0_Simple__UnifiedMemoryStreams

使用 OpenMP 和 pthreads 两种环境，利用实现统一内存编址，计算基本的矩阵乘法 result = α * A * x + β * result 。

▶ 源代码

 #include <cstdio>
 #include <vector>
 #include <algorithm>
 #include <cuda_runtime.h>
 #include "device_launch_parameters.h"
 #include <cublas_v2.h>
 
 //#define USE_PTHREADS // 使用 pthread 时补充定义 USE_PTHREADS
 #ifdef USE_PTHREADS
     #include <pthread.h>
     #pragma comment(lib, "pthreadVC2.lib")
 #else
     #include <omp.h>
 #endif
 
 // Windows 系统需要构造与函数 SRAND48 和 DRAND48 等价的随机函数
 #if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
 void srand48(long seed) { srand((unsigned int)seed); }
 double drand48() { return double(rand()) / RAND_MAX; }
 #endif
 
 template <typename T> struct Task// struct 也可使用类的构造和析构函数
 {
     unsigned int size, id;
     T *data;
     T *result;
     T *vector;
 
     Task() : size(), id(), data(NULL), result(NULL), vector(NULL) {};
     Task(unsigned int s) : size(s), id(), data(NULL), result(NULL)
     {
         cudaMallocManaged(&data, sizeof(T)*size*size);
         cudaMallocManaged(&result, sizeof(T)*size);
         cudaMallocManaged(&vector, sizeof(T)*size);
         cudaDeviceSynchronize();
     }
 
     ~Task()
     {
         cudaDeviceSynchronize();
         cudaFree(data);
         cudaFree(result);
         cudaFree(vector);
     }
 
     void allocate(const unsigned int s, const unsigned int unique_id)// 申请内存，初始化各成员数组
     {
         id = unique_id;
         size = s;
         cudaMallocManaged(&data, sizeof(T)*size*size);
         cudaMallocManaged(&result, sizeof(T)*size);
         cudaMallocManaged(&vector, sizeof(T)*size);
         cudaDeviceSynchronize();
 
         for (int i = ; i < size*size; i++)
             data[i] = drand48();
         for (int i = ; i < size; i++)
         {
             result[i] = .;
             vector[i] = drand48();
         }
     }
 };
 
 #ifdef USE_PTHREADS// 封装 pthread 型的任务
 struct threadData_t
 {
     int tid;
     Task<double> *TaskListPtr;
     cudaStream_t *streams;
     cublasHandle_t *handles;
     int taskSize;
 };
 
 typedef struct threadData_t threadData;
 #endif
 
 template <typename T> void gemv(int m, int n, T *alpha, T *A, T *x, T *beta, T *result)// 计算 result = α * A * x + β * result
 {
     for (int i = ; i < m; i++)// 源代码这写成了 n，并且漏掉了后面的 alpha
     {
         result[i] *= *beta;
         for (int j = ; j < n; j++)
             result[i] += *alpha * A[i*n + j] * x[j];
     }
 }
 
 // execute a single task on either host or device depending on size
 #ifdef USE_PTHREADS
 void * execute(void* inpArgs)
 {
     threadData *dataPtr    = (threadData *) inpArgs;
     cudaStream_t *stream   = dataPtr->streams;
     cublasHandle_t *handle = dataPtr->handles;
     int tid                = dataPtr->tid;
 
     for (int i = ; i < dataPtr->taskSize; i++)
     {
         Task<double>  &t = dataPtr->TaskListPtr[i];
         double alpha = 1.0;
         double beta = 0.0;
         if (t.size < )// 数据规模较小在主机上运行，否则在设备上运行
         {
             printf("\nTask [%2d], thread [%2d], size [%4d], on host",t.id,tid,t.size);
             cudaStreamAttachMemAsync(stream[], t.data, , cudaMemAttachHost);
             cudaStreamAttachMemAsync(stream[], t.vector, , cudaMemAttachHost);
             cudaStreamAttachMemAsync(stream[], t.result, , cudaMemAttachHost);
             cudaStreamSynchronize(stream[]);
             gemv(t.size, t.size, &alpha, t.data, t.vector, &beta, t.result);
         }
         else
         {
             printf("\nTask [%2d], thread [%2d], size [%4d], on device",t.id,tid,t.size);
             cublasSetStream(handle[tid+], stream[tid+]);
             cudaStreamAttachMemAsync(stream[tid+], t.data, , cudaMemAttachSingle);
             cudaStreamAttachMemAsync(stream[tid+], t.vector, , cudaMemAttachSingle);
             cudaStreamAttachMemAsync(stream[tid+], t.result, , cudaMemAttachSingle);
             cublasDgemv(handle[tid+], CUBLAS_OP_N, t.size, t.size, &alpha, t.data, t.size, t.vector, , &beta, t.result, );
         }
     }
     return NULL;
 }
 #else
 template <typename T> void execute(Task<T> &t, cublasHandle_t *handle, cudaStream_t *stream, int tid)
 {
     double alpha = 1.0;
     double beta = 0.0;
     if (t.size < )// 数据规模较小在主机上运行，否则在设备上运行
     {
         printf("\nTask [%2d], thread [%2d], size [%4d], on host",t.id,tid,t.size);
         cudaStreamAttachMemAsync(stream[], t.data, , cudaMemAttachHost);
         cudaStreamAttachMemAsync(stream[], t.vector, , cudaMemAttachHost);
         cudaStreamAttachMemAsync(stream[], t.result, , cudaMemAttachHost);
         cudaStreamSynchronize(stream[]);
         gemv(t.size, t.size, &alpha, t.data, t.vector, &beta, t.result);
     }
     else
     {
         printf("\nTask [%2d], thread [%2d], size[%4d], on device",t.id,tid,t.size);
         cublasSetStream(handle[tid+], stream[tid+]);
         cudaStreamAttachMemAsync(stream[tid+], t.data, , cudaMemAttachSingle);
         cudaStreamAttachMemAsync(stream[tid+], t.vector, , cudaMemAttachSingle);
         cudaStreamAttachMemAsync(stream[tid+], t.result, , cudaMemAttachSingle);
         cublasDgemv(handle[tid+], CUBLAS_OP_N, t.size, t.size, &alpha, t.data, t.size, t.vector, , &beta, t.result, );
     }
 }
 #endif
 
