xref: /petsc/src/mat/impls/sell/seq/seqcuda/sellcuda.cu (revision 4e58db63d9a16e1e6d7a6b59b385108b2e578961)

#include <cuda_runtime.h>

#include <petscdevice_cuda.h>
#include <../src/mat/impls/sell/seq/sell.h> /*I   "petscmat.h"  I*/

#define SLICE_HEIGHT 16

typedef struct {
  PetscInt  *colidx; /* column index */
  MatScalar *val;
  PetscInt  *sliidx;
  PetscInt   nonzerostate;
  PetscInt   kernelchoice;
  PetscInt   blocky;
} Mat_SeqSELLCUDA;

static PetscErrorCode MatSeqSELLCUDA_Destroy(Mat_SeqSELLCUDA **cudastruct)
{
  PetscFunctionBegin;
  if (*cudastruct) {
    if ((*cudastruct)->colidx) { PetscCallCUDA(cudaFree((*cudastruct)->colidx)); }
    if ((*cudastruct)->val) { PetscCallCUDA(cudaFree((*cudastruct)->val)); }
    if ((*cudastruct)->sliidx) { PetscCallCUDA(cudaFree((*cudastruct)->sliidx)); }
    PetscCall(PetscFree(*cudastruct));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode MatSeqSELLCUDACopyToGPU(Mat A)
{
  Mat_SeqSELLCUDA *cudastruct = (Mat_SeqSELLCUDA *)A->spptr;
  Mat_SeqSELL     *a          = (Mat_SeqSELL *)A->data;

  PetscFunctionBegin;
  if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
    PetscCall(PetscLogEventBegin(MAT_CUDACopyToGPU, A, 0, 0, 0));
    if (A->assembled && A->nonzerostate == cudastruct->nonzerostate) {
      /* copy values only */
      PetscCallCUDA(cudaMemcpy(cudastruct->val, a->val, a->sliidx[a->totalslices] * sizeof(MatScalar), cudaMemcpyHostToDevice));
      PetscCall(PetscLogCpuToGpu(a->sliidx[a->totalslices] * (sizeof(MatScalar))));
    } else {
      if (cudastruct->colidx) { PetscCallCUDA(cudaFree(cudastruct->colidx)); }
      if (cudastruct->val) { PetscCallCUDA(cudaFree(cudastruct->val)); }
      if (cudastruct->sliidx) { PetscCallCUDA(cudaFree(cudastruct->sliidx)); }
      PetscCallCUDA(cudaMalloc((void **)&(cudastruct->colidx), a->maxallocmat * sizeof(PetscInt)));
      PetscCallCUDA(cudaMalloc((void **)&(cudastruct->val), a->maxallocmat * sizeof(MatScalar)));
      /* copy values, nz or maxallocmat? */
      PetscCallCUDA(cudaMemcpy(cudastruct->colidx, a->colidx, a->sliidx[a->totalslices] * sizeof(PetscInt), cudaMemcpyHostToDevice));
      PetscCallCUDA(cudaMemcpy(cudastruct->val, a->val, a->sliidx[a->totalslices] * sizeof(MatScalar), cudaMemcpyHostToDevice));

      PetscCallCUDA(cudaMalloc((void **)&(cudastruct->sliidx), (a->totalslices + 1) * sizeof(PetscInt)));
      PetscCallCUDA(cudaMemcpy(cudastruct->sliidx, a->sliidx, (a->totalslices + 1) * sizeof(PetscInt), cudaMemcpyHostToDevice));
      PetscCall(PetscLogCpuToGpu(a->sliidx[a->totalslices] * (sizeof(MatScalar) + sizeof(PetscInt)) + (a->totalslices + 1) * sizeof(PetscInt)));
      cudastruct->nonzerostate = A->nonzerostate;
    }
    PetscCallCUDA(WaitForCUDA());
    PetscCall(PetscLogEventEnd(MAT_CUDACopyToGPU, A, 0, 0, 0));
    A->offloadmask = PETSC_OFFLOAD_BOTH;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

__global__ void matmult_seqsell_basic_kernel(PetscInt nrows, PetscInt sliceheight, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
{
  PetscInt  i, row, slice_id, row_in_slice;
  MatScalar sum;
  /* one thread per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / sliceheight;
    row_in_slice = row % sliceheight;
    sum          = 0.0;
    for (i = sliidx[slice_id] + row_in_slice; i < sliidx[slice_id + 1]; i += sliceheight) sum += aval[i] * x[acolidx[i]];
    y[row] = sum;
  }
}

