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设计Mock CUDA库的流程与实现

news 2025/8/3 8:33:57

在没有英伟达GPU可用的情况下用C++库写一个Mock CUDA的库，调用多核CPU和内存的资源来模拟GPU的算力，调试CUDA的C++代码，做这个库的基本流程步骤和详细设计是什么？

设计一个Mock CUDA库来模拟GPU功能，需要系统性地实现CUDA核心功能的多核CPU映射。以下是详细流程和设计：

一、基本流程步骤

需求分析
- 支持核心CUDA API：内存管理、内核启动、线程同步
- 映射GPU线程模型到CPU线程池
- 模拟设备内存和共享内存
- 实现关键内置变量（threadIdx, blockIdx等）
架构设计
开发阶段
- 阶段1：基础内存管理
- 阶段2：线程调度系统
- 阶段3：内核执行引擎
- 阶段4：同步原语实现
- 阶段5：原子操作支持

二、详细设计实现

1. 内存管理系统

class MemoryManager {std::unordered_map<void*, size_t> deviceAllocations;std::mutex memMutex;public:cudaError_t cudaMalloc(void** devPtr, size_t size) {std::lock_guard<std::mutex> lock(memMutex);*devPtr = malloc(size);deviceAllocations[*devPtr] = size;return (*devPtr) ? cudaSuccess : cudaErrorMemoryAllocation;}cudaError_t cudaMemcpy(void* dst, const void* src, size_t count, cudaMemcpyKind kind) {memcpy(dst, src, count);return cudaSuccess;}cudaError_t cudaFree(void* devPtr) {std::lock_guard<std::mutex> lock(memMutex);if (deviceAllocations.erase(devPtr)) {free(devPtr);return cudaSuccess;}return cudaErrorInvalidDevicePointer;}
};

2. 线程调度系统

class ThreadScheduler {ThreadPool pool;  // 自定义线程池struct BlockTask {dim3 blockIdx;std::function<void()> task;};public:void launchKernel(dim3 gridDim, dim3 blockDim, std::function<void()> kernelFunc) {std::vector<BlockTask> tasks;// 生成线程块任务for (int z = 0; z < gridDim.z; ++z)for (int y = 0; y < gridDim.y; ++y)for (int x = 0; x < gridDim.x; ++x) {tasks.push_back({.blockIdx = dim3(x, y, z),.task = [=] {// 线程块内循环for (int tz = 0; tz < blockDim.z; ++tz)for (int ty = 0; ty < blockDim.y; ++ty)for (int tx = 0; tx < blockDim.x; ++tx) {// 设置线程状态ThreadState::set(blockIdx, dim3(tx, ty, tz), blockDim);kernelFunc();  // 执行内核函数}}});}// 提交任务到线程池for (auto& task : tasks) {pool.enqueue(task.task);}pool.waitAll();}
};

3. 线程状态管理（TLS）

namespace ThreadState {thread_local dim3 currentBlockIdx;thread_local dim3 currentThreadIdx;thread_local dim3 currentBlockDim;void set(const dim3& blockIdx, const dim3& threadIdx, const dim3& blockDim) {currentBlockIdx = blockIdx;currentThreadIdx = threadIdx;currentBlockDim = blockDim;}// 获取当前线程索引__device__ int threadIdx_x() { return currentThreadIdx.x; }// 其他维度类似...
}

4. 共享内存模拟

class SharedMemory {static thread_local std::unordered_map<size_t, void*> sharedMemMap;static thread_local size_t currentSize;public:static void* getBuffer(size_t requestSize) {if (!sharedMemMap.count(requestSize)) {sharedMemMap[requestSize] = malloc(requestSize);}currentSize = requestSize;return sharedMemMap[requestSize];}static void freeAll() {for (auto& [size, ptr] : sharedMemMap) {free(ptr);}sharedMemMap.clear();}
};// 使用示例
extern "C" __device__ void* mySharedMem;
#define __shared__ __declspec(shared)
__shared__ float sharedBuffer[256];// 内核启动前
void configureSharedMemory(size_t size) {mySharedMem = SharedMemory::getBuffer(size);
}

5. 同步原语实现

class BlockBarrier {std::mutex mtx;std::condition_variable cv;int expectedCount;int currentCount = 0;public:BlockBarrier(int count) : expectedCount(count) {}void sync() {std::unique_lock<std::mutex> lock(mtx);if (++currentCount >= expectedCount) {currentCount = 0;cv.notify_all();} else {cv.wait(lock, [this] { return currentCount == 0; });}}
};// __syncthreads() 实现
void __syncthreads() {auto& barrier = ThreadState::getCurrentBarrier();barrier.sync();
}

三、内核启动机制

// 宏替换CUDA启动语法
#define cudaLaunchKernel(kernel, grid, block, sharedMem, stream, ...) \mockLaunchKernel(kernel, grid, block, sharedMem, ##__VA_ARGS__)template <typename Func, typename... Args>
void mockLaunchKernel(Func kernel, dim3 grid, dim3 block, size_t sharedMem, Args... args) {// 配置共享内存configureSharedMemory(sharedMem);// 创建线程屏障BlockBarrier barrier(block.x * block.y * block.z);// 封装内核函数auto wrappedKernel = [&, kernel, args...] {ThreadState::setBarrier(barrier);kernel(args...);};// 调度执行ThreadScheduler::instance().launchKernel(grid, block, wrappedKernel);
}

四、原子操作模拟

__device__ int atomicAdd(int* address, int val) {std::atomic<int>* atomicAddr = reinterpret_cast<std::atomic<int>*>(address);return atomicAddr->fetch_add(val);
}