self.comm.Get_size()

    def distribute_tasks(self, tasks):
        if self.rank == 0:
            for i in range(1, self.size):
                self.comm.send(tasks[i-1::self.size-1], dest=i, tag=11)
        else:
            tasks = self.comm.recv(source=0, tag=11)
            self.process_tasks(tasks)

    def process_tasks(self, tasks):
        results = [self.process_task(task) for task in tasks]
        self.comm.send(results, dest=0, tag=22)

    def process_task(self, task):
        # Placeholder for actual task processing
        return f"Processed {task}"

    def collect_results(self):
        if self.rank == 0:
            results = []
            for i in range(1, self.size):
                results.extend(self.comm.recv(source=i, tag=22))
            return results

def main():
    scheduler = TaskScheduler()
    if scheduler.rank == 0:
        tasks = ["Task1", "Task2", "Task3", "Task4"]
        scheduler.distribute_tasks(tasks)
        results = scheduler.collect_results()
        print(f"Final Results: {results}")
    else:
        scheduler.distribute_tasks(None)

if __name__ == "__main__":
    main()

符号解析引擎模块

import sympy as sp

class SymbolicParser:
    def __init__(self):
        pass

    def parse_expression(self, expression_str):
        return sp.sympify(expression_str)

    def differentiate(self, expression, var):
        return sp.diff(expression, var)

    def integrate(self, expression, var):
        return sp.integrate(expression, var)

    def simplify(self, expression):
        return sp.simplify(expression)

高性能计算优化模块

import numpy as np
from numba import jit

@jit(nopython=True)
def high_performance_function(data):
    return np.sin(data) + np.cos(data)

class HPCOptimizer:
    def __init__(self):
        pass

    def optimize(self, data):
        return high_performance_function(data)

系统通信模块

from mpi4py import MPI

class CommunicationManager:
    def __init__(self):
        self.comm = MPI.COMM_WORLD
        self.rank = self.comm.Get_rank()
        self.size = self.comm.Get_size()

    def send_data(self, data, dest, tag=0):
        self.comm.send(data, dest=dest, tag=tag)

    def receive_data(self, source, tag=0):
        return self.comm.recv(source=source, tag=tag)

数据存储模块

import sqlite3

class DataStorage:
    def __init__(self, db_name="symbolic_data.db"):
        self.conn = sqlite3.connect(db_name)
        self.create_table()

    def create_table(self):
        with self.conn:
            self.conn.execute('''CREATE TABLE IF NOT EXISTS RESULTS
                                 (ID INTEGER PRIMARY KEY AUTOINCREMENT,
                                 EXPRESSION TEXT NOT NULL,
                                 RESULT TEXT NOT NULL);''')

    def store_result(self, expression, result):
        with self.conn:
            self.conn.execute("INSERT INTO RESULTS (EXPRESSION, RESULT) VALUES (?, ?)", (expression, result))

    def fetch_results(self):
        with self.conn:
            cursor = self.conn.execute("SELECT ID, EXPRESSION, RESULT from RESULTS")
            return cursor.fetchall()

集成运行

结合上述模块，创建一个集成的系统：

def run_distributed_symbolic_computation():
    scheduler = TaskScheduler()
    parser = SymbolicParser()
    optimizer = HPCOptimizer()
    comm_manager = CommunicationManager()
    storage = DataStorage()

    if scheduler.rank == 0:
        tasks = ["sin(x) + cos(x)", "diff(sin(x), x)", "integrate(x**2, x)"]
        scheduler.distribute_tasks(tasks)
        results = scheduler.collect_results()
        for expression, result in results:
            storage.store_result(expression, result)
        print(storage.fetch_results())
    else:
        scheduler.distribute_tasks(None)

if __name__ == "__main__":
    run_distributed_symbolic_computation()