__global__ void matmultadd_seqsell_basic_kernel(PetscInt nrows, PetscInt sliceheight, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
{
  PetscInt  i, row, slice_id, row_in_slice;
  MatScalar sum;
  /* one thread per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / sliceheight;
    row_in_slice = row % sliceheight;
    sum          = 0.0;
    for (i = sliidx[slice_id] + row_in_slice; i < sliidx[slice_id + 1]; i += sliceheight) sum += aval[i] * x[acolidx[i]];
    z[row] = y[row] + sum;
  }
}

/* use 1 block per slice, suitable for large slice width */
template <int BLOCKY>
__global__ void matmult_seqsell_tiled_kernel9(PetscInt nrows, PetscInt sliceheight, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
{
  __shared__ MatScalar shared[32][BLOCKY];
  PetscInt             i, row, slice_id = blockIdx.x;
  int                  tid = threadIdx.x + threadIdx.y * 32;
  /* transposed index */
  int         tidx = tid % BLOCKY;
  int         tidy = tid / BLOCKY;
  PetscScalar t    = 0.0;

  row = slice_id * sliceheight + threadIdx.x % sliceheight;
  if (row < nrows) {
    for (i = sliidx[slice_id] + threadIdx.x + 32 * threadIdx.y; i < sliidx[slice_id + 1]; i += 32 * BLOCKY) t += aval[i] * x[acolidx[i]];
  }
#pragma unroll
  for (int offset = 16; offset >= sliceheight; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset); }
  /* transpose layout to reduce each row using warp shfl */
  shared[threadIdx.x][threadIdx.y] = t;
  __syncthreads();
  t = shared[tidy][tidx];
#pragma unroll
  for (int offset = BLOCKY / 2; offset > 0; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset, BLOCKY); }
  if (tidx == 0 && tidy < sliceheight) { shared[0][tidy] = t; }
  __syncthreads();
  if (row < nrows && threadIdx.y == 0 && threadIdx.x < sliceheight) { y[row] = shared[0][threadIdx.x]; }
}

/* use 1 warp per slice, suitable for small slice width */
__global__ void matmult_seqsell_tiled_kernel7(PetscInt nrows, PetscInt sliceheight, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
{
  PetscInt i, row, slice_id;
  slice_id = blockIdx.x * blockDim.y + threadIdx.y;
  row      = slice_id * sliceheight + threadIdx.x % sliceheight;
  double t = 0.0;
  if (row < nrows) {
    for (i = sliidx[slice_id] + threadIdx.x; i < sliidx[slice_id + 1]; i += 32) t += aval[i] * x[acolidx[i]];
  }
#pragma unroll
  for (int offset = 16; offset >= sliceheight; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset); }
  if (row < nrows && threadIdx.x < sliceheight) { y[row] = t; }
}

__global__ void matmult_seqsell_tiled_kernel6(PetscInt nrows, PetscInt totalslices, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
{
  __shared__ MatScalar shared[512];
  PetscInt             i, row, slice_id, row_in_slice;
  /* multiple threads per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / SLICE_HEIGHT;
    row_in_slice = row % SLICE_HEIGHT;

    shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    for (i = sliidx[slice_id] + row_in_slice + SLICE_HEIGHT * threadIdx.y; i < sliidx[slice_id + 1]; i += SLICE_HEIGHT * blockDim.y) shared[threadIdx.y * blockDim.x + threadIdx.x] += aval[i] * x[acolidx[i]];
    __syncthreads();
    if (threadIdx.y < 16) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 16) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 8) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 8) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 1) {
      shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
      y[row] = shared[threadIdx.x];
    }
  }
}

