Indexing CUDA索引未按预期工作

我正在尝试使用PyCUDA处理一个二维数组，我需要每个线程的x，y坐标

此问题已被提出并回答，但链接的解决方案不适用于超出块大小的二维数据。为什么?

下面是我用来帮助解决这个问题的SourceModule：

mod = SourceModule("""
  __global__ void kIndexTest(float *M, float *X, float*Y)
  {
    int bIdx = blockIdx.x + blockIdx.y * gridDim.x; 
    int idx = bIdx * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x;

    /* this array shows me the unique thread indices */
    M[idx] = idx;

    /* these arrays should capture x, y for each unique index */    
    X[idx] = (blockDim.x * blockIdx.x) + threadIdx.x;
    Y[idx] = (blockDim.y * blockIdx.y) + threadIdx.y;

  }
  """)

我正在执行内核，如下所示：

gIndexTest = mod.get_function("kIndexTest")

dims = (8, 8)

M = gpuarray.to_gpu(numpy.zeros(dims, dtype=numpy.float32))
X = gpuarray.to_gpu(numpy.zeros(dims, dtype=numpy.float32))
Y = gpuarray.to_gpu(numpy.zeros(dims, dtype=numpy.float32))

gIndexTest(M, X, Y, block=(4, 4, 1), grid=(2, 2, 1))

M返回我测试过的所有维度和所有块/网格配置的正确索引。仅当X和Y的尺寸与块尺寸相同时，X和Y才返回正确的坐标值，但不返回我期望的其他值。例如，上述配置产生：

M:
[[  0.   1.   2.   3.   4.   5.   6.   7.]
 [  8.   9.  10.  11.  12.  13.  14.  15.]
 [ 16.  17.  18.  19.  20.  21.  22.  23.]
 [ 24.  25.  26.  27.  28.  29.  30.  31.]
 [ 32.  33.  34.  35.  36.  37.  38.  39.]
 [ 40.  41.  42.  43.  44.  45.  46.  47.]
 [ 48.  49.  50.  51.  52.  53.  54.  55.]
 [ 56.  57.  58.  59.  60.  61.  62.  63.]] (correct)

X:
[[ 0.  1.  2.  3.  0.  1.  2.  3.]
 [ 0.  1.  2.  3.  0.  1.  2.  3.]
 [ 4.  5.  6.  7.  4.  5.  6.  7.]
 [ 4.  5.  6.  7.  4.  5.  6.  7.]
 [ 0.  1.  2.  3.  0.  1.  2.  3.]
 [ 0.  1.  2.  3.  0.  1.  2.  3.]
 [ 4.  5.  6.  7.  4.  5.  6.  7.]
 [ 4.  5.  6.  7.  4.  5.  6.  7.]] (not what I expect)

Y:
[[ 0.  0.  0.  0.  1.  1.  1.  1.]
 [ 2.  2.  2.  2.  3.  3.  3.  3.]
 [ 0.  0.  0.  0.  1.  1.  1.  1.]
 [ 2.  2.  2.  2.  3.  3.  3.  3.]
 [ 4.  4.  4.  4.  5.  5.  5.  5.]
 [ 6.  6.  6.  6.  7.  7.  7.  7.]
 [ 4.  4.  4.  4.  5.  5.  5.  5.]
 [ 6.  6.  6.  6.  7.  7.  7.  7.]] (not what I expect)

以下是我对X和Y的实际期望：

X:
[[ 0.  1.  2.  3.  4.  5.  6.  7.]
 [ 0.  1.  2.  3.  4.  5.  6.  7.]
 [ 0.  1.  2.  3.  4.  5.  6.  7.]
 [ 0.  1.  2.  3.  4.  5.  6.  7.]
 [ 0.  1.  2.  3.  4.  5.  6.  7.]
 [ 0.  1.  2.  3.  4.  5.  6.  7.]
 [ 0.  1.  2.  3.  4.  5.  6.  7.]
 [ 0.  1.  2.  3.  4.  5.  6.  7.]] (only works when X dims = block dims)

Y:
[[ 0.  0.  0.  0.  0.  0.  0.  0.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.]
 [ 2.  2.  2.  2.  2.  2.  2.  2.]
 [ 3.  3.  3.  3.  3.  3.  3.  3.]
 [ 4.  4.  4.  4.  4.  4.  4.  4.]
 [ 5.  5.  5.  5.  5.  5.  5.  5.]
 [ 6.  6.  6.  6.  6.  6.  6.  6.]
 [ 7.  7.  7.  7.  7.  7.  7.  7.]] (only works when Y dims = block dims)

我不明白什么？

以下是我的设备查询：

Device 0: "GeForce GT 755M"
  CUDA Driver Version / Runtime Version          7.5 / 6.5
  CUDA Capability Major/Minor version number:    3.0
  Total amount of global memory:                 1024 MBytes (1073283072 bytes)
  ( 2) Multiprocessors, (192) CUDA Cores/MP:     384 CUDA Cores
  GPU Clock rate:                                1085 MHz (1.09 GHz)
  Memory Clock rate:                             2500 Mhz
  Memory Bus Width:                              128-bit
  L2 Cache Size:                                 262144 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device PCI Bus ID / PCI location ID:           1 / 0

一切都“如广告所示”正常运转。这里的问题是，您将不兼容的索引方案混合在一起，这会产生不一致的结果

如果希望

X

和

Y

按预期显示，则需要以不同的方式计算

idx

：

  __global__ void kIndexTest(float *M, float *X, float*Y)
  {
    int xidx = (blockDim.x * blockIdx.x) + threadIdx.x;
    int yidx = (blockDim.y * blockIdx.y) + threadIdx.y;
    int idx = (gridDim.x * blockDim.x * yidx) + xidx;

    X[idx] = xidx;
    Y[idx] = yidx;
    M[idx] = idx;
  }

---

在这个方案中，

xidx

和

yidx

是网格x和y坐标，

idx

是全局索引，所有这些都假设列的主要顺序（即x是变化最快的维度）。

Aha！非常感谢。要回答这个问题，我不明白的是，我的索引方案并不总是将x和y放置在x和y中适当的x，y位置。不是因为x，y计算有缺陷，而是因为索引计算有缺陷。@Dariencerane:不，这不是正确的解释。您的

idx

计算没有“缺陷”，它只是计算网格中唯一索引方案的另一种方法。在二维网格中计算唯一索引有（至少）四种不同的方法，您只是选择了混合两种不兼容的方法。对于此应用程序来说，这是一种有缺陷的方法。我的假设是，我应该首先生成一个唯一的“idx”，然后使用它将x和y映射到x和y。我不知道我使用的是两种不同的索引方案，因此我从未想过您提到的不兼容性。