CUDA 不能使用所有可用的常量内存

Question

我有一个代码使用合作组来执行一些操作。因此我编译我的代码：

/usr/local/cuda/bin/nvcc -arch=sm_61 -gencode=arch=compute_61,code=sm_61, --device-c -g -O2 foo.cu

然后我尝试调用设备链接器：

/usr/local/cuda/bin/nvcc -arch=sm_61 -gencode=arch=compute_61,code=sm_61, -g -dlink foo.o

然后它会产生错误：

ptxas 错误：文件使用了过多的全局常量数据（0x10100 字节，最大 0x10000）

问题是由我分配常量内存的方式引起的：

__constant__ float d_cnst_centers[CONST_MEM / sizeof(float)];

其中 CONST_MEM = 65536 字节，这是我从 SM_61 的设备查询中获得的。但是，如果我将常量内存减少到 64536 之类的东西，问题就消失了。几乎就像在编译期间出于某些目的“保留”了常量内存一样。我搜索了 CUDA 文档，但没有找到令人满意的答案。使用可用的最大常量内存是否安全？为什么会出现这个问题？

编辑：这是触发 SM_61 错误的代码 sn-p：

#include <algorithm>
#include <vector>
#include <type_traits>
#include <cuda_runtime.h>
#include <cfloat>
#include <iostream>

#include <cooperative_groups.h>

using namespace cooperative_groups;


struct foo_params {
    float * points;
    float * centers;
    int * centersDist;
    int * centersIndex;
    int numPoints;
};

__constant__ float d_cnst_centers[65536 / sizeof(float)];

template <int R, int C>
__device__ int 
nearestCenter(float * points, float * pC) {
    float mindist = FLT_MAX;
    int minidx = 0;
    int clistidx = 0;
    for(int i=0; i<C;i++) {
        clistidx = i*R;
        float dist;
        {
            float *point = points;
            float *center = &pC[clistidx];
            float accum;
            for(int i = 0; i<R; i++) {
                float delta = point[i] - center[i];
                accum += delta*delta;
            }
            dist = sqrt(accum);
        }
        /* ... */
    }
    return minidx;
}


template<int R, int C, bool bRO, bool ROWMAJ=true>
__global__ void getNeatestCenter(struct foo_params params) {
        float * points = params.points;
        float * centers = params.centers;
        int * centersDist = params.centersDist;
        int * centersIndex = params.centersIndex;
        int numPoints = params.numPoints;

        grid_group grid = this_grid();
        {
            int idx = blockIdx.x*blockDim.x+threadIdx.x;
            if (idx < numPoints) {
                centersIndex[idx] = nearestCenter<R,C>(&points[idx*R], d_cnst_centers);
            }
        }
        /* ... other code */
}

int main () {
    // foo paramaters, for illustration purposes
    struct foo_params param;
    param.points = NULL;
    param.centers = NULL;
    param.centersDist = NULL;
    param.centersIndex = NULL;
    param.numPoints = 1000000;
    void *p_params = &param;

    int minGridSize = 0, blockSize = 0;
    cudaOccupancyMaxPotentialBlockSize(
                            &minGridSize,
                            &blockSize,
                            (void*)getNeatestCenter<128, 64, true>,
                            0,
                            0);

    dim3 dimGrid(minGridSize, 1, 1), dimBlock(blockSize, 1, 1);

    cudaLaunchCooperativeKernel((void *)getNeatestCenter<32, 32, true>, dimGrid, dimBlock, &p_params);
}

问题似乎是由以下行引起的：

grid_group grid = this_grid();

似乎在没有已知原因的情况下使用了大约 0x100 字节的常量内存。

Answer 1

为了记录在这个用例中到底发生了什么，我拼凑了编译过程的以下工作。希望它能阐明这个问题是如何产生的，提供一些有用的诊断工具，同时消除一些误解。

请注意，这是一项正在进行的工作，随着更多信息的曝光，可能会定期更新。请根据您的需要进行编辑和贡献

首先，如 cmets 中所述，完全可以分配常量内存的每个字节，直到 64kb 限制。这个例子几乎是原始问题中描述的用例：

const int sz = 65536;
const int NMax = sz / sizeof(float);
__constant__ float buffer[NMax];

__global__ 
void akernel(const float* __restrict__ arg1, float* __restrict__ arg2, int N)
{
    int tid = threadIdx.x + blockIdx.x * blockDim.x;
    if (tid < N) {
        float ans = 0;
        #pragma unroll 128
        for(int i=0; i<NMax; i++)  {
            float val = buffer[i];
            float y = (i%2 == 0) ? 1.f : -1.f;
            float x = val / 255.f;
            ans = ans + y * sinf(x);
        }
        arg2[tid] = ans + arg1[tid];
    }
}