__global__ void matmult_seqsell_tiled_kernel5(PetscInt nrows, PetscInt totalslices, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
{
  __shared__ MatScalar shared[512];
  PetscInt             i, row, slice_id, row_in_slice;
  /* multiple threads per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / SLICE_HEIGHT;
    row_in_slice = row % SLICE_HEIGHT;

    shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    for (i = sliidx[slice_id] + row_in_slice + SLICE_HEIGHT * threadIdx.y; i < sliidx[slice_id + 1]; i += SLICE_HEIGHT * blockDim.y) shared[threadIdx.y * blockDim.x + threadIdx.x] += aval[i] * x[acolidx[i]];
    __syncthreads();
    if (threadIdx.y < 8) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 8) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 1) {
      shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
      y[row] = shared[threadIdx.x];
    }
  }
}

__global__ void matmult_seqsell_tiled_kernel4(PetscInt nrows, PetscInt totalslices, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
{
  __shared__ MatScalar shared[512];
  PetscInt             i, row, slice_id, row_in_slice;
  /* multiple threads per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / SLICE_HEIGHT;
    row_in_slice = row % SLICE_HEIGHT;

    shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    for (i = sliidx[slice_id] + row_in_slice + SLICE_HEIGHT * threadIdx.y; i < sliidx[slice_id + 1]; i += SLICE_HEIGHT * blockDim.y) shared[threadIdx.y * blockDim.x + threadIdx.x] += aval[i] * x[acolidx[i]];
    __syncthreads();
    if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 1) {
      shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
      y[row] = shared[threadIdx.x];
    }
  }
}

__global__ void matmult_seqsell_tiled_kernel3(PetscInt nrows, PetscInt totalslices, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
{
  __shared__ MatScalar shared[512];
  PetscInt             i, row, slice_id, row_in_slice;
  /* multiple threads per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / SLICE_HEIGHT;
    row_in_slice = row % SLICE_HEIGHT;

    shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    for (i = sliidx[slice_id] + row_in_slice + SLICE_HEIGHT * threadIdx.y; i < sliidx[slice_id + 1]; i += SLICE_HEIGHT * blockDim.y) shared[threadIdx.y * blockDim.x + threadIdx.x] += aval[i] * x[acolidx[i]];
    __syncthreads();
    if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 1) {
      shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
      y[row] = shared[threadIdx.x];
    }
  }
}

__global__ void matmult_seqsell_tiled_kernel2(PetscInt nrows, PetscInt totalslices, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
{
  __shared__ MatScalar shared[512];
  PetscInt             i, row, slice_id, row_in_slice;
  /* multiple threads per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / SLICE_HEIGHT;
    row_in_slice = row % SLICE_HEIGHT;

    shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    for (i = sliidx[slice_id] + row_in_slice + SLICE_HEIGHT * threadIdx.y; i < sliidx[slice_id + 1]; i += SLICE_HEIGHT * blockDim.y) shared[threadIdx.y * blockDim.x + threadIdx.x] += aval[i] * x[acolidx[i]];
    __syncthreads();
    if (threadIdx.y < 1) {
      shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
      y[row] = shared[threadIdx.x];
    }
  }
}

__global__ void matmultadd_seqsell_tiled_kernel6(PetscInt nrows, PetscInt totalslices, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
{
  __shared__ MatScalar shared[512];
  PetscInt             i, row, slice_id, row_in_slice;
  /* multiple threads per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / SLICE_HEIGHT;
    row_in_slice = row % SLICE_HEIGHT;

    shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    for (i = sliidx[slice_id] + row_in_slice + SLICE_HEIGHT * threadIdx.y; i < sliidx[slice_id + 1]; i += SLICE_HEIGHT * blockDim.y) shared[threadIdx.y * blockDim.x + threadIdx.x] += aval[i] * x[acolidx[i]];
    __syncthreads();
    if (threadIdx.y < 16) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 16) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 8) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 8) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 1) {
      shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
      z[row] = y[row] + shared[threadIdx.x];
    }
  }
}

__global__ void matmultadd_seqsell_tiled_kernel5(PetscInt nrows, PetscInt totalslices, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
{
  __shared__ MatScalar shared[512];
  PetscInt             i, row, slice_id, row_in_slice;
  /* multiple threads per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / SLICE_HEIGHT;
    row_in_slice = row % SLICE_HEIGHT;

    shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    for (i = sliidx[slice_id] + row_in_slice + SLICE_HEIGHT * threadIdx.y; i < sliidx[slice_id + 1]; i += SLICE_HEIGHT * blockDim.y) shared[threadIdx.y * blockDim.x + threadIdx.x] += aval[i] * x[acolidx[i]];
    __syncthreads();
    if (threadIdx.y < 8) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 8) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 1) {
      shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
      z[row] = y[row] + shared[threadIdx.x];
    }
  }
}

