Python 代码实现高性能异构分布式并行电路设计系统
任务管理模块:
任务调度与分配。 任务的状态跟踪和结果收集。
from multiprocessing import Process, Queue
class TaskManager:
def __init__(self):
self.task_queue = Queue()
self.result_queue = Queue()
def submit_task(self, task):
self.task_queue.put(task)
def execute_tasks(self, worker_function):
processes = []
for _ in range(NUM_WORKERS):
p = Process(target=worker_function, args=(self.task_queue, self.result_queue))
p.start()
processes.append(p)
for p in processes:
p.join()
def collect_results(self):
results = []
while not self.result_queue.empty():
results.append(self.result_queue.get())
return results
计算模块:
计算核心,支持异构计算,包括 CPU 和 GPU。 电路仿真和优化算法。
import torch
import numpy as np
class ComputeModule:
def __init__(self, use_gpu=False):
self.device = torch.device("cuda" if use_gpu and torch.cuda.is_available() else "cpu")
def simulate_circuit(self, circuit_data):
# Example: Simulate a simple circuit using matrix operations
circuit_matrix = torch.tensor(circuit_data, device=self.device)
result = torch.inverse(circuit_matrix)
return result.cpu().numpy()
def optimize_circuit(self, circuit_data):
# Implement optimization algorithms, e.g., genetic algorithms
pass
数据管理模块:
输入/输出数据的处理和管理。 分布式文件系统支持。
import os
import json
class DataManager:
def __init__(self, data_dir):
self.data_dir = data_dir
def load_data(self, filename):
with open(os.path.join(self.data_dir, filename), 'r') as f:
return json.load(f)
def save_results(self, filename, results):
with open(os.path.join(self.data_dir, filename), 'w') as f:
json.dump(results, f)
通信模块:
进程间的通信(例如 MPI 或 gRPC)。 网络传输的优化。
from mpi4py import MPI
class CommunicationModule:
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):
self.comm.send(data, dest=dest)
def receive_data(self, source):
return self.comm.recv(source=source)
资源管理模块:
系统资源的监控和调度。 异构资源的负载均衡。
import psutil
class ResourceManager:
def __init__(self):
pass
def monitor_resources(self):
cpu_usage = psutil.cpu_percent()
memory_usage = psutil.virtual_memory().percent
return {"cpu": cpu_usage, "memory": memory_usage}
def load_balance(self, tasks):
# Implement load balancing logic
pass
用户接口模块:
用户交互界面。 配置和结果可视化工具。
import tkinter as tk
from tkinter import filedialog
class UserInterface:
def __init__(self, root):
self.root = root
self.root.title("Distributed Circuit Design System")
self.create_widgets()
def create_widgets(self):
load_button = tk.Button(self.root, text="Load Circuit", command=self.load_circuit)
load_button.pack()
run_button = tk.Button(self.root, text="Run Simulation", command=self.run_simulation)
run_button.pack()
def load_circuit(self):
filename = filedialog.askopenfilename()
# Load circuit data logic
print(f"Loaded {filename}")
def run_simulation(self):
# Trigger simulation
print("Running simulation...")
