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引言：重新定义轻量级并发

在现代应用开发中，并发和异步编程能够有效提高处理器利用率，并在交互式应用中确保程序的响应速度。仓颉语言作为华为面向鸿蒙生态的原生编程语言，其并发机制和Go类似，协程框架、运行时自动调度，IO场景同步编码、异步运行，开发和运行效率都很高。本文将从M:N线程模型的理论基础出发，深入剖析仓颉协程调度器的设计哲学、运行时架构以及性能优化策略，揭示其在高并发场景下的工程价值。

M:N线程模型：架构设计的基石

仓颉语言采用M:N线程模型，即M个仓颉线程在N个native线程上调度执行，其中M和N不一定相等。这种设计选择在理论与实践之间找到了精妙的平衡点。仓颉线程本质上是用户态的轻量级线程，每个仓颉线程都受到底层native线程的调度执行，并且多个仓颉线程可以由一个native线程执行。

让我们通过一个实际的示例来理解这种模型：

main() {
    // 创建大量轻量级线程
    let handles = Array<Thread>()
    
    for i in 0..1000 {
        let handle = spawn {
            println("Thread ${i} starting on native thread")
            // 模拟一些工作
            var sum = 0
            for j in 0..10000 {
                sum += j
            }
            println("Thread ${i} completed with result: ${sum}")
        }
        handles.append(handle)
    }
    
    // 等待所有线程完成
    for handle in handles {
        handle.join()
    }
    
    println("All 1000 threads completed")
}

在这个示例中，我们创建了1000个仓颉线程，但实际上它们可能只在4-8个native线程上执行（取决于CPU核数）。运行时调度器负责在这些native线程之间动态分配仓颉线程的执行时机。

更复杂的场景涉及线程间的通信和协作：

import std.sync.*

// 生产者-消费者模式展示M:N调度
func producerConsumer() {
    let queue = ConcurrentQueue<Int64>()
    let producerCount = 10
    let consumerCount = 5
    let itemsPerProducer = 100
    
    // 创建多个生产者
    let producers = Array<Thread>()
    for i in 0..producerCount {
        let producer = spawn {
            for j in 0..itemsPerProducer {
                let item = i * itemsPerProducer + j
                queue.enqueue(item)
                println("Producer ${i} produced: ${item}")
            }
        }
        producers.append(producer)
    }
    
    // 创建多个消费者
    let consumers = Array<Thread>()
    for i in 0..consumerCount {
        let consumer = spawn {
            var processed = 0
            while (processed < (producerCount * itemsPerProducer / consumerCount)) {
                if let item = queue.dequeue() {
                    println("Consumer ${i} consumed: ${item}")
                    processed += 1
                }
            }
        }
        consumers.append(consumer)
    }
    
    // 等待所有线程完成
    for producer in producers { producer.join() }
    for consumer in consumers { consumer.join() }
}

在这个生产者-消费者示例中，15个仓颉线程（10个生产者+5个消费者）会被调度器智能地分配到少量native线程上执行，当某个线程在等待队列时，调度器会将执行权交给其他就绪的线程。

运行时调度架构：多层协同的精密系统

仓颉线程的运行时包含了执行线程、监视器、处理器与调度器等几个部分。这种分层架构体现了对调度复杂度的系统性分解。让我们通过代码来理解这些组件如何协同工作：

import std.sync.*
import std.time.*

// 展示调度器如何处理不同类型的任务
func schedulerDemo() {
    let mutex = ReentrantMutex()
    let sharedCounter = AtomicInt(0)
    
    // CPU密集型任务
    let cpuIntensiveTask = spawn {
        var result = 0
        for i in 0..10000000 {
            result += i * i
        }
        println("CPU intensive task result: ${result}")
    }
    
    // IO密集型任务（模拟网络请求）
    let ioTasks = Array<Thread>()
    for i in 0..20 {
        let task = spawn {
            println("IO task ${i} starting")
            // 模拟IO等待
            sleep(Duration.milliseconds(100))
            println("IO task ${i} completed")
        }
        ioTasks.append(task)
    }
    
