3. Error Handle和获取硬件信息

3. CUDA Error Handle

一个良好的cuda编程习惯里，我们习惯在调用一个cuda runtime api时，例如cudaMalloc() cudaMemcpy()我们就用error handler进行包装。这样
可以方便我们排查错误的来源

具体来说，CUDA的runtime API都会返回一个cudaError(枚举类), 可以通过枚举类来查看到它里面要么是成功了要么就是各种错误

__FILE__, __LINE__这两个指的是当前文件，下面的行和文件名就是这里来的

ERROR: src/matmul_gpu_basic.cu:62, CODE:cudaErrorInvalidConfiguration, DETAIL:invalid configuration argument

至于这里两个，宏定义, 一个是用来检查CUDA Runtime API, 一个是检查核函数的。检查kernel function的时候，用LAST_KERNEL_CHECK(), 这个放在同步后面, 确保之前的所有CUDA操作（包括kernel的执行）都已经完成,Z再来检查

有cudaPeekAtLastError或者cudaGetLastError, 区别是是否传播错误

kernelFunction<<<numBlocks, numThreads>>>();
cudaError_t err1 = cudaPeekAtLastError();  // 只查看，不清除错误状态
cudaError_t err2 = cudaGetLastError();  // 查看并清除错误状态

#include 
#include 

#define CUDA_CHECK(call)             __cudaCheck(call, __FILE__, __LINE__)
#define LAST_KERNEL_CHECK()          __kernelCheck(__FILE__, __LINE__)
#define BLOCKSIZE 16

inline static void __cudaCheck(cudaError_t err, const char* file, const int line) {
    if (err != cudaSuccess) {
        printf("ERROR: %s:%d, ", file, line);
        printf("CODE:%s, DETAIL:%s\n", cudaGetErrorName(err), cudaGetErrorString(err));
        exit(1);
    }
}

inline static void __kernelCheck(const char* file, const int line) {
    /* 
     * 在编写CUDA是，错误排查非常重要，默认的cuda runtime API中的函数都会返回cudaError_t类型的结果，
     * 但是在写kernel函数的时候，需要通过cudaPeekAtLastError或者cudaGetLastError来获取错误
     */
    cudaError_t err = cudaPeekAtLastError();
    if (err != cudaSuccess) {
        printf("ERROR: %s:%d, ", file, line);
        printf("CODE:%s, DETAIL:%s\n", cudaGetErrorName(err), cudaGetErrorString(err));
        exit(1);
    }
}

3.1 两个错误案例

EX1:

这里分配之前矩阵乘法的blockSize = 64, 那么一个线程块里面有64x64=4096个线程，超出了1024的限制, 下面是不用KernelCheck()和用了的区别

不加是不会报错的

matmul in cpu                  uses 4092.84 ms
matmul in GPU Warmup           uses 199.453 ms
matmul in GPU blockSize = 1    uses 13.1558 ms
matmul in GPU blockSize = 16   uses 13.0716 ms
matmul in GPU blockSize = 32   uses 13.0694 ms
matmul in GPU blockSize = 64   uses 2.00626 ms
res is different in 0, cpu: 260.89050293, gpu: 0.00000000
Matmul result is different

加了会出现报错, 这个错误 cudaErrorInvalidConfiguration 表示在执行CUDA kernel时，传递给 kernel 的配置参数无效。具体来说，CUDA kernel的配置包括线程块的数量、线程块内线程的数量等。

matmul in cpu                  uses 4115.42 ms
matmul in GPU Warmup           uses 201.464 ms
matmul in GPU blockSize = 1    uses 13.1182 ms
matmul in GPU blockSize = 16   uses 13.0607 ms
matmul in GPU blockSize = 32   uses 13.0602 ms
ERROR: src/matmul_gpu_basic.cu:69, CODE:cudaErrorInvalidConfiguration, DETAIL:invalid configuration argument

EX2:

    // 分配grid, block
    dim3 dimBlock(blockSize, blockSize);
    int gridDim = (width + blockSize - 1) / blockSize;
    dim3 dimGrid(gridDim, gridDim);

写成了

    // 分配grid, block
    dim3 dimBlock(blockSize, blockSize);
    int gridDim = (width + blockSize - 1) / blockSize;
    dim3 dimGrid(gridDim);

matmul in cpu                  uses 4152.26 ms
matmul in GPU Warmup           uses 189.667 ms
matmul in GPU blockSize = 1    uses 2.92747 ms
matmul in GPU blockSize = 16   uses 2.85372 ms
matmul in GPU blockSize = 32   uses 2.86483 ms
res is different in 32768, cpu: 260.76977539, gpu: 0.00000000