它编译没有问题（Godbolt 链接here）。这证明问题中的链接器阶段必须从其他代码中提取额外的常量内存分配，无论是用户代码、其他设备库还是设备运行时支持。

因此，让我们将注意力转向更新问题中发布的复制案例，经过轻微修改，通过稍微减少常量内存占用，通过编译和链接阶段，缓冲区为 64536 字节：

$ nvcc -arch=sm_61 --device-c -g -O2 -Xptxas="-v" -o constmemuse.cu.o constmemuse.cu 
constmemuse.cu(51): warning: variable "centers" was declared but never referenced

constmemuse.cu(52): warning: variable "centersDist" was declared but never referenced

constmemuse.cu(31): warning: variable "dist" was set but never used
          detected during instantiation of "void getNeatestCenter<R,C,bRO,ROWMAJ>(foo_params) [with R=128, C=64, bRO=true, ROWMAJ=true]" 

constmemuse.cu(26): warning: variable "mindist" was declared but never referenced
          detected during instantiation of "void getNeatestCenter<R,C,bRO,ROWMAJ>(foo_params) [with R=128, C=64, bRO=true, ROWMAJ=true]" 

ptxas info    : 0 bytes gmem, 64536 bytes cmem[3]
ptxas info    : Function properties for cudaDeviceGetAttribute
    0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads
ptxas info    : Compiling entry function '_Z16getNeatestCenterILi128ELi64ELb1ELb1EEv10foo_params' for 'sm_61'
ptxas info    : Function properties for _Z16getNeatestCenterILi128ELi64ELb1ELb1EEv10foo_params
    0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads
ptxas info    : Used 5 registers, 360 bytes cmem[0]
ptxas info    : Function properties for cudaMalloc
    0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads
ptxas info    : Function properties for cudaOccupancyMaxActiveBlocksPerMultiprocessor
    0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads
ptxas info    : Function properties for cudaGetDevice
    0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads
ptxas info    : Compiling entry function '_Z16getNeatestCenterILi32ELi32ELb1ELb1EEv10foo_params' for 'sm_61'
ptxas info    : Function properties for _Z16getNeatestCenterILi32ELi32ELb1ELb1EEv10foo_params
    0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads
ptxas info    : Used 5 registers, 360 bytes cmem[0]
ptxas info    : Function properties for cudaFuncGetAttributes
    0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads
ptxas info    : Function properties for cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags
    0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads

几点：

• 64536 bytes cmem[3] 显示了我们指定的用户可控常量内存库的大小
• ptxas info : Used 5 registers, 360 bytes cmem[0] 显示函数的寄存器使用情况，cmem[0] 是内部保留的常量内存库，用于保存内核参数和编译器放入常量内存的任何其他内容。请注意，寄存器溢出会进入本地内存，而不是常量内存。

现在让我们运行设备链接步骤：

$ nvcc -arch=sm_61 -gencode=arch=compute_61,code=sm_61, -g -dlink -Xnvlink="-v" -o constmemuse.o constmemuse.cu.o