    // 需要锁的任务
    let lockTasks = Array<Thread>()
    for i in 0..10 {
        let task = spawn {
            // 尝试获取锁
            mutex.lock()
            try {
                let current = sharedCounter.load()
                // 模拟临界区操作
                for j in 0..1000 {
                    let _ = current + j
                }
                sharedCounter.store(current + 1)
                println("Lock task ${i} incremented counter to ${sharedCounter.load()}")
            } finally {
                mutex.unlock()
            }
        }
        lockTasks.append(task)
    }
    
    // 等待所有任务完成
    cpuIntensiveTask.join()
    for task in ioTasks { task.join() }
    for task in lockTasks { task.join() }
    
    println("Final counter value: ${sharedCounter.load()}")
}

在这个示例中，调度器需要处理三种不同特征的任务：

  • CPU密集型任务会占用较长的执行时间片
  • IO密集型任务在sleep时会被挂起，执行线程继续调度其他任务
  • 需要锁的任务在等待锁时会被挂起，避免无谓的CPU消耗

抢占式调度：协作与强制的平衡艺术

协程调度的一大难题是如何在保持轻量级的同时，避免协作式调度的饿死问题。仓颉通过引入抢占式调度机制，在协作与强制之间找到了平衡。让我们通过代码来展示这一机制：

import std.time.*

// 展示抢占式调度如何处理长时间运行的任务
func preemptionDemo() {
    let startTime = DateTime.now()
    
    // 任务1：长时间计算，但有安全点
    let task1 = spawn {
        var sum = 0
        for i in 0..100000000 {
            sum += i
            // 每10000次迭代主动检查调度点
            if (i % 10000 == 0) {
                // 这里会插入安全点检查
                let _ = 0  // 编译器会在此处插入调度检查
            }
        }
        println("Task 1 completed: ${sum}")
    }
    
    // 任务2：需要及时响应的高优先级任务
    let task2 = spawn {
        sleep(Duration.milliseconds(100))
        println("Task 2 executed at ${DateTime.now().subtract(startTime).totalMilliseconds()} ms")
    }
    
    // 任务3：另一个长时间计算
    let task3 = spawn {
        var product = 1
        for i in 1..1000000 {
            product = (product * i) % 1000000007
        }
        println("Task 3 completed: ${product}")
    }
    
    task1.join()
    task2.join()
    task3.join()
}

// 对比：没有合理安全点的恶性循环
func badPreemptionExample() {
    let goodTask = spawn {
        var count = 0
        for i in 0..100000000 {
            count += 1
            // 定期让出CPU
            if (i % 50000 == 0) {
                // 主动协作点
            }
        }
        println("Good task completed")
    }
    
    // 这种任务会长时间占用CPU，导致其他任务饿死
    let poorTask = spawn {
        var x = 1.0
        // 纯计算循环，缺少安全点
        while (x < 1000000000.0) {
            x = x * 1.0000001
        }
        println("Poor task completed")
    }
    
    let urgentTask = spawn {
        println("I need immediate response!")
    }
    
    goodTask.join()
    poorTask.join()
    urgentTask.join()
}

编译器会在适当位置插入安全点检查，但作为高级开发者，我们可以主动设计更好的协作点：

// 手动优化的长时间计算任务
func optimizedLongRunningTask(data: Array<Int64>): Int64 {
    var result: Int64 = 0
    let chunkSize = 10000
    
    for chunkStart in 0..(data.size() / chunkSize) {
        // 处理一个数据块
        let chunkEnd = min((chunkStart + 1) * chunkSize, data.size())
        for i in (chunkStart * chunkSize)..chunkEnd {
            result += processDataPoint(data[i])
        }
        
        // 在数据块之间主动让出CPU
        // 这为调度器提供了切换其他线程的机会
        yield()  // 显式协作点
    }
    
    return result
}

func processDataPoint(value: Int64): Int64 {
    // 模拟复杂计算
    var temp = value
    for i in 0..100 {
        temp = (temp * temp) % 1000000007
    }
    return temp
}