if __name__ == "__main__":
root = tk.Tk()
app = UserInterface(root)
root.mainloop()
整合与执行
将所有模块结合起来,构建一个完整的系统:
if __name__ == "__main__":
task_manager = TaskManager()
compute_module = ComputeModule(use_gpu=True)
data_manager = DataManager('/path/to/data')
comm_module = CommunicationModule()
resource_manager = ResourceManager()
# Load data
circuit_data = data_manager.load_data('circuit.json')
# Submit tasks
task_manager.submit_task(lambda: compute_module.simulate_circuit(circuit_data))
# Execute tasks
task_manager.execute_tasks(worker_function=lambda q, r: r.put(q.get()()))
# Collect results
results = task_manager.collect_results()
# Save results
data_manager.save_results('results.json', results)
C++ 代码实现高性能异构分布式并行电路设计系统
任务管理模块:
任务调度与分配。 任务的状态跟踪和结果收集。
#include <iostream>
#include <queue>
#include <thread>
#include <mutex>
#include <functional>
#include <vector>
class TaskManager {
public:
void submitTask(std::function<void()> task) {
std::lock_guard<std::mutex> lock(queue_mutex_);
task_queue_.push(task);
}
void executeTasks() {
std::vector<std::thread> workers;
for (int i = 0; i < num_workers_; ++i) {
workers.emplace_back([this]() {
while (true) {
std::function<void()> task;
{
std::lock_guard<std::mutex> lock(queue_mutex_);
if (task_queue_.empty()) break;
task = task_queue_.front();
task_queue_.pop();
}
task();
}
});
}
for (auto& worker : workers) {
worker.join();
}
}
private:
std::queue<std::function<void()>> task_queue_;
std::mutex queue_mutex_;
const int num_workers_ = 4;
};
计算模块:
计算核心,支持异构计算,包括 CPU 和 GPU。 电路仿真和优化算法。
#include <iostream>
#include <vector>
#ifdef USE_CUDA
#include <cuda_runtime.h>
#endif
class ComputeModule {
public:
ComputeModule(bool use_gpu = false) : use_gpu_(use_gpu) {}
std::vector<std::vector<double>> simulateCircuit(const std::vector<std::vector<double>>& circuit_data) {
if (use_gpu_) {
// Perform GPU computation
#ifdef USE_CUDA
// CUDA-specific simulation code
#endif
} else {
// Perform CPU computation
// For simplicity, just return the input data as a "simulation" result
return circuit_data;
}
}
void optimizeCircuit(const std::vector<std::vector<double>>& circuit_data) {
// Implement optimization algorithms, e.g., genetic algorithms
}
private:
bool use_gpu_;
};
数据管理模块:
输入/输出数据的处理和管理。 分布式文件系统支持。
#include <iostream>
#include <fstream>
#include <vector>
#include <nlohmann/json.hpp>
class DataManager {
public:
DataManager(const std::string& data_dir) : data_dir_(data_dir) {}
std::vector<std::vector<double>> loadData(const std::string& filename) {
std::ifstream file(data_dir_ + "/" + filename);
nlohmann::json json_data;
file >> json_data;
return json_data.get<std::vector<std::vector<double>>>();
}
void saveResults(const std::string& filename, const std::vector<std::vector<double>>& results) {
std::ofstream file(data_dir_ + "/" + filename);
nlohmann::json json_data = results;
file << json_data.dump(4);
}
private:
std::string data_dir_;
};
通信模块:
进程间的通信(例如 MPI 或 gRPC)。 网络传输的优化。
#include <mpi.h>
#include <vector>
class CommunicationModule {
public:
CommunicationModule() {
MPI_Init(nullptr, nullptr);
MPI_Comm_rank(MPI_COMM_WORLD, &rank_);
MPI_Comm_size(MPI_COMM_WORLD, &size_);
}
~CommunicationModule() {
MPI_Finalize();
}
void sendData(const std::vector<double>& data, int dest) {
MPI_Send(data.data(), data.size(), MPI_DOUBLE, dest, 0, MPI_COMM_WORLD);
}
std::vector<double> receiveData(int source, int size) {
std::vector<double> data(size);
MPI_Recv(data.data(), size, MPI_DOUBLE, source, 0, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
return data;
}
private:
int rank_, size_;
};
资源管理模块:
系统资源的监控和调度。 异构资源的负载均衡。
#include <iostream>
#include <thread>
#include <vector>
#include <chrono>
class ResourceManager {
public:
void monitorResources() {
// Simulate resource monitoring
std::cout << "CPU Usage: 50%, Memory Usage: 40%" << std::endl;
}
void loadBalance(std::vector<std::function<void()>>& tasks) {
// Implement load balancing logic
}
};
用户接口模块:
用户交互界面。 配置和结果可视化工具。
#include <iostream>
class UserInterface {
public:
void loadCircuit() {
std::cout << "Loading Circuit..." << std::endl;
// Load circuit data logic
}
void runSimulation() {
std::cout << "Running Simulation..." << std::endl;
// Trigger simulation
}
};
整合与执行
将所有模块结合起来,构建一个完整的系统:
int main(int argc, char* argv[]) {
TaskManager task_manager;
ComputeModule compute_module(true); // Use GPU if available
DataManager data_manager("/path/to/data");
CommunicationModule comm_module;
ResourceManager resource_manager;
UserInterface ui;
// Load data
auto circuit_data = data_manager.loadData("circuit.json");
// Submit tasks
task_manager.submitTask([&]() {
auto result = compute_module.simulateCircuit(circuit_data);
data_manager.saveResults("results.json", result);
});
// Execute tasks
task_manager.executeTasks();
// Resource monitoring
resource_manager.monitorResources();
return 0;
}