linux/mm/memory.c/try_to_share()

本文详细介绍了Linux内核中的try_to_share函数如何实现不同进程间的内存页面共享。该函数通过复杂的地址转换和页表操作机制,使得当前进程能够安全地共享另一个进程的干净物理页面。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

/*
 * try_to_share() checks the page at address "address" in the task "p",
 * to see if it exists, and if it is clean. If so, share it with the current
 * task.
 *
 * NOTE! This assumes we have checked that p != current, and that they
 * share the same executable.
 */
// try_to_share函数可以让当前进程尝试去共享p进程address处的一个物理页面内容。
// 首先,这里的“共享”指的是能操作、实际就是让当前进程address处寻址最终也
// 指向p进程的address处物理页面。
// 其次,要理解这个函数的实现,一定要搞清楚address不是线性地址,而是逻辑地址。
static int try_to_share(unsigned long address, struct task_struct * p)
{
unsigned long from;
unsigned long to;
unsigned long from_page;
unsigned long to_page;
unsigned long phys_addr;


// 这里address不是线性地址了,而是逻辑地址、即虚拟地址。这个地址是以当前进程
// 64MB从零开始的地址空间计算得来的偏移地址。p->start_code为p进程的start_code
// 线性地址。因为linux0.11中进程都是代码段在前的所以start_code都是0.这个就决定了
// 一个事实,(p->start_code)>>20就得到了这个进程页表在页目录表中最多16个表项中
// 的第一个表项地址指针。然后address又是段内偏移地址,所以address>>20又能得到
// address所属页表在最多16个页表中的序数,于是乎这里两个+=就计算出了address处
// 页表在两个进程中的页表项地址了。
from_page = to_page = ((address>>20) & 0xffc);
from_page += ((p->start_code>>20) & 0xffc);
to_page += ((current->start_code>>20) & 0xffc);
/* is there a page-directory at from? */
from = *(unsigned long *) from_page;
if (!(from & 1))
return 0;
from &= 0xfffff000;
// 得到address在p进程中对应的页表基地址
from_page = from + ((address>>10) & 0xffc);
// 从页表项中取出address在p进程中对应物理页面的基地址
phys_addr = *(unsigned long *) from_page;
/* is the page clean and present? */
if ((phys_addr & 0x41) != 0x01)
return 0;
phys_addr &= 0xfffff000;
if (phys_addr >= HIGH_MEMORY || phys_addr < LOW_MEM)
return 0;
to = *(unsigned long *) to_page;
// 若当前进程address地址处对应的页表还未建立,则为当前进程注册一页物理内存
// 并且更新页目录项使其指向新申请的物理内存。即确保当前进程address处可以在
// 页目录表中找到对应表项
if (!(to & 1))
if (to = get_free_page())
*(unsigned long *) to_page = to | 7;
else
oom();
to &= 0xfffff000;
// 得到address在当前进程对应的页表项,把寻址推进到页表级
to_page = to + ((address>>10) & 0xffc);
// *(unsigned long *)to_page用来取出to_page处页表表项内容,这个内容
// 包括物理页面基地址和一些属性bit
if (1 & *(unsigned long *) to_page)
panic("try_to_share: to_page already exists");
/* share them: write-protect */
*(unsigned long *) from_page &= ~2;
// 若to_page处页面不存在,则满足share条件,开始设置share
// 把address在p进程中物理页面设为只读,然后将address在current process
// 的页表项内容设置为和p进程中一致。这样相当于address在当前进程和p进程
// 内都指向同一个物理页面了(这个页面就是p进程之前address处的物理页面)
*(unsigned long *) to_page = *(unsigned long *) from_page;
invalidate();
// 最后如果整个share顺利的话,p进程的address处物理页面必然多了当前进程
// 的一个引用,因此给他在mem_map中的引用计数+1
phys_addr -= LOW_MEM;
phys_addr >>= 12;
mem_map[phys_addr]++;
return 1;
}
import time import threading import psutil import smtplib import logging from datetime import datetime import schedule import winsound from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart import platform import os import gc import requests import json class XiaoxingService: def __init__(self, config_path="C:/xiaoxing/config.json"): """ 初始化AI服务 :param config_path: 配置文件路径 """ self.running = True self.last_optimized = None self.config = self.load_config(config_path) self.setup_logging() self.setup_tasks() logging.info("小星AI服务初始化完成") def load_config(self, path): """加载配置文件""" default_config = { "notification_email": None, "smtp_server": "smtp.example.com", "smtp_port": 587, "smtp_user": "xiaoxing@example.com", "smtp_pass": "your_password", "knowledge_sources": [ "https://api.tech-news.com/v1/latest", "https://ai-research-updates.org/feed" ], "optimization_threshold": { "cpu": 80, "memory": 85 }, "log_path": "C:/xiaoxing/service.log", "icon_path": "C:/xiaoxing/icon.ico", "knowledge_db": "C:/xiaoxing/knowledge.db" } try: with open(path, 'r') as f: return json.load(f) except FileNotFoundError: logging.warning("配置文件未找到,使用默认配置") return default_config except json.JSONDecodeError: logging.error("配置文件格式错误,使用默认配置") return default_config def