Hi

I need to find the maxima in a cube, say 128 x 128 x 128 (this size can vary, with the constraint that each dimension is a power of 2, >= 32) of floats, stored linearly in global memory.

To do this I have thread blocks of size 8 x 8 x 8 (i.e. 512 threads) which each work on their own 8 x 8 x 8 sub-cube of the cube. I want to load the values of the 10 x 10 x 10 sub-cube centred on the 8 x 8 x 8 sub-cube into shared memory, so that I can then do the maxima detection.

I would like to load this 10 x 10 x 10 sub-cube from global memory into shared memory using as coalesced reads as possible. Can anybody help?

Many thanks.

Hi

I need to find the maxima in a cube, say 128 x 128 x 128 (this size can vary, with the constraint that each dimension is a power of 2, >= 32) of floats, stored linearly in global memory.

To do this I have thread blocks of size 8 x 8 x 8 (i.e. 512 threads) which each work on their own 8 x 8 x 8 sub-cube of the cube. I want to load the values of the 10 x 10 x 10 sub-cube centred on the 8 x 8 x 8 sub-cube into shared memory, so that I can then do the maxima detection.

I would like to load this 10 x 10 x 10 sub-cube from global memory into shared memory using as coalesced reads as possible. Can anybody help?

Many thanks.

If the cube with 128^3 elements is stored linear in memory, wouldn’t it be easier for you just to skip the 3D abstraction on-chip in shared memory and just linearly search through the data?

You can use thrust or code that people have published on these forums that takes care of linear memory using a reduction scheme to find min and max.

If the cube with 128^3 elements is stored linear in memory, wouldn’t it be easier for you just to skip the 3D abstraction on-chip in shared memory and just linearly search through the data?

You can use thrust or code that people have published on these forums that takes care of linear memory using a reduction scheme to find min and max.

Sorry, I wasn’t clear first time. I want to find all the local maxima in the cube. A voxel is a local maximum if it is greater than it’s 26 neighbours in the 3d volume.

I currently have the following:

[codebox]global void integer_modes2(float *d_V, int d0, int d1, int d2, int *I)

{

``````int wid = threadIdx.x + (threadIdx.y + threadIdx.z * 8) * 8;

int bz = blockIdx.y / (d1 / 8);

int by = blockIdx.y - (bz * (d1 / 8));

bz *= 8;

by *= 8;

int w_num = wid / WARP_SIZE;

wid = wid % WARP_SIZE;
``````

// Cache for 10 x 10 x 10

``````__shared__ float v[10][10][10];

int x, y, z;
``````

// Each warp will load 10 floats coalesced

``````for (z = 0; z < 10; ++z) {

for (y = 0; y < 10; ++y) {

if (((y + z * 10) % ((8*8*8)/WARP_SIZE)) == w_num) {

if ((z == 0 && bz == 0) || (z == 9 && bz == d2-8) || (y == 0 && by == 0) || (y == 9 && by == d1-8)) {

// Boundary case

if (wid < 10) {

v[z][y][wid] = BOUNDARY_VAL;

}

} else {

// Compute the offset for each thread

x = (wid+blockIdx.x*8-1) + ((y+by) + (z+bz) * d1) * d0 % WARP_SIZE;

if (x < 10) {

// Boundary case

if ((x == 0 && blockIdx.x == 0) || (x == 9 && blockIdx.x == d0/8-1)) {

v[z][y][x] = BOUNDARY_VAL;

} else {

int vidx = (x+blockIdx.x*8-1) + ((y+by) + (z+bz) * d1) * d0;

v[z][y][x] = d_V[vidx];

}

}

}

}

}

}
``````

// Record value

``````__syncthreads();

if (val == 0.0f)

return;
``````

// Determine if mode

``````    for (z = 0; z < 2; ++z) {

for (y = 0; y < 2; ++y) {

for (x = 0; x < 2; ++x) {

return;

}

}

}

if (off < MAX_NUM_FEATURES)

I[off] = (threadIdx.x + blockIdx.x * 8) + ((threadIdx.y + by) + (threadIdx.z + bz) * d1) * d0;
``````

}[/codebox]

I launch the kernel with:

integer_modes2<<<dim3(d0/8, (d1/8)*(d2/8)), dim3(8, 8, 8)>>>(d_V, d0, d1, d2, I);

However, I currently get an unspecified launch failure. Am I on the right track, and if so, why do I get the error? If not, does anyone have another suggestion?

Many thanks.

Sorry, I wasn’t clear first time. I want to find all the local maxima in the cube. A voxel is a local maximum if it is greater than it’s 26 neighbours in the 3d volume.

I currently have the following:

[codebox]global void integer_modes2(float *d_V, int d0, int d1, int d2, int *I)

{

``````int wid = threadIdx.x + (threadIdx.y + threadIdx.z * 8) * 8;

int bz = blockIdx.y / (d1 / 8);

int by = blockIdx.y - (bz * (d1 / 8));

bz *= 8;

by *= 8;

int w_num = wid / WARP_SIZE;

wid = wid % WARP_SIZE;
``````

// Cache for 10 x 10 x 10

``````__shared__ float v[10][10][10];

int x, y, z;
``````

// Each warp will load 10 floats coalesced

``````for (z = 0; z < 10; ++z) {

for (y = 0; y < 10; ++y) {

if (((y + z * 10) % ((8*8*8)/WARP_SIZE)) == w_num) {

if ((z == 0 && bz == 0) || (z == 9 && bz == d2-8) || (y == 0 && by == 0) || (y == 9 && by == d1-8)) {

// Boundary case

if (wid < 10) {

v[z][y][wid] = BOUNDARY_VAL;

}

} else {

// Compute the offset for each thread

x = (wid+blockIdx.x*8-1) + ((y+by) + (z+bz) * d1) * d0 % WARP_SIZE;

if (x < 10) {

// Boundary case

if ((x == 0 && blockIdx.x == 0) || (x == 9 && blockIdx.x == d0/8-1)) {

v[z][y][x] = BOUNDARY_VAL;

} else {

int vidx = (x+blockIdx.x*8-1) + ((y+by) + (z+bz) * d1) * d0;

v[z][y][x] = d_V[vidx];

}

}

}

}

}

}
``````

// Record value

``````__syncthreads();

if (val == 0.0f)

return;
``````

// Determine if mode

``````    for (z = 0; z < 2; ++z) {

for (y = 0; y < 2; ++y) {

for (x = 0; x < 2; ++x) {

return;

}

}

}

if (off < MAX_NUM_FEATURES)

I[off] = (threadIdx.x + blockIdx.x * 8) + ((threadIdx.y + by) + (threadIdx.z + bz) * d1) * d0;
``````

}[/codebox]

I launch the kernel with:

integer_modes2<<<dim3(d0/8, (d1/8)*(d2/8)), dim3(8, 8, 8)>>>(d_V, d0, d1, d2, I);

However, I currently get an unspecified launch failure. Am I on the right track, and if so, why do I get the error? If not, does anyone have another suggestion?

Many thanks.