__global__ void matmultadd_seqsell_tiled_kernel4(PetscInt nrows, PetscInt totalslices, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
{
  __shared__ MatScalar shared[512];
  PetscInt             i, row, slice_id, row_in_slice;
  /* multiple threads per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / SLICE_HEIGHT;
    row_in_slice = row % SLICE_HEIGHT;

    shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    for (i = sliidx[slice_id] + row_in_slice + SLICE_HEIGHT * threadIdx.y; i < sliidx[slice_id + 1]; i += SLICE_HEIGHT * blockDim.y) shared[threadIdx.y * blockDim.x + threadIdx.x] += aval[i] * x[acolidx[i]];
    __syncthreads();
    if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 1) {
      shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
      z[row] = y[row] + shared[threadIdx.x];
    }
  }
}

__global__ void matmultadd_seqsell_tiled_kernel3(PetscInt nrows, PetscInt totalslices, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
{
  __shared__ MatScalar shared[512];
  PetscInt             i, row, slice_id, row_in_slice;
  /* multiple threads per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / SLICE_HEIGHT;
    row_in_slice = row % SLICE_HEIGHT;

    shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    for (i = sliidx[slice_id] + row_in_slice + SLICE_HEIGHT * threadIdx.y; i < sliidx[slice_id + 1]; i += SLICE_HEIGHT * blockDim.y) shared[threadIdx.y * blockDim.x + threadIdx.x] += aval[i] * x[acolidx[i]];
    __syncthreads();
    if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    __syncthreads();
    if (threadIdx.y < 1) {
      shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
      z[row] = y[row] + shared[threadIdx.x];
    }
  }
}

__global__ void matmultadd_seqsell_tiled_kernel2(PetscInt nrows, PetscInt totalslices, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
{
  __shared__ MatScalar shared[512];
  PetscInt             i, row, slice_id, row_in_slice;
  /* multiple threads per row. */
  row = blockIdx.x * blockDim.x + threadIdx.x;
  if (row < nrows) {
    slice_id     = row / SLICE_HEIGHT;
    row_in_slice = row % SLICE_HEIGHT;

    shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    for (i = sliidx[slice_id] + row_in_slice + SLICE_HEIGHT * threadIdx.y; i < sliidx[slice_id + 1]; i += SLICE_HEIGHT * blockDim.y) shared[threadIdx.y * blockDim.x + threadIdx.x] += aval[i] * x[acolidx[i]];
    __syncthreads();
    if (threadIdx.y < 1) {
      shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
      z[row] = y[row] + shared[threadIdx.x];
    }
  }
}

PetscErrorCode MatMult_SeqSELLCUDA(Mat A, Vec xx, Vec yy)
{
  Mat_SeqSELL       *a          = (Mat_SeqSELL *)A->data;
  Mat_SeqSELLCUDA   *cudastruct = (Mat_SeqSELLCUDA *)A->spptr;
  PetscScalar       *y;
  const PetscScalar *x;
  PetscInt           totalslices = a->totalslices, nrows = A->rmap->n, sliceheight = a->sliceheight;
  MatScalar         *aval;
  PetscInt          *acolidx;
  PetscInt          *sliidx;
  PetscInt           nblocks, blocksize = 512; /* blocksize must be multiple of SLICE_HEIGHT*32 */
  dim3               block2(256, 2), block4(128, 4), block8(64, 8), block16(32, 16), block32(16, 32);

  PetscFunctionBegin;
  PetscCheck(32 % sliceheight == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "The kernel requires a slice height be a divisor of 32, but the input matrix has a slice height of %" PetscInt_FMT, sliceheight);
  PetscCall(MatSeqSELLCUDACopyToGPU(A));
  /* cudastruct may not be available until MatSeqSELLCUDACopyToGPU() is called */
  aval    = cudastruct->val;
  acolidx = cudastruct->colidx;
  sliidx  = cudastruct->sliidx;