阻塞与异步：IO密集型场景的优化策略

协程调度的最大价值在于IO密集型应用。让我们通过实际的文件和网络IO示例来展示仓颉如何实现"同步编码、异步运行"：

import std.io.*
import std.net.*
import std.time.*

// 传统阻塞式IO（会阻塞整个native线程）
func blockingIOExample() {
    let startTime = DateTime.now()
    
    // 顺序读取多个文件
    let files = ["file1.txt", "file2.txt", "file3.txt"]
    for filename in files {
        let content = readFileBlocking(filename)
        println("Read ${filename}: ${content.length} bytes")
    }
    
    let elapsed = DateTime.now().subtract(startTime)
    println("Blocking IO took: ${elapsed.totalMilliseconds()} ms")
}

// 仓颉协程优化的并发IO
func concurrentIOExample() {
    let startTime = DateTime.now()
    
    let files = ["file1.txt", "file2.txt", "file3.txt"]
    let readTasks = Array<Thread>()
    
    // 并发读取所有文件
    for filename in files {
        let task = spawn {
            // 看起来是同步代码，实际运行时异步执行
            let content = readFileAsync(filename)
            println("Read ${filename}: ${content.length} bytes")
        }
        readTasks.append(task)
    }
    
    // 等待所有读取完成
    for task in readTasks {
        task.join()
    }
    
    let elapsed = DateTime.now().subtract(startTime)
    println("Concurrent IO took: ${elapsed.totalMilliseconds()} ms")
}

// 网络IO示例：并发HTTP请求
func concurrentHttpRequests() {
    let urls = [
        "https://api.example.com/data1",
        "https://api.example.com/data2",
        "https://api.example.com/data3",
        "https://api.example.com/data4",
        "https://api.example.com/data5"
    ]
    
    let requests = Array<Thread>()
    let results = ConcurrentHashMap<String, String>()
    
    for url in urls {
        let request = spawn {
            try {
                // 同步风格的代码，运行时自动异步化
                let response = httpGet(url)
                results.put(url, response.body)
                println("Completed request to ${url}")
            } catch (e: NetworkException) {
                println("Failed to fetch ${url}: ${e.message}")
            }
        }
        requests.append(request)
    }
    
    // 等待所有请求完成
    for request in requests {
        request.join()
    }
    
    println("Completed ${results.size()} requests")
}

// 混合IO和计算的复杂场景
func dataProcessingPipeline() {
    let inputFiles = ["data1.csv", "data2.csv", "data3.csv"]
    let processedResults = ConcurrentQueue<ProcessedData>()
    
    // 阶段1：并发读取和解析文件
    let parseTasks = Array<Thread>()
    for filename in inputFiles {
        let task = spawn {
            // IO操作：异步读取
            let rawData = readFileAsync(filename)
            
            // CPU操作：解析数据
            let parsed = parseCSV(rawData)
            
            // IO操作：异步写入中间结果
            writeToCache(filename, parsed)
            
            println("Parsed ${filename}")
        }
        parseTasks.append(task)
    }
    
    for task in parseTasks { task.join() }
    
    // 阶段2：并发处理数据
    let processTasks = Array<Thread>()
    for filename in inputFiles {
        let task = spawn {
            // IO操作：从缓存读取
            let data = readFromCache(filename)
            
            // CPU密集型：数据分析
            let result = analyzeData(data)
            
            processedResults.enqueue(result)
            println("Processed ${filename}")
        }
        processTasks.append(task)
    }
    
    for task in processTasks { task.join() }
    
    // 阶段3：汇总结果
    let aggregateTask = spawn {
        var totalRecords = 0
        while let result = processedResults.dequeue() {
            totalRecords += result.recordCount
        }
        println("Total records processed: ${totalRecords}")
    }
    
    aggregateTask.join()
}

在这些示例中，仓颉的调度器会智能地处理IO操作：当线程执行IO时，不会阻塞native线程，而是将该仓颉线程挂起，让native线程继续执行其他就绪的仓颉线程。

并发对象库：零成本抽象的调度协同

仓颉提供的并发数据结构与调度器深度集成，让我们通过实际代码来展示其用法：

import std.sync.*
import std.collection.*

// 展示各种并发原语的使用
func concurrencyPrimitivesDemo() {
    // 1. 互斥锁示例
    demonstrateMutex()
    