nvlink info    : 9944 bytes gmem, 64792 bytes cmem[3] (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi1ELi1ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 10 registers, 0 stack, 2056 bytes smem, 448 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi1ELi1ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 10 registers, 0 stack, 2056 bytes smem, 448 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi1ELi0ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 10 registers, 0 stack, 2056 bytes smem, 448 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi1ELi0ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 10 registers, 0 stack, 2056 bytes smem, 448 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi0ELi1ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 20 registers, 0 stack, 2056 bytes smem, 448 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi0ELi1ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 23 registers, 0 stack, 2056 bytes smem, 448 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi0ELi0ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 28 registers, 0 stack, 2056 bytes smem, 448 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi0ELi0ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 23 registers, 0 stack, 2056 bytes smem, 448 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi1ELi1ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 10 registers, 0 stack, 2056 bytes smem, 416 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi1ELi1ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 10 registers, 0 stack, 2056 bytes smem, 416 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi1ELi0ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 10 registers, 0 stack, 2056 bytes smem, 416 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi1ELi0ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 10 registers, 0 stack, 2056 bytes smem, 416 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi0ELi1ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 12 registers, 0 stack, 2056 bytes smem, 416 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi0ELi1ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 17 registers, 0 stack, 2056 bytes smem, 416 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi0ELi0ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 14 registers, 0 stack, 2056 bytes smem, 416 bytes cmem[0], 4 bytes cmem[2], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi0ELi0ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_': (target: sm_61)
nvlink info    : used 16 registers, 0 stack, 2056 bytes smem, 416 bytes cmem[0], 4 bytes cmem[2], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi1ELi1ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 6 registers, 0 stack, 0 bytes smem, 424 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi1ELi1ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 6 registers, 0 stack, 0 bytes smem, 424 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi1ELi0ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 6 registers, 0 stack, 0 bytes smem, 424 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi1ELi0ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 6 registers, 0 stack, 0 bytes smem, 424 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi0ELi1ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 16 registers, 0 stack, 0 bytes smem, 424 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi0ELi1ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 14 registers, 0 stack, 0 bytes smem, 424 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi0ELi0ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 17 registers, 0 stack, 0 bytes smem, 424 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi1ELi1ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 6 registers, 0 stack, 0 bytes smem, 400 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi1ELi1ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 6 registers, 0 stack, 0 bytes smem, 400 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi1ELi0ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 6 registers, 0 stack, 0 bytes smem, 400 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi1ELi0ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 6 registers, 0 stack, 0 bytes smem, 400 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi0ELi1ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 8 registers, 0 stack, 0 bytes smem, 400 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi0ELi1ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 11 registers, 0 stack, 0 bytes smem, 400 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi0ELi0ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 12 registers, 0 stack, 0 bytes smem, 400 bytes cmem[0], 4 bytes cmem[2], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi0ELi0ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 11 registers, 0 stack, 0 bytes smem, 400 bytes cmem[0], 4 bytes cmem[2], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '__nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi0ELi0ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_': (target: sm_61)
nvlink info    : used 21 registers, 0 stack, 0 bytes smem, 424 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '_Z16getNeatestCenterILi32ELi32ELb1ELb1EEv10foo_params': (target: sm_61)
nvlink info    : used 6 registers, 0 stack, 0 bytes smem, 360 bytes cmem[0], 0 bytes lmem (target: sm_61)
nvlink info    : Function properties for '_Z16getNeatestCenterILi128ELi64ELb1ELb1EEv10foo_params': (target: sm_61)
nvlink info    : used 6 registers, 0 stack, 0 bytes smem, 360 bytes cmem[0], 0 bytes lmem (target: sm_61)

补充几点：

• 9944 bytes gmem, 64792 bytes cmem[3] 现在显示链接模块的全局和常量内存预留。如您所见，我们在常量 bank 0（用户可修改的 bank）中继承了 256 个额外字节，外加 9944 字节的静态保留全局内存。如果数组分配为 65536 字节（如问题所示），则链接将失败，因为它超过了 64kb 限制。
• 您可以看到在链接阶段（memcpy 和 memset）自动链接了许多设备运行时库函数

很明显，额外的恒定内存使用量正在连接设备运行时，可以通过cuobjdump post hoc 确认。编译后的对象：

$ cuobjdump -res-usage constmemuse.cu.o

Fatbin elf code:
================
arch = sm_61
code version = [1,7]
producer = <unknown>
host = linux
compile_size = 64bit
compressed

Resource usage:
 Common:
  GLOBAL:0 CONSTANT[3]:64536
 Function cudaDeviceGetAttribute:
  REG:5 STACK:0 SHARED:0 LOCAL:0 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z16getNeatestCenterILi128ELi64ELb1ELb1EEv10foo_params:
  REG:5 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:360 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function cudaMalloc:
  REG:5 STACK:0 SHARED:0 LOCAL:0 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function cudaOccupancyMaxActiveBlocksPerMultiprocessor:
  REG:5 STACK:0 SHARED:0 LOCAL:0 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function cudaGetDevice:
  REG:5 STACK:0 SHARED:0 LOCAL:0 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z16getNeatestCenterILi32ELi32ELb1ELb1EEv10foo_params:
  REG:5 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:360 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function cudaFuncGetAttributes:
  REG:5 STACK:0 SHARED:0 LOCAL:0 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags:
  REG:5 STACK:0 SHARED:0 LOCAL:0 TEXTURE:0 SURFACE:0 SAMPLER:0

Fatbin ptx code:
================
arch = sm_61
code version = [6,4]
producer = <unknown>
host = linux
compile_size = 64bit
compressed
ptxasOptions = -v --compile-only  

以及链接后的对象：

$ cuobjdump -res-usage constmemuse.o

Fatbin elf code:
================
arch = sm_61
code version = [1,7]
producer = <unknown>
host = linux
compile_size = 64bit