 template <typename T> void initialise_tasks(std::vector< Task<T> > &TaskList)
 {
     for (unsigned int i = ; i < TaskList.size(); i++)
     {
         int size;
         size = std::max((int)(drand48()*1000.0), );
         TaskList[i].allocate(size, i);
     }
 }
 
 int main()
 {
     printf("\n\tStart.\n");
 
     cudaDeviceProp device_prop;
     cudaGetDeviceProperties(&device_prop, );
     if (!device_prop.managedMemory)
     {
         printf("\n\tUnified Memory not supported\n");
         getchar();
         return ;
     }
     if (device_prop.computeMode == cudaComputeModeProhibited)// Device 为线程禁用模式
     {
         printf("\n\tComputeMode is cudaComputeModeProhibited\n");
         getchar();
         return ;
     }
 
     srand48(time(NULL));
     const int nthreads = ;
     cudaStream_t *streams = new cudaStream_t[nthreads+];
     cublasHandle_t *handles = new cublasHandle_t[nthreads+];
     for (int i=; i<nthreads+; i++)
     {
         cudaStreamCreate(&streams[i]);
         cublasCreate(&handles[i]);
     }
 
     unsigned int N = ;
     std::vector<Task<double> > TaskList(N);
     initialise_tasks(TaskList);
     cudaSetDevice();
 
 #ifdef USE_PTHREADS
     pthread_t threads[nthreads];
     threadData *InputToThreads = new threadData[nthreads];
     int temp = TaskList.size() / nthreads;
     for (int i=; i < nthreads; i++)
     {
         InputToThreads[i].tid = i;
         InputToThreads[i].streams = streams;
         InputToThreads[i].handles = handles;
 
         if (temp == )  // 任务数量比线程数少
         {
             InputToThreads[i].taskSize = ;
             InputToThreads[i].TaskListPtr = &TaskList[];
         }
         else            // 任务数量不少于线程数。任务尽量均分，多出的零头全部塞给最后一个线程
         {
             if (i == nthreads - )
             {
                 InputToThreads[i].taskSize = temp + (TaskList.size() % nthreads);
                 InputToThreads[i].TaskListPtr = &TaskList[i*temp + (TaskList.size() % nthreads)];
             }
             else
             {
                 InputToThreads[i].taskSize = temp;
                 InputToThreads[i].TaskListPtr = &TaskList[i*temp];
             }
         }
         pthread_create(&threads[i], NULL, &execute, &InputToThreads[i]);
     }
     for (int i=; i < nthreads; i++)
         pthread_join(threads[i], NULL);
 #else
     omp_set_num_threads(nthreads);
     #pragma omp parallel for schedule(dynamic)
     for (int i=; i<TaskList.size(); i++)
     {
         int tid = omp_get_thread_num();
         execute(TaskList[i], handles, streams, tid);
     }
 #endif
     cudaDeviceSynchronize();
 
     // 清理工作
     for (int i=; i<nthreads+; i++)
     {
         cudaStreamDestroy(streams[i]);
         cublasDestroy(handles[i]);
     }
     std::vector< Task<double> >().swap(TaskList);
     printf("\n\tFinish.\n");
     getchar();
     return ;
 }

▶ 输出结果：OpenMP

    Start.
 
Task [ ], thread [ ], size[ ], on device
Task [ ], thread [ ], size[ ], on device
Task [ ], thread [ ], size[ ], on device
Task [ ], thread [ ], size[ ], on device
Task [ ], thread [ ], size[ ], on device
Task [ ], thread [ ], size[ ], on device
Task [ ], thread [ ], size[ ], on device
Task [ ], thread [ ], size[ ], on device
Task [ ], thread [ ], size[ ], on device
Task [ ], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
Task [], thread [ ], size[ ], on device
    Finish.

▶ 输出结果：pthreads

    Start.
 
Task [ ], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [ ], thread [ ], size [  ], on host
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [ ], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [ ], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [ ], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [ ], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [  ], on host
Task [ ], thread [ ], size [  ], on host
Task [], thread [ ], size [ ], on device
Task [ ], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [ ], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [ ], thread [ ], size [  ], on host
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
Task [], thread [ ], size [ ], on device
    Finish.

▶ 涨姿势：

● 使用 C++ 结构体完成了类似类的方法。即在结构体中定义构造函数、析构函数及其他方法。

● 使用了 cuBLAS 库，注意句柄的使用和库函数的调用。

● 用到的申请内存的函数

 // driver_types.h
 #define cudaMemAttachGlobal 0x01  // 可访问内存
 #define cudaMemAttachHost   0x02  // 不可访问内存
 #define cudaMemAttachSingle 0x04  // 单线程可访问内存
 
 // cuda_runtime.h
 template<class T> static __inline__ __host__ cudaError_t cudaStreamAttachMemAsync(cudaStream_t stream, T *devPtr, size_t length = , unsigned int flags = cudaMemAttachSingle)
 {
     return ::cudaStreamAttachMemAsync(stream, (void*)devPtr, length, flags);
 }
 
 // cuda_runtime_api.h
 extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamAttachMemAsync(cudaStream_t stream, void *devPtr, size_t length __dv(), unsigned int flags __dv(cudaMemAttachSingle));