    // 2. 并发哈希表示例
    demonstrateConcurrentHashMap()
    
    // 3. 并发队列示例
    demonstrateConcurrentQueue()
    
    // 4. 原子操作示例
    demonstrateAtomics()
}

// 互斥锁：保护共享资源
func demonstrateMutex() {
    let mutex = ReentrantMutex()
    let sharedResource = SharedData(0)
    let threads = Array<Thread>()
    
    for i in 0..10 {
        let thread = spawn {
            for j in 0..1000 {
                // 锁感知调度：获取失败时主动挂起
                mutex.lock()
                try {
                    sharedResource.value += 1
                } finally {
                    mutex.unlock()
                }
            }
        }
        threads.append(thread)
    }
    
    for thread in threads { thread.join() }
    println("Final value with mutex: ${sharedResource.value}")  // 应该是10000
}

// 并发哈希表：细粒度锁实现
func demonstrateConcurrentHashMap() {
    let concurrentMap = ConcurrentHashMap<String, Int64>()
    let updateTasks = Array<Thread>()
    
    // 多个线程并发更新不同的key
    for i in 0..20 {
        let task = spawn {
            let key = "key_${i % 5}"  // 5个不同的key
            for j in 0..100 {
                // 原子操作：读取-修改-写入
                let current = concurrentMap.getOrElse(key, {0})
                concurrentMap.put(key, current + 1)
            }
        }
        updateTasks.append(task)
    }
    
    for task in updateTasks { task.join() }
    
    // 并发读取
    let readTasks = Array<Thread>()
    for i in 0..5 {
        let task = spawn {
            let key = "key_${i}"
            if let value = concurrentMap.get(key) {
                println("${key}: ${value}")
            }
        }
        readTasks.append(task)
    }
    
    for task in readTasks { task.join() }
}

// 并发队列：无锁实现
func demonstrateConcurrentQueue() {
    let queue = ConcurrentQueue<WorkItem>()
    
    // 生产者：快速入队，不会阻塞
    let producers = Array<Thread>()
    for i in 0..5 {
        let producer = spawn {
            for j in 0..100 {
                let item = WorkItem(i, j)
                queue.enqueue(item)
                // 无锁操作，不会导致线程挂起
            }
        }
        producers.append(producer)
    }
    
    // 消费者：并发出队
    let consumers = Array<Thread>()
    let processedCount = AtomicInt(0)
    
    for i in 0..3 {
        let consumer = spawn {
            var localCount = 0
            while (processedCount.load() < 500) {
                if let item = queue.dequeue() {
                    processWorkItem(item)
                    localCount += 1
                    processedCount.fetchAdd(1)
                }
            }
            println("Consumer ${i} processed ${localCount} items")
        }
        consumers.append(consumer)
    }
    
    for producer in producers { producer.join() }
    for consumer in consumers { consumer.join() }
}

// 原子操作：最细粒度的并发控制
func demonstrateAtomics() {
    let counter = AtomicInt(0)
    let flag = AtomicBool(false)
    let threads = Array<Thread>()
    
    // 使用CAS（Compare-And-Swap）实现无锁计数
    for i in 0..10 {
        let thread = spawn {
            for j in 0..1000 {
                // 自旋直到CAS成功
                var success = false
                while (!success) {
                    let current = counter.load()
                    success = counter.compareAndSwap(current, current + 1)
                    // CAS失败时不会挂起线程，而是继续重试
                }
            }
        }
        threads.append(thread)
    }
    
    // 使用原子标志实现一次性操作
    let initTask = spawn {
        if (flag.compareAndSwap(false, true)) {
            println("Initialization performed by this thread")
            performExpensiveInit()
        }
    }
    
    for thread in threads { thread.join() }
    initTask.join()
    println("Final atomic counter: ${counter.load()}")
}