该系统包括任务调度、符号解析、高性能优化、系统通信和数据存储，实现了一个高性能异构分布式数学符号解析系统的基本功能。可以根据具体需求进一步优化和扩展。

C++ 代码实现高性能异构分布式数学符号解析系统

任务调度和管理模块

#include <mpi.h>
#include <iostream>
#include <vector>
#include <string>

class TaskScheduler {
public:
    TaskScheduler() {
        MPI_Comm_rank(MPI_COMM_WORLD, &rank);
        MPI_Comm_size(MPI_COMM_WORLD, &size);
    }

    void distributeTasks(const std::vector<std::string>& tasks) {
        if (rank == 0) {
            int num_tasks = tasks.size();
            for (int i = 1; i < size; ++i) {
                int task_start = (i - 1) * (num_tasks / (size - 1));
                int task_end = (i < size - 1) ? i * (num_tasks / (size - 1)) : num_tasks;
                std::vector<std::string> sub_tasks(tasks.begin() + task_start, tasks.begin() + task_end);
                MPI_Send(sub_tasks.data(), sub_tasks.size(), MPI_CHAR, i, 0, MPI_COMM_WORLD);
            }
        } else {
            MPI_Status status;
            MPI_Probe(0, 0, MPI_COMM_WORLD, &status);
            int num_tasks;
            MPI_Get_count(&status, MPI_CHAR, &num_tasks);
            std::vector<char> buffer(num_tasks);
            MPI_Recv(buffer.data(), num_tasks, MPI_CHAR, 0, 0, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
            std::vector<std::string> tasks(buffer.begin(), buffer.end());
            processTasks(tasks);
        }
    }

    void processTasks(const std::vector<std::string>& tasks) {
        std::vector<std::string> results;
        for (const auto& task : tasks) {
            results.push_back(processTask(task));
        }
        MPI_Send(results.data(), results.size(), MPI_CHAR, 0, 1, MPI_COMM_WORLD);
    }

    std::string processTask(const std::string& task) {
        // Placeholder for actual task processing
        return "Processed " + task;
    }

    void collectResults() {
        if (rank == 0) {
            std::vector<std::string> results;
            for (int i = 1; i < size; ++i) {
                MPI_Status status;
                MPI_Probe(i, 1, MPI_COMM_WORLD, &status);
                int num_results;
                MPI_Get_count(&status, MPI_CHAR, &num_results);
                std::vector<char> buffer(num_results);
                MPI_Recv(buffer.data(), num_results, MPI_CHAR, i, 1, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
                results.insert(results.end(), buffer.begin(), buffer.end());
            }
            for (const auto& result : results) {
                std::cout << result << std::endl;
            }
        }
    }

private:
    int rank, size;
};

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

    TaskScheduler scheduler;
    if (scheduler.rank == 0) {
        std::vector<std::string> tasks = {"Task1", "Task2", "Task3", "Task4"};
        scheduler.distributeTasks(tasks);
        scheduler.collectResults();
    } else {
        scheduler.distributeTasks({});
    }

    MPI_Finalize();
    return 0;
}

符号解析引擎模块

#include <symengine/expression.h>
#include <symengine/derivative.h>
#include <symengine/integrate.h>
#include <symengine/simplify.h>

class SymbolicParser {
public:
    SymbolicParser() {}

    SymEngine::Expression parseExpression(const std::string& expression_str) {
        return SymEngine::parse(expression_str);
    }

    SymEngine::Expression differentiate(const SymEngine::Expression& expression, const SymEngine::RCP<const SymEngine::Symbol>& var) {
        return SymEngine::diff(expression, var);
    }

    SymEngine::Expression integrate(const SymEngine::Expression& expression, const SymEngine::RCP<const SymEngine::Symbol>& var) {
        return SymEngine::integrate(expression, var);
    }

    SymEngine::Expression simplify(const SymEngine::Expression& expression) {
        return SymEngine::simplify(expression);
    }
};