  PetscCall(VecCUDAGetArrayRead(xx, &x));
  PetscCall(VecCUDAGetArrayWrite(yy, &y));
  PetscCall(PetscLogGpuTimeBegin());

  switch (cudastruct->kernelchoice) {
  case 9:
    nblocks = 1 + (nrows - 1) / sliceheight;
    if (cudastruct->blocky == 2) {
      matmult_seqsell_tiled_kernel9<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    } else if (cudastruct->blocky == 4) {
      matmult_seqsell_tiled_kernel9<4><<<nblocks, dim3(32, 4)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    } else if (cudastruct->blocky == 8) {
      matmult_seqsell_tiled_kernel9<8><<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    } else if (cudastruct->blocky == 16) {
      matmult_seqsell_tiled_kernel9<16><<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    } else if (cudastruct->blocky == 32) {
      matmult_seqsell_tiled_kernel9<32><<<nblocks, dim3(32, 32)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    }
    break;
  case 7:
    nblocks = 1 + (nrows - 1) / (2 * sliceheight);
    if (cudastruct->blocky == 2) {
      matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    } else if (cudastruct->blocky == 4) {
      matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 4)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    } else if (cudastruct->blocky == 8) {
      matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    } else if (cudastruct->blocky == 16) {
      matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    } else if (cudastruct->blocky == 32) {
      matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 32)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    } else {
      matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    }
    break;
  case 6:
    nblocks = 1 + (nrows - 1) / (blocksize / 32); /* 1 slice per block if blocksize=512 */
    matmult_seqsell_tiled_kernel6<<<nblocks, block32>>>(nrows, totalslices, acolidx, aval, sliidx, x, y);
    break;
  case 5:
    nblocks = 1 + (nrows - 1) / (blocksize / 16); /* 2 slices per block if blocksize=512*/
    matmult_seqsell_tiled_kernel5<<<nblocks, block16>>>(nrows, totalslices, acolidx, aval, sliidx, x, y);
    break;
  case 4:
    nblocks = 1 + (nrows - 1) / (blocksize / 8); /* 4 slices per block if blocksize=512 */
    matmult_seqsell_tiled_kernel4<<<nblocks, block8>>>(nrows, totalslices, acolidx, aval, sliidx, x, y);
    break;
  case 3:
    nblocks = 1 + (nrows - 1) / (blocksize / 4); /* 8 slices per block if blocksize=512 */
    matmult_seqsell_tiled_kernel3<<<nblocks, block4>>>(nrows, totalslices, acolidx, aval, sliidx, x, y);
    break;
  case 2: /* 16 slices per block if blocksize=512 */
    nblocks = 1 + (nrows - 1) / (blocksize / 2);
    matmult_seqsell_tiled_kernel2<<<nblocks, block2>>>(nrows, totalslices, acolidx, aval, sliidx, x, y);
    break;
  case 1: /* 32 slices per block if blocksize=512 */
    nblocks = 1 + (nrows - 1) / blocksize;
    matmult_seqsell_basic_kernel<<<nblocks, blocksize>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    break;
  case 0:
    if (sliceheight * a->maxslicewidth > 20800) { /* important threshold */
      nblocks = 1 + (nrows - 1) / sliceheight;
      matmult_seqsell_tiled_kernel9<32><<<nblocks, dim3(32, 32)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    } else {
      PetscInt avgslicesize = sliceheight * a->avgslicewidth;
      if (avgslicesize <= 96) {
        nblocks = 1 + (nrows - 1) / (2 * sliceheight); /* two slices per block */
        matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
      } else if (avgslicesize <= 432) {
        nblocks = 1 + (nrows - 1) / sliceheight;
        matmult_seqsell_tiled_kernel9<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
      } else if (avgslicesize <= 2400) {
        nblocks = 1 + (nrows - 1) / sliceheight;
        matmult_seqsell_tiled_kernel9<8><<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
      } else {
        nblocks = 1 + (nrows - 1) / sliceheight;
        matmult_seqsell_tiled_kernel9<16><<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
      }
    }
    break;
  }
  PetscCall(PetscLogGpuTimeEnd());
  PetscCall(VecCUDARestoreArrayRead(xx, &x));
  PetscCall(VecCUDARestoreArrayWrite(yy, &y));
  PetscCall(PetscLogGpuFlops(2.0 * a->nz - a->nonzerorowcnt));
  PetscFunctionReturn(PETSC_SUCCESS);
}