// 高级示例：实现一个简单的任务池
class TaskPool {
    let taskQueue: ConcurrentQueue<() -> Unit>
    let workers: Array<Thread>
    let running: AtomicBool
    
    init(workerCount: Int) {
        taskQueue = ConcurrentQueue<() -> Unit>()
        workers = Array<Thread>()
        running = AtomicBool(true)
        
        // 创建工作线程
        for i in 0..workerCount {
            let worker = spawn {
                workerLoop(i)
            }
            workers.append(worker)
        }
    }
    
    func workerLoop(workerId: Int) {
        while (running.load()) {
            if let task = taskQueue.dequeue() {
                try {
                    task()
                } catch (e: Exception) {
                    println("Worker ${workerId} error: ${e.message}")
                }
            } else {
                // 队列为空，主动让出CPU
                sleep(Duration.milliseconds(1))
            }
        }
    }
    
    func submit(task: () -> Unit) {
        taskQueue.enqueue(task)
    }
    
    func shutdown() {
        running.store(false)
        for worker in workers {
            worker.join()
        }
    }
}

// 使用任务池
func taskPoolExample() {
    let pool = TaskPool(workerCount: 4)
    
    // 提交100个任务
    for i in 0..100 {
        pool.submit({
            println("Executing task ${i}")
            sleep(Duration.milliseconds(10))
            println("Completed task ${i}")
        })
    }
    
    // 等待一段时间让任务完成
    sleep(Duration.seconds(5))
    pool.shutdown()
}

线程取消：协作式终止的工程实践

仓颉的协作式线程取消机制需要线程主动检查取消状态。让我们通过代码来展示最佳实践：

import std.sync.*
import std.time.*

// 基本的线程取消示例
func basicCancellationExample() {
    let cancellableTask = spawn {
        var iteration = 0
        while (!Thread.currentThread().isCancelled()) {
            println("Working... iteration ${iteration}")
            sleep(Duration.milliseconds(100))
            iteration += 1
            
            // 定期检查取消状态
            if (iteration > 50) {
                println("Task auto-completing after 50 iterations")
                break
            }
        }
        println("Task terminated gracefully")
    }
    
    // 主线程等待一段时间后取消任务
    sleep(Duration.seconds(1))
    cancellableTask.cancel()
    cancellableTask.join()
}

// 优雅处理清理工作的取消
func cancellationWithCleanup() {
    let file = openFile("temp_data.txt")
    let mutex = ReentrantMutex()
    
    let task = spawn {
        try {
            while (!Thread.currentThread().isCancelled()) {
                mutex.lock()
                try {
                    // 执行一些工作
                    file.write("Processing data...\n")
                } finally {
                    mutex.unlock()
                }
                
                sleep(Duration.milliseconds(50))
            }
        } finally {
            // 清理资源
            println("Cleaning up resources")
            mutex.lock()
            try {
                file.flush()
                file.close()
            } finally {
                mutex.unlock()
            }
        }
    }
    
    sleep(Duration.milliseconds(500))
    task.cancel()
    task.join()
    println("Task cancelled and cleaned up")
}

// 复杂的取消传播示例
func cancellationPropagation() {
    let parentTask = spawn {
        let childTasks = Array<Thread>()
        
        try {
            // 创建多个子任务
            for i in 0..5 {
                let child = spawn {
                    while (!Thread.currentThread().isCancelled()) {
                        println("Child ${i} working")
                        sleep(Duration.milliseconds(100))
                    }
                    println("Child ${i} cancelled")
                }
                childTasks.append(child)
            }
            
            // 父任务监控取消状态
            while (!Thread.currentThread().isCancelled()) {
                println("Parent monitoring")
                sleep(Duration.milliseconds(200))
            }
            
        } finally {
            // 父任务被取消时，取消所有子任务
            println("Parent cancelled, cancelling children")
            for child in childTasks {
                child.cancel()
            }
            