高性能计算优化模块

#include <cmath>
#include <vector>
#include <iostream>

class HPCOptimizer {
public:
    HPCOptimizer() {}

    std::vector<double> optimize(const std::vector<double>& data) {
        std::vector<double> result(data.size());
        for (size_t i = 0; i < data.size(); ++i) {
            result[i] = std::sin(data[i]) + std::cos(data[i]);
        }
        return result;
    }
};

系统通信模块

#include <mpi.h>

class CommunicationManager {
public:
    CommunicationManager() {
        MPI_Comm_rank(MPI_COMM_WORLD, &rank);
        MPI_Comm_size(MPI_COMM_WORLD, &size);
    }

    void sendData(const std::vector<char>& data, int dest, int tag = 0) {
        MPI_Send(data.data(), data.size(), MPI_CHAR, dest, tag, MPI_COMM_WORLD);
    }

    std::vector<char> receiveData(int source, int tag = 0) {
        MPI_Status status;
        MPI_Probe(source, tag, MPI_COMM_WORLD, &status);
        int count;
        MPI_Get_count(&status, MPI_CHAR, &count);
        std::vector<char> buffer(count);
        MPI_Recv(buffer.data(), count, MPI_CHAR, source, tag, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
        return buffer;
    }

private:
    int rank, size;
};

数据存储模块

#include <sqlite3.h>
#include <string>
#include <iostream>

class DataStorage {
public:
    DataStorage(const std::string& db_name = "symbolic_data.db") {
        sqlite3_open(db_name.c_str(), &db);
        createTable();
    }

    ~DataStorage() {
        sqlite3_close(db);
    }

    void storeResult(const std::string& expression, const std::string& result) {
        std::string sql = "INSERT INTO RESULTS (EXPRESSION, RESULT) VALUES ('" + expression + "', '" + result + "');";
        char* errmsg;
        sqlite3_exec(db, sql.c_str(), nullptr, nullptr, &errmsg);
        if (errmsg) {
            std::cerr << "Error: " << errmsg << std::endl;
            sqlite3_free(errmsg);
        }
    }

    void fetchResults() {
        std::string sql = "SELECT * FROM RESULTS;";
        char* errmsg;
        sqlite3_exec(db, sql.c_str(), callback, nullptr, &errmsg);
        if (errmsg) {
            std::cerr << "Error: " << errmsg << std::endl;
            sqlite3_free(errmsg);
        }
    }

private:
    sqlite3* db;

    void createTable() {
        std::string sql = "CREATE TABLE IF NOT EXISTS RESULTS (ID INTEGER PRIMARY KEY AUTOINCREMENT, EXPRESSION TEXT NOT NULL, RESULT TEXT NOT NULL);";
        char* errmsg;
        sqlite3_exec(db, sql.c_str(), nullptr, nullptr, &errmsg);
        if (errmsg) {
            std::cerr << "Error: " << errmsg << std::endl;
            sqlite3_free(errmsg);
        }
    }

    static int callback(void* NotUsed, int argc, char** argv, char** azColName) {
        for (int i = 0; i < argc; ++i) {
            std::cout << azColName[i] << ": " << argv[i] << std::endl;
        }
        std::cout << std::endl;
        return 0;
    }
};

集成与运行

结合上述模块，创建一个集成的系统：

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

    TaskScheduler scheduler;
    SymbolicParser parser;
    HPCOptimizer optimizer;
    CommunicationManager comm_manager;
    DataStorage storage;

    if (scheduler.rank == 0) {
        std::vector<std::string> tasks = {"sin(x) + cos(x)", "diff(sin(x), x)", "integrate(x^2, x)"};
        scheduler.distributeTasks(tasks);
        scheduler.collectResults();
    } else {
        scheduler.distributeTasks({});
    }

    MPI_Finalize();
    return 0;
}

该系统包括任务调度、符号解析、高性能优化、系统通信和数据存储，实现了一个高性能异构分布式数学符号解析系统的基本功能。可以根据具体需求进一步优化和扩展。