PetscErrorCode MatMultAdd_SeqSELLCUDA(Mat A, Vec xx, Vec yy, Vec zz)
{
  Mat_SeqSELL       *a          = (Mat_SeqSELL *)A->data;
  Mat_SeqSELLCUDA   *cudastruct = (Mat_SeqSELLCUDA *)A->spptr;
  PetscScalar       *z;
  const PetscScalar *y, *x;
  PetscInt           totalslices = a->totalslices, nrows = A->rmap->n, sliceheight = a->sliceheight;
  MatScalar         *aval    = cudastruct->val;
  PetscInt          *acolidx = cudastruct->colidx;
  PetscInt          *sliidx  = cudastruct->sliidx;

  PetscFunctionBegin;
  PetscCheck(sliceheight == 16, PETSC_COMM_SELF, PETSC_ERR_SUP, "The kernel requires a slice height of 16, but the input matrix has a slice height of %" PetscInt_FMT, sliceheight);
  PetscCall(MatSeqSELLCUDACopyToGPU(A));
  if (a->nz) {
    PetscInt nblocks, blocksize = 512;
    dim3     block2(256, 2), block4(128, 4), block8(64, 8), block16(32, 16), block32(16, 32);
    PetscCall(VecCUDAGetArrayRead(xx, &x));
    PetscCall(VecCUDAGetArrayRead(yy, &y));
    PetscCall(VecCUDAGetArrayWrite(zz, &z));
    PetscCall(PetscLogGpuTimeBegin());

    switch (cudastruct->kernelchoice) {
    case 6:
      nblocks = 1 + (nrows - 1) / (blocksize / 32);
      matmultadd_seqsell_tiled_kernel6<<<nblocks, block32>>>(nrows, totalslices, acolidx, aval, sliidx, x, y, z);
      break;
    case 5:
      nblocks = 1 + (nrows - 1) / (blocksize / 16);
      matmultadd_seqsell_tiled_kernel5<<<nblocks, block16>>>(nrows, totalslices, acolidx, aval, sliidx, x, y, z);
      break;
    case 4:
      nblocks = 1 + (nrows - 1) / (blocksize / 8);
      matmultadd_seqsell_tiled_kernel4<<<nblocks, block8>>>(nrows, totalslices, acolidx, aval, sliidx, x, y, z);
      break;
    case 3:
      nblocks = 1 + (nrows - 1) / (blocksize / 4);
      matmultadd_seqsell_tiled_kernel3<<<nblocks, block4>>>(nrows, totalslices, acolidx, aval, sliidx, x, y, z);
      break;
    case 2:
      nblocks = 1 + (nrows - 1) / (blocksize / 2);
      matmultadd_seqsell_tiled_kernel2<<<nblocks, block2>>>(nrows, totalslices, acolidx, aval, sliidx, x, y, z);
      break;
    case 1:
      nblocks = 1 + (nrows - 1) / blocksize;
      matmultadd_seqsell_basic_kernel<<<nblocks, blocksize>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
      break;
    case 0: /* TODO */
      break;
    }
    PetscCall(PetscLogGpuTimeEnd());
    PetscCall(VecCUDARestoreArrayRead(xx, &x));
    PetscCall(VecCUDARestoreArrayRead(yy, &y));
    PetscCall(VecCUDARestoreArrayWrite(zz, &z));
    PetscCall(PetscLogGpuFlops(2.0 * a->nz));
  } else {
    PetscCall(VecCopy(yy, zz));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode MatSetFromOptions_SeqSELLCUDA(Mat A, PetscOptionItems *PetscOptionsObject)
{
  Mat_SeqSELLCUDA *cudastruct = (Mat_SeqSELLCUDA *)A->spptr;
  PetscInt         kernel, blocky;
  PetscBool        flg;