            // 等待所有子任务完成清理
            for child in childTasks {
                child.join()
            }
            println("All children cleaned up")
        }
    }
    
    sleep(Duration.seconds(1))
    parentTask.cancel()
    parentTask.join()
}

// 超时机制的实现
func timeoutExample() {
    func runWithTimeout<T>(timeout: Duration, task: () -> T): Option<T> {
        let result = AtomicOption<T>()
        let completed = AtomicBool(false)
        
        let worker = spawn {
            let value = task()
            result.store(Some(value))
            completed.store(true)
        }
        
        let timer = spawn {
            sleep(timeout)
            if (!completed.load()) {
                println("Task timed out, cancelling")
                worker.cancel()
            }
        }
        
        worker.join()
        timer.cancel()
        timer.join()
        
        return result.load()
    }
    
    // 使用超时运行任务
    let result = runWithTimeout(Duration.seconds(2), {
        // 模拟长时间运行的任务
        var sum = 0
        for i in 0..1000000 {
            sum += i
            if (Thread.currentThread().isCancelled()) {
                println("Long task cancelled")
                return -1
            }
        }
        return sum
    })
    
    match (result) {
        case Some(value) => println("Task completed with result: ${value}")
        case None => println("Task was cancelled or timed out")
    }
}

性能基准测试：量化调度开销

让我们通过实际的基准测试代码来量化协程调度的性能特征：

import std.time.*
import std.sync.*

// 基准测试框架
struct BenchmarkResult {
    let name: String
    let iterations: Int64
    let totalTime: Duration
    let avgTimePerOp: Duration
    let throughput: Float64  // operations per second
}

func benchmark(name: String, iterations: Int64, operation: () -> Unit): BenchmarkResult {
    let startTime = DateTime.now()
    
    for i in 0..iterations {
        operation()
    }
    
    let totalTime = DateTime.now().subtract(startTime)
    let avgTime = Duration.microseconds(totalTime.totalMicroseconds() / iterations)
    let throughput = iterations.toFloat64() / totalTime.totalSeconds()
    
    return BenchmarkResult(name, iterations, totalTime, avgTime, throughput)
}

// 测试1：线程创建和销毁开销
func benchmarkThreadCreation() {
    println("=== Thread Creation Benchmark ===")
    
    let result = benchmark("Thread Creation", 10000, {
        let thread = spawn {
            let _ = 1 + 1  // 最小化工作量
        }
        thread.join()
    })
    
    println("Iterations: ${result.iterations}")
    println("Total time: ${result.totalTime.totalMilliseconds()} ms")
    println("Avg time per operation: ${result.avgTimePerOp.totalMicroseconds()} µs")
    println("Throughput: ${result.throughput} ops/sec")
}

// 测试2：上下文切换开销
func benchmarkContextSwitch() {
    println("\n=== Context Switch Benchmark ===")
    
    let barrier = CyclicBarrier(2)
    let iterations: Int64 = 100000
    
    let startTime = DateTime.now()
    
    let thread1 = spawn {
        for i in 0..iterations {
            barrier.await()  // 强制上下文切换
        }
    }
    
    let thread2 = spawn {
        for i in 0..iterations {
            barrier.await()  // 强制上下文切换
        }
    }
    
    thread1.join()
    thread2.join()
    
    let totalTime = DateTime.now().subtract(startTime)
    let avgSwitchTime = totalTime.totalMicroseconds() / (iterations * 2)
    
    println("Context switches: ${iterations * 2}")
    println("Total time: ${totalTime.totalMilliseconds()} ms")
    println("Avg switch time: ${avgSwitchTime} µs")
}

// 测试3：并发队列性能
func benchmarkConcurrentQueue() {
    println("\n=== Concurrent Queue Benchmark ===")
    
    let queue = ConcurrentQueue<Int64>()
    let iterations: Int64 = 1000000
    let producerCount = 4
    let consumerCount = 4
    
    let startTime = DateTime.now()
    