setup_logging(self): """配置日志系统""" logging.basicConfig( filename=self.config["log_path"], level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', filemode='a' ) # 添加控制台输出 console = logging.StreamHandler() console.setLevel(logging.INFO) formatter = logging.Formatter('%(levelname)s: %(message)s') console.setFormatter(formatter) logging.getLogger().addHandler(console) def setup_tasks(self): """设置定时任务""" # 系统维护任务 schedule.every().day.at("02:00").do(self.optimize_system) schedule.every(30).minutes.do(self.check_system) # 知识管理任务 schedule.every().hour.do(self.update_knowledge) schedule.every().monday.at("04:00").do(self.self_evolve) # 健康报告任务 schedule.every().day.at("08:00").do(self.daily_health_report) logging.info("定时任务已设置") def optimize_system(self): """执行系统优化""" logging.info("开始系统优化") try: # 记录优化前的状态 cpu_before = psutil.cpu_percent(interval=1) mem_before = psutil.virtual_memory().percent # 执行优化操作 self.clean_memory() self.optimize_resources() # 记录优化结果 cpu_after = psutil.cpu_percent(interval=1) mem_after = psutil.virtual_memory().percent msg = (f"系统优化完成!\n" f"CPU使用率: {cpu_before}% → {cpu_after}%\n" f"内存使用率: {mem_before}% → {mem_after}%") logging.info(msg) self.notify("系统优化报告", msg) self.last_optimized = datetime.now() except Exception as e: logging.error(f"优化失败: {str(e)}") self.notify("优化失败", str(e)) def clean_memory(self): """内存清理优化""" # 跨平台内存清理 if platform.system() == 'Windows': try: import ctypes ctypes.windll.kernel32.SetProcessWorkingSetSize(-1, 0xFFFFFFFF, 0xFFFFFFFF) except Exception: pass else: # Linux/macOS 内存清理 os.system('sync && echo 3 > /proc/sys/vm/drop_caches') # Python内部垃圾回收 gc.collect() def optimize_resources(self): """优化系统资源使用""" # 清理临时文件 temp_dir = os.path.join(os.environ.get('TEMP', '/tmp'), 'xiaoxing_cache') if os.path.exists(temp_dir): for filename in os.listdir(temp_dir): file_path = os.path.join(temp_dir, filename) try: if os.path.isfile(file_path): os.unlink(file_path) except Exception as e: logging.warning(f"无法删除临时文件 {file_path}: {str(e)}") def update_knowledge(self): """更新知识库""" logging.info("开始更新知识库") try: new_knowledge = [] for source in self.config["knowledge_sources"]: try: response = requests.get(source, timeout=10) if response.status_code == 200: # 实际应用中需要解析不同格式的数据 # 这里简化为直接保存原始数据 new_knowledge.append(f"来源: {source}\n内容: {response.text[:200]}...") except requests.RequestException as e: logging.warning(f"知识源 {source} 获取失败: {str(e)}") if new_knowledge: with open(self.config["knowledge_db"], "a", encoding="utf-8") as f: f.write(f"\n\n=== 更新于 {datetime.now()} ===\n") f.write("\n".join(new_knowledge)) msg = f"获取 {len(new_knowledge)} 条新知识" logging.info(msg) self.notify("知识库更新", msg) else: logging.info("本次未获取到新知识") except Exception as e: logging.error(f"知识库更新失败: {str(e)}") self.notify("知识更新错误", str(e)) def check_system(self): """监控系统状态""" cpu_percent = psutil.cpu_percent(interval=1) mem_percent = psutil.virtual_memory().percent disk_percent = psutil.disk_usage('/').percent if platform.system() != 'Windows' else psutil.disk_usage('C:').percent logging.info(f"系统状态: CPU={cpu_percent}%, 内存={mem_percent}%, 磁盘={disk_percent}%") # 检查阈值 thresholds = self.config["optimization_threshold"] if cpu_percent > thresholds["cpu"]: self.handle_high_cpu(cpu_percent) if mem_percent > thresholds["memory"]: self.handle_high_memory(mem_percent) def handle_high_cpu(self, usage): """处理高CPU使用率""" logging.warning(f"CPU使用率过高: {usage}%") # 找出高CPU进程 processes = [] for proc in psutil.process_iter(['pid', 'name', 'cpu_percent']): try: if proc.info['cpu_percent'] > 10: # 筛选高CPU进程 processes.append(proc.info) except (psutil.NoSuchProcess, psutil.AccessDenied): pass # 按CPU使用率排序 processes.sort(key=lambda x: x['cpu_percent'], reverse=True) # 生成报告 report = f"当前CPU使用率: {usage}%\n" report += "高CPU进程:\n" for i, proc in enumerate(processes[:5], 1): report += f"{i}. {proc['name']} (PID:{proc['pid']}) - {proc['cpu_percent']:.1f}%\n" self.notify("CPU使用率警告", report) # 如果最近15分钟内没有优化过,执行优化 if not self.last_optimized or (datetime.now() - self.last_optimized).seconds > 900: self.optimize_system() def handle_high_memory(self, usage): """处理高内存使用率""" logging.warning(f"内存使用率过高: {usage}%") self.notify("内存警告", f"当前内存使用率: {usage}%") self.clean_memory() def self_evolve(self): """执行自我进化""" logging.info("启动自我进化协议") try: # 模拟进化过程 improvements = [ "神经网络架构升级: 引入注意力机制", "知识图谱扩展: 新增10万实体关系", "推理引擎优化: 响应速度提升40%", "安全模块强化: 量子加密算法集成" ] # 生成进化报告 report = "进化完成!