  PetscFunctionBegin;
  PetscOptionsHeadBegin(PetscOptionsObject, "SeqSELLCUDA options");
  PetscCall(PetscOptionsGetInt(NULL, NULL, "-mat_sell_spmv_cuda_kernel", &kernel, &flg));
  if (flg) {
    PetscCheck(kernel >= 0 && kernel <= 9, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Wrong kernel choice: %" PetscInt_FMT " it should be in [0,9]", kernel);
    cudastruct->kernelchoice = kernel;
  }
  PetscCall(PetscOptionsGetInt(NULL, NULL, "-mat_sell_spmv_cuda_blocky", &blocky, &flg));
  if (flg) {
    PetscCheck(blocky == 2 || blocky == 4 || blocky == 8 || blocky == 16 || blocky == 32, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Unsupported blocky: %" PetscInt_FMT " it should be in {2,4,8,16,32}", kernel);
    cudastruct->blocky = blocky;
  }
  PetscOptionsHeadEnd();
  PetscFunctionReturn(PETSC_SUCCESS);
}

PETSC_INTERN PetscErrorCode MatAssemblyEnd_SpMV_Preprocessing_Private(Mat A)
{
  Mat_SeqSELL *a = (Mat_SeqSELL *)A->data;

  PetscCall(MatSeqSELLGetAvgSliceWidth(A, &a->avgslicewidth));
  PetscCall(MatSeqSELLGetMaxSliceWidth(A, &a->maxslicewidth));
  PetscCall(MatSeqSELLGetFillRatio(A, &a->fillratio));
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode MatAssemblyEnd_SeqSELLCUDA(Mat A, MatAssemblyType mode)
{
  PetscFunctionBegin;
  PetscCall(MatAssemblyEnd_SeqSELL(A, mode));
  PetscCall(MatAssemblyEnd_SpMV_Preprocessing_Private(A));
  if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
  if (A->factortype == MAT_FACTOR_NONE) { PetscCall(MatSeqSELLCUDACopyToGPU(A)); }
  A->ops->mult    = MatMult_SeqSELLCUDA;
  A->ops->multadd = MatMultAdd_SeqSELLCUDA;
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode MatDestroy_SeqSELLCUDA(Mat A)
{
  PetscFunctionBegin;
  if (A->factortype == MAT_FACTOR_NONE) {
    if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) { PetscCall(MatSeqSELLCUDA_Destroy((Mat_SeqSELLCUDA **)&A->spptr)); }
  }
  PetscCall(MatDestroy_SeqSELL(A));
  PetscFunctionReturn(PETSC_SUCCESS);
}

PETSC_INTERN PetscErrorCode MatConvert_SeqSELL_SeqSELLCUDA(Mat);
static PetscErrorCode       MatDuplicate_SeqSELLCUDA(Mat A, MatDuplicateOption cpvalues, Mat *B)
{
  PetscFunctionBegin;
  PetscCall(MatDuplicate_SeqSELL(A, cpvalues, B));
  PetscCall(MatConvert_SeqSELL_SeqSELLCUDA(*B));
  PetscFunctionReturn(PETSC_SUCCESS);
}

PETSC_EXTERN PetscErrorCode MatConvert_SeqSELL_SeqSELLCUDA(Mat B)
{
  Mat_SeqSELLCUDA *cudastruct;

  PetscFunctionBegin;
  PetscCall(PetscFree(B->defaultvectype));
  PetscCall(PetscStrallocpy(VECCUDA, &B->defaultvectype));

  if (!B->spptr) {
    if (B->factortype == MAT_FACTOR_NONE) {
      PetscCall(PetscNew(&cudastruct));
      B->spptr = cudastruct;
    }
  }

  B->ops->assemblyend    = MatAssemblyEnd_SeqSELLCUDA;
  B->ops->destroy        = MatDestroy_SeqSELLCUDA;
  B->ops->setfromoptions = MatSetFromOptions_SeqSELLCUDA;
  B->ops->mult           = MatMult_SeqSELLCUDA;
  B->ops->multadd        = MatMultAdd_SeqSELLCUDA;
  B->ops->duplicate      = MatDuplicate_SeqSELLCUDA;

  /* No need to assemble SeqSELL, but need to do the preprocessing for SpMV */
  PetscCall(MatAssemblyEnd_SpMV_Preprocessing_Private(B));

  PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQSELLCUDA));
  B->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
  PetscFunctionReturn(PETSC_SUCCESS);
}

PETSC_EXTERN PetscErrorCode MatCreate_SeqSELLCUDA(Mat B)
{
  PetscFunctionBegin;
  PetscCall(MatCreate_SeqSELL(B));
  PetscCall(MatConvert_SeqSELL_SeqSELLCUDA(B));
  PetscFunctionReturn(PETSC_SUCCESS);
}