    // 生产者线程
    let producers = Array<Thread>()
    for i in 0..producerCount {
        let producer = spawn {
            let itemsPerProducer = iterations / producerCount
            for j in 0..itemsPerProducer {
                queue.enqueue(i * itemsPerProducer + j)
            }
        }
        producers.append(producer)
    }
    
    // 消费者线程
    let consumers = Array<Thread>()
    let consumedCount = AtomicInt(0)
    
    for i in 0..consumerCount {
        let consumer = spawn {
            while (consumedCount.load() < iterations) {
                if let _ = queue.dequeue() {
                    consumedCount.fetchAdd(1)
                }
            }
        }
        consumers.append(consumer)
    }
    
    for producer in producers { producer.join() }
    for consumer in consumers { consumer.join() }
    
    let totalTime = DateTime.now().subtract(startTime)
    let throughput = iterations.toFloat64() / totalTime.totalSeconds()
    
    println("Total operations: ${iterations}")
    println("Total time: ${totalTime.totalMilliseconds()} ms")
    println("Throughput: ${throughput} ops/sec")
}

// 测试4：CPU密集 vs IO密集对比
func benchmarkCpuVsIo() {
    println("\n=== CPU vs IO Intensive Benchmark ===")
    
    let threadCount = 100
    
    // CPU密集型测试
    let cpuStartTime = DateTime.now()
    let cpuTasks = Array<Thread>()
    
    for i in 0..threadCount {
        let task = spawn {
            var sum = 0
            for j in 0..100000 {
                sum += j * j
            }
        }
        cpuTasks.append(task)
    }
    
    for task in cpuTasks { task.join() }
    let cpuTime = DateTime.now().subtract(cpuStartTime)
    
    // IO密集型测试
    let ioStartTime = DateTime.now()
    let ioTasks = Array<Thread>()
    
    for i in 0..threadCount {
        let task = spawn {
            sleep(Duration.milliseconds(10))  // 模拟IO等待
        }
        ioTasks.append(task)
    }
    
    for task in ioTasks { task.join() }
    let ioTime = DateTime.now().subtract(ioStartTime)
    
    println("CPU intensive (${threadCount} threads):")
    println("  Total time: ${cpuTime.totalMilliseconds()} ms")
    println("  Time per thread: ${cpuTime.totalMilliseconds() / threadCount} ms")
    
    println("IO intensive (${threadCount} threads):")
    println("  Total time: ${ioTime.totalMilliseconds()} ms")
    println("  Time per thread: ${ioTime.totalMilliseconds() / threadCount} ms")
    println("  Speedup from concurrency: ${(threadCount * 10.0) / ioTime.totalMilliseconds()}x")
}

// 运行所有基准测试
func runAllBenchmarks() {
    benchmarkThreadCreation()
    benchmarkContextSwitch()
    benchmarkConcurrentQueue()
    benchmarkCpuVsIo()
}

最佳实践示例集锦

最后，让我们通过一个综合示例来展示协程调度的最佳实践：

import std.sync.*
import std.collection.*
import std.time.*

// 实战案例：高性能Web服务器请求处理
class RequestHandler {
    let connectionPool: ConnectionPool
    let cache: ConcurrentHashMap<String, CachedResponse>
    let requestQueue: ConcurrentQueue<Request>
    let workerPool: Array<Thread>
    let metrics: AtomicMetrics
    
    init(workerCount: Int) {
        connectionPool = ConnectionPool(maxSize: 100)
        cache = ConcurrentHashMap<String, CachedResponse>()
        requestQueue = ConcurrentQueue<Request>()
        workerPool = Array<Thread>()
        metrics = AtomicMetrics()
        
        // 创建工作线程池
        for i in 0..workerCount {
            let worker = spawn {
                workerLoop(i)
            }
            workerPool.append(worker)
        }
    }
    
    func workerLoop(workerId: Int) {
        while (true) {
            if let request = requestQueue.dequeue() {
                handleRequest(workerId, request)
            } else {
                // 队列为空，短暂休眠避免busy-wait
                sleep(Duration.milliseconds(1))
            }
            
