在这里,我尝试使用一些伪代码自我解释CUDA启动参数模型(或执行配置模型),但我不知道是否存在一些重大错误,所以希望有人帮助查看它,并给我一些建议.谢谢先进.
这里是:
/*
normally, we write kernel function like this.
note, __global__ means this function will be called from host codes,
and executed on device. and a __global__ function could only return void.
if there's any parameter passed into __global__ function, it should be stored
in shared memory on device. so, kernel function is so different from the *normal*
C/C++ functions. if I was the CUDA authore, I should make the kernel function more
different from a normal C function.
*/
__global__ void
kernel(float *arr_on_device, int n) {
int idx = blockIdx.x * blockDIm.x + threadIdx.x;
if (idx < n) {
arr_on_device[idx] = arr_on_device[idx] * arr_on_device[idx];
}
}
/*
after this definition, we could call this kernel function in our normal C/C++ codes !!
do you feel something wired ? un-consistant ?
normally, when I write C codes, I will think a lot about the execution process down to
the metal in my mind, and this one...it's like some fragile codes. break the sequential
thinking process in my mind.
in order to make things normal, I found a way to explain: I expand the *__global__ * function
to some pseudo codes:
*/
#define __foreach(var, start, end) for (var = start, var < end; ++var)
__device__ int
__indexing() {
const int blockId = blockIdx.x * gridDim.x + gridDim.x * gridDim.y * blockIdx.z;
return
blockId * (blockDim.x * blockDim.y * blockDim.z) +
threadIdx.z * (blockDim.x * blockDim.y) +
threadIdx.x;
}
global_config =:
{
/*
global configuration.
note the default values are all 1, so in the kernel codes,
we could just ignore those dimensions.
*/
gridDim.x = gridDim.y = gridDim.z = 1;
blockDim.x = blockDim.y = blockDim.z = 1;
};
kernel =:
{
/*
I thought CUDA did some bad evil-detail-covering things here.
it's said that CUDA C is an extension of C, but in my mind,
CUDA C is more like C++, and the *<<<>>>* part is too tricky.
for example:
kernel<<<10, 32>>>(); means kernel will execute in 10 blocks each have 32 threads.
dim3 dimG(10, 1, 1);
dim3 dimB(32, 1, 1);
kernel<<<dimG, dimB>>>(); this is exactly the same thing with above.
it's not C style, and C++ style ? at first, I thought this could be done by
C++'s constructor stuff, but I checked structure *dim3*, there's no proper
constructor for this. this just brroke the semantics of both C and C++. I thought
force user to use *kernel<<<dim3, dim3>>>* would be better. So I'd like to keep
this rule in my future codes.
*/
gridDim = dimG;
blockDim = dimB;
__foreach(blockIdx.z, 0, gridDim.z)
__foreach(blockIdx.y, 0, gridDim.y)
__foreach(blockIdx.x, 0, gridDim.x)
__foreach(threadIdx.z, 0, blockDim.z)
__foreach(threadIdx.y, 0, blockDim.y)
__foreach(threadIdx.x, 0, blockDim.x)
{
const int idx = __indexing();
if (idx < n) {
arr_on_device[idx] = arr_on_device[idx] * arr_on_device[idx];
}
}
};
/*
so, for me, gridDim & blockDim is like some boundaries.
e.g. gridDim.x is the upper bound of blockIdx.x, this is not that obvious for people like me.
*/
/* the declaration of dim3 from vector_types.h of CUDA/include */
struct __device_builtin__ dim3
{
unsigned int x, y, z;
#if defined(__cplusplus)
__host__ __device__ dim3(unsigned int vx = 1, unsigned int vy = 1, unsigned int vz = 1) : x(vx), y(vy), z(vz) {}
__host__ __device__ dim3(uint3 v) : x(v.x), y(v.y), z(v.z) {}
__host__ __device__ operator uint3(void) { uint3 t; t.x = x; t.y = y; t.z = z; return t; }
#endif /* __cplusplus */
};
typedef __device_builtin__ struct dim3 dim3;
Gre*_*ith 12
CUDA DRIVER API
CUDA Driver API v4.0及更高版本使用以下函数来控制内核启动:
cuFuncSetCacheConfig
cuFuncSetSharedMemConfig
cuLaunchKernel
在v4.0中引入cuLaunchKernel之前,使用了以下CUDA驱动程序API函数.
cuFuncSetBlockShape()
cuFuncSetSharedSize()
cuParamSet{Size,i,fv}()
cuLaunch
cuLaunchGrid
有关这些功能的更多信息,请参阅cuda.h.
CUresult CUDAAPI cuLaunchKernel(CUfunction f,
unsigned int gridDimX,
unsigned int gridDimY,
unsigned int gridDimZ,
unsigned int blockDimX,
unsigned int blockDimY,
unsigned int blockDimZ,
unsigned int sharedMemBytes,
CUstream hStream,
void **kernelParams,
void **extra);
cuLaunchKernel将整个启动配置作为参数.
有关详细信息,请参阅NVIDIA驱动程序API [执行控制] 1.
CUDA KERNEL发布
cuLaunchKernel将1.验证启动参数2.更改共享内存配置3.更改本地内存分配4.将流同步令牌推入命令缓冲区以确保流中的两个命令不重叠4.推送启动参数进入命令缓冲区5.将启动命令推入命令缓冲区6.将命令缓冲区提交给设备(在wddm驱动程序上,此步骤可能会延迟)7.在wddm上,内核驱动程序将分页设备内存中所需的所有内存
GPU将1.验证命令2.将命令发送到计算工作分配器3.将启动配置和线程块分派给SM
当所有线程块都已完成时,工作分配器将刷新缓存以遵守CUDA内存模型,并将内核标记为已完成,以便流中的下一个项目可以进行前进.
调度线程块的顺序因架构而异.
计算能力1.x设备将内核参数存储在共享内存中.计算能力2.0-3.5设备将kenrel参数存储在常量存储器中.
CUDA RUNTIME API
CUDA Runtime是CUDA驱动程序API之上的C++软件库和构建工具链.CUDA Runtime使用以下函数来控制内核启动:
cudaConfigureCall cudaFuncSetCacheConfig cudaFuncSetSharedMemConfig cudaLaunch cudaSetupArgument
请参阅NVIDIA Runtime API [执行控制] 2
<<< >>> CUDA语言扩展是用于启动内核的最常用方法.
在编译期间,nvcc将为使用<<< >>>调用的每个内核函数创建一个新的CPU存根函数,它将用对stub函数的调用替换<<< >>>.
例如
__global__ void kernel(float* buf, int j)
{
// ...
}
kernel<<<blocks,threads,0,myStream>>>(d_buf,j);
生成
void __device_stub__Z6kernelPfi(float *__par0, int __par1){__cudaSetupArgSimple(__par0, 0U);__cudaSetupArgSimple(__par1, 4U);__cudaLaunch(((char *)((void ( *)(float *, int))kernel)));}
您可以通过在您的nvcc命令行中添加--keep来检查生成的文件.
cudaLaunch调用cuLaunchKernel.
CUDA DYNAMIC PARALLELISM
CUDA CDP的工作方式与上述CUDA Runtime API类似.