Resource usage:
 Common:
  GLOBAL:9944 CONSTANT[3]:64792
 Function _Z16getNeatestCenterILi128ELi64ELb1ELb1EEv10foo_params:
  REG:6 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:360 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function _Z16getNeatestCenterILi32ELi32ELb1ELb1EEv10foo_params:
  REG:6 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:360 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi0ELi0ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:21 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:424 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi0ELi0ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:11 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:400 CONSTANT[2]:4 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi0ELi0ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:12 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:400 CONSTANT[2]:4 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi0ELi1ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:11 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:400 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi0ELi1ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:8 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:400 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi1ELi0ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:6 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:400 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi1ELi0ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:6 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:400 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi1ELi1ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:6 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:400 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceIjLi1ELi1ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:6 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:400 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi0ELi0ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:17 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:424 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi0ELi1ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:14 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:424 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi0ELi1ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:16 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:424 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi1ELi0ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:6 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:424 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi1ELi0ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:6 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:424 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi1ELi1ELi0EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:6 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:424 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memset_3d_deviceImLi1ELi1ELi1EEvPhhjT_S1_S1_S1_S1_jjjjjjjS1_S0_:
  REG:6 STACK:0 SHARED:0 LOCAL:0 CONSTANT[0]:424 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi0ELi0ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:16 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:416 CONSTANT[2]:4 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi0ELi0ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:14 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:416 CONSTANT[2]:4 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi0ELi1ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:17 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:416 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi0ELi1ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:12 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:416 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi1ELi0ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:10 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:416 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi1ELi0ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:10 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:416 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi1ELi1ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:10 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:416 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceIjLi1ELi1ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:10 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:416 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi0ELi0ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:23 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:448 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi0ELi0ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:28 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:448 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi0ELi1ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:23 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:448 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi0ELi1ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:20 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:448 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi1ELi0ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:10 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:448 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi1ELi0ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:10 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:448 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi1ELi1ELi0EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:10 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:448 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function __nv_static_51__38_cuda_device_runtime_compute_75_cpp1_ii_8b1a5d37__Z16memcpy_3d_deviceImLi1ELi1ELi1EEvPKhPhT_S3_S3_S3_S3_S3_S3_jjjjjjjjS3_S1_S2_:
  REG:10 STACK:0 SHARED:2056 LOCAL:0 CONSTANT[0]:448 TEXTURE:0 SURFACE:0 SAMPLER:0
 Function cudaCGGetIntrinsicHandle:
  REG:6 STACK:0 SHARED:0 LOCAL:0 TEXTURE:0 SURFACE:0 SAMPLER:0

已在接受的答案中证明，数学库可以为一些三角函数和超越函数的系数和查找表保留常量内存。但是，在这种情况下，原因似乎是内核中使用协作组所产生的支持样板。进一步深入研究额外 bank 0 常量内存的确切来源需要对该代码进行反汇编和逆向工程，我现在不打算这样做。

Answer 2

这个答案是推测性的，因为 OP 没有提供最小但完整的重现代码。

GPU 包含多个用于程序存储的不同部分的常量内存库。这些银行之一供程序员使用。重要的是，CUDA 标准数学库代码使用相同的库，因为数学库代码通过函数内联成为程序员代码的一部分。在过去，这很明显，因为整个 CUDA 数学库最初只是几个头文件。

一些数学函数在内部需要小的常量数据表。具体的例子是sin、cos、tan。使用这些数学函数时，程序员可用的__constant__ 数据量从64KB 减少了少量。以下是一些用于演示的示例程序，使用 CUDA 8 工具链和-arch=sm_61 编译：

#include <stdio.h>
#include <stdlib.h>

#define CONST_MEM (65536)
__constant__ float d_cnst_centers[CONST_MEM / sizeof(float)] = {1};

__global__ void kernel (int i, float f)
{
    float r = d_cnst_centers[i] * expf(f);
    printf ("r=%15.8f\n", r);
}

int main (void)
{
    kernel<<<1,1>>>(0,25.0f);
    cudaDeviceSynchronize();
    return EXIT_SUCCESS;
}

这可以很好地编译并在运行时打印r=72004902912.00000000。现在让我们将expf 更改为sinf：

#include <stdio.h>
#include <stdlib.h>

#define CONST_MEM (65536)
__constant__ float d_cnst_centers[CONST_MEM / sizeof(float)] = {1};

__global__ void kernel (int i, float f)
{
    float r = d_cnst_centers[i] * sinf(f);
    printf ("r=%15.8f\n", r);
}

int main (void)
{
    kernel<<<1,1>>>(0,25.0f);
    cudaDeviceSynchronize();
    return EXIT_SUCCESS;
}

这会在编译期间引发错误： ptxas error : File uses too much global constant data (0x10018 bytes, 0x10000 max)

如果我们改用双精度函数sin，则需要更多的常量内存：

#include <stdio.h>
#include <stdlib.h>

#define CONST_MEM (65536)
__constant__ float d_cnst_centers[CONST_MEM / sizeof(float)] = {1};

__global__ void kernel (int i, float f)
{
    float r = d_cnst_centers[i] * sin((double)f);
    printf ("r=%15.8f\n", r);
}

int main (void)
{
    kernel<<<1,1>>>(0,25.0f);
    cudaDeviceSynchronize();
    return EXIT_SUCCESS;
}

我们收到错误消息： ptxas error : File uses too much global constant data (0x10110 bytes, 0x10000 max)