            // 定期检查取消状态
            if (Thread.currentThread().isCancelled()) {
                println("Worker ${workerId} shutting down")
                break
            }
        }
    }
    
    func handleRequest(workerId: Int, request: Request) {
        let startTime = DateTime.now()
        
        try {
            // 1. 检查缓存（无锁读取）
            if let cached = cache.get(request.url) {
                if (!cached.isExpired()) {
                    metrics.cacheHit()
                    sendResponse(request, cached.data)
                    return
                }
            }
            
            metrics.cacheMiss()
            
            // 2. 从连接池获取连接（可能阻塞）
            let connection = connectionPool.acquire()
            
            try {
                // 3. 执行IO操作（自动异步化）
                let response = connection.fetchData(request.url)
                
                // 4. 更新缓存
                cache.put(request.url, CachedResponse(response, Duration.minutes(5)))
                
                // 5. 发送响应
                sendResponse(request, response)
                
            } finally {
                // 6. 归还连接
                connectionPool.release(connection)
            }
            
        } catch (e: Exception) {
            println("Worker ${workerId} error: ${e.message}")
            sendError(request, e)
        } finally {
            let elapsed = DateTime.now().subtract(startTime)
            metrics.recordLatency(elapsed)
        }
    }
    
    func submitRequest(request: Request) {
        requestQueue.enqueue(request)
    }
    
    func shutdown() {
        println("Shutting down request handler")
        
        // 取消所有工作线程
        for worker in workerPool {
            worker.cancel()
        }
        
        // 等待所有线程完成
        for worker in workerPool {
            worker.join()
        }
        
        connectionPool.closeAll()
        metrics.printSummary()
    }
}

// 指标收集器
class AtomicMetrics {
    let totalRequests: AtomicInt
    let cacheHits: AtomicInt
    let cacheMisses: AtomicInt
    let totalLatency: AtomicInt64  // microseconds
    
    init() {
        totalRequests = AtomicInt(0)
        cacheHits = AtomicInt(0)
        cacheMisses = AtomicInt(0)
        totalLatency = AtomicInt64(0)
    }
    
    func cacheHit() {
        totalRequests.fetchAdd(1)
        cacheHits.fetchAdd(1)
    }
    
    func cacheMiss() {
        totalRequests.fetchAdd(1)
        cacheMisses.fetchAdd(1)
    }
    
    func recordLatency(duration: Duration) {
        totalLatency.fetchAdd(duration.totalMicroseconds())
    }
    
    func printSummary() {
        let total = totalRequests.load()
        let hits = cacheHits.load()
        let avgLatency = totalLatency.load() / total
        
        println("=== Performance Metrics ===")
        println("Total requests: ${total}")
        println("Cache hit rate: ${(hits * 100 / total)}%")
        println("Average latency: ${avgLatency} µs")
    }
}

// 使用示例
func webServerExample() {
    let handler = RequestHandler(workerCount: 8)
    
    // 模拟大量并发请求
    let requestSenders = Array<Thread>()
    for i in 0..20 {
        let sender = spawn {
            for j in 0..50 {
                let url = "https://api.example.com/data${j % 10}"
                handler.submitRequest(Request(url))
                sleep(Duration.milliseconds(10))
            }
        }
        requestSenders.append(sender)
    }
    
    // 等待所有请求发送完成
    for sender in requestSenders {
        sender.join()
    }
    
    // 等待处理完成
    sleep(Duration.seconds(5))
    
    handler.shutdown()
}

结语

仓颉的协程调度机制体现了对并发编程本质的深刻理解。通过M:N线程模型、抢占式调度、异步IO集成、调度感知的并发数据结构，仓颉在保持轻量级的同时，实现了接近操作系统线程的功能完备性。本文通过大量实战代码示例，从线程创建、上下文切换、IO处理、并发控制到性能基准测试，全面展示了如何在实际项目中驾驭这一强大的调度系统。深入理解这些机制和代码模式，能够帮助高级开发者写出更高效、更健壮的并发应用，为鸿蒙生态的高性能开发奠定坚实基础。

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