这个没有报错, 这里grid(网格)只有一个, 然后这里不够块去计算了, 所以计算了一部分他就不计算了, 所以运行的速度快了很多, 以后如果CUDA编程中速度快了很多，要参考是否是没有完整的计算。

4. 获取合适的硬件信息

4.1 为什么要获取硬件信息

当进行CUDA编程时，了解硬件规格是非常重要的，因为这些规格限制了你可以使用的并行策略和优化方式。

*********************Architecture related**********************
Device id:                              7
Device name:                            NVIDIA GeForce RTX 3090
Device compute capability:              8.6
GPU global meory size:                  23.70GB
L2 cache size:                          6.00MB
Shared memory per block:                48.00KB
Shared memory per SM:                   100.00KB
Device clock rate:                      1.69GHz
Device memory clock rate:               9.75Ghz
Number of SM:                           82
Warp size:                              32
*********************Parameter related************************
Max block numbers:                      16
Max threads per block:                  1024
Max block dimension size:               1024:1024:64
Max grid dimension size:                2147483647:65535:65535

4.2 代码

#include 
#include 
#include 

#define CUDA_CHECK(call)             __cudaCheck(call, __FILE__, __LINE__)
#define LAST_KERNEL_CHECK(call)      __kernelCheck(__FILE__, __LINE__)
#define LOG(...)                     __log_info(__VA_ARGS__)

#define BLOCKSIZE 16

static void __cudaCheck(cudaError_t err, const char* file, const int line) {
    if (err != cudaSuccess) {
        printf("ERROR: %s:%d, ", file, line);
        printf("CODE:%s, DETAIL:%s\n", cudaGetErrorName(err), cudaGetErrorString(err));
        exit(1);
    }
}

static void __kernelCheck(const char* file, const int line) {
    cudaError_t err = cudaPeekAtLastError();
    if (err != cudaSuccess) {
        printf("ERROR: %s:%d, ", file, line);
        printf("CODE:%s, DETAIL:%s\n", cudaGetErrorName(err), cudaGetErrorString(err));
        exit(1);
    }
}

// 使用变参进行LOG的打印。比较推荐的打印log的写法
static void __log_info(const char* format, ...) {
    char msg[1000];
    va_list args;
    va_start(args, format);

    vsnprintf(msg, sizeof(msg), format, args);

    fprintf(stdout, "%s\n", msg);
    va_end(args);
}

#include 
#include 
#include 

#include "utils.hpp"

int main(){
    int count;
    int index = 0;
    cudaGetDeviceCount(&count);
    while (index < count) {
        cudaSetDevice(index);
        cudaDeviceProp prop;
        cudaGetDeviceProperties(&prop, index);
        LOG("%-40s",             "*********************Architecture related**********************");
        LOG("%-40s%d%s",         "Device id: ",                   index, "");
        LOG("%-40s%s%s",         "Device name: ",                 prop.name, "");
        LOG("%-40s%.1f%s",       "Device compute capability: ",   prop.major + (float)prop.minor / 10, "");
        LOG("%-40s%.2f%s",       "GPU global meory size: ",       (float)prop.totalGlobalMem / (1<<30), "GB");
        LOG("%-40s%.2f%s",       "L2 cache size: ",               (float)prop.l2CacheSize / (1<<20), "MB");
        LOG("%-40s%.2f%s",       "Shared memory per block: ",     (float)prop.sharedMemPerBlock / (1<<10), "KB");
        LOG("%-40s%.2f%s",       "Shared memory per SM: ",        (float)prop.sharedMemPerMultiprocessor / (1<<10), "KB");
        LOG("%-40s%.2f%s",       "Device clock rate: ",           prop.clockRate*1E-6, "GHz");
        LOG("%-40s%.2f%s",       "Device memory clock rate: ",    prop.memoryClockRate*1E-6, "Ghz");
        LOG("%-40s%d%s",         "Number of SM: ",                prop.multiProcessorCount, "");
        LOG("%-40s%d%s",         "Warp size: ",                   prop.warpSize, "");

        LOG("%-40s",             "*********************Parameter related************************");
        LOG("%-40s%d%s",         "Max block numbers: ",           prop.maxBlocksPerMultiProcessor, "");
        LOG("%-40s%d%s",         "Max threads per block: ",       prop.maxThreadsPerBlock, "");
        LOG("%-40s%d:%d:%d%s",   "Max block dimension size:",     prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2], "");
        LOG("%-40s%d:%d:%d%s",   "Max grid dimension size: ",     prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2], "");
        index ++;
        printf("\n");
    }
    return 0;
}