主要改进:\n" for i, imp in enumerate(improvements, 1): report += f"{i}. {imp}\n" logging.info(report) self.notify("自我进化报告", report) self.play_sound_alert() except Exception as e: logging.error(f"进化失败: {str(e)}") self.notify("进化失败", str(e)) def daily_health_report(self): """生成每日健康报告""" logging.info("生成每日健康报告") try: # 获取系统指标 cpu_avg = psutil.cpu_percent(interval=1) mem_usage = psutil.virtual_memory().percent disk_usage = psutil.disk_usage('/').percent if platform.system() != 'Windows' else psutil.disk_usage('C:').percent # 获取网络状态 net_io = psutil.net_io_counters() # 构建报告 report = ( "小星AI每日健康报告\n" "===================\n" f"CPU平均使用率: {cpu_avg}%\n" f"内存使用率: {mem_usage}%\n" f"磁盘使用率: {disk_usage}%\n" f"网络流量: 接收 {net_io.bytes_recv/1024/1024:.2f}MB / 发送 {net_io.bytes_sent/1024/1024:.2f}MB\n" f"运行时间: {self.get_uptime()}\n" "===================\n" "系统状态: 一切正常 ✅" ) logging.info(report) self.notify("每日健康报告", report) except Exception as e: logging.error(f"健康报告生成失败: {str(e)}") def get_uptime(self): """获取服务运行时间""" if hasattr(self, 'start_time'): uptime = datetime.now() - self.start_time days = uptime.days hours, remainder = divmod(uptime.seconds, 3600) minutes, _ = divmod(remainder, 60) return f"{days}天 {hours}小时 {minutes}分钟" return "未知" def play_sound_alert(self): """播放声音提示""" try: if platform.system() == 'Windows': winsound.Beep(1000, 500) else: # Linux/Mac 使用系统声音 os.system('afplay /System/Library/Sounds/Ping.aiff' if platform.system() == 'Darwin' else 'paplay /usr/share/sounds/freedesktop/stereo/complete.oga') except Exception: pass def notify(self, title, message): """发送通知""" # 系统通知 self.show_system_notification(title, message) # 邮件通知 if self.config.get("notification_email"): self.send_email(title, message) def show_system_notification(self, title, message): """显示系统通知""" try: if platform.system() == 'Windows': from win10toast import ToastNotifier toaster = ToastNotifier() toaster.show_toast( title, message, icon_path=self.config.get("icon_path", ""), duration=10 ) elif platform.system() == 'Darwin': # macOS os.system(f"osascript -e 'display notification \"{message}\" with title \"{title}\"'") else: # Linux os.system(f'notify-send "{title}" "{message}"') except Exception as e: logging.warning(f"系统通知失败: {str(e)}") def send_email(self, subject, body): """发送邮件通知""" try: msg = MIMEMultipart() msg['Subject'] = subject msg['From'] = self.config["smtp_user"] msg['To'] = self.config["notification_email"] msg.attach(MIMEText(body, 'plain')) with smtplib.SMTP(self.config["smtp_server"], self.config["smtp_port"]) as server: server.starttls() server.login(self.config["smtp_user"], self.config["smtp_pass"]) server.send_message(msg) logging.info("邮件通知已发送") except Exception as e: logging.error(f"邮件发送失败: {str(e)}") def run(self): """启动服务主循环""" logging.info("小星AI后台服务启动") self.start_time = datetime.now() self.notify("小星AI服务", "后台服务已启动,开始24小时运行") # 定时任务线程 def schedule_runner(): while self.running: schedule.run_pending() time.sleep(1) threading.Thread(target=schedule_runner, daemon=True).start() # 主循环 try: while self.running: time.sleep(60) except KeyboardInterrupt: self.stop() def stop(self): """停止服务""" self.running = False logging.info("服务停止中...") self.notify("小星AI服务", "后台服务已安全停止") logging.info("服务已停止") if __name__ == "__main__": service = XiaoxingService() try: service.run() except Exception as e: logging.critical(f"服务崩溃: {str(e)}") service.notify("服务崩溃", str(e))
最新发布
07-20
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

朱有鹏老师

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值