Date: 2025-03-29T05:58:35+00:00

Actual source code: sellcuda.cu

      1: #include <cuda_runtime.h>

      3: #include <petscdevice_cuda.h>
      4: #include <petsc/private/cupmatomics.hpp>
      5: #include <../src/mat/impls/sell/seq/sell.h>

      7: #define SLICE_HEIGHT 16

      9: typedef struct {
     10:   PetscInt   maxallocmat;
     11:   PetscInt   totalentries;
     12:   PetscInt  *colidx; /* column index array, device pointer */
     13:   MatScalar *val;    /* value array, device pointer */
     14:   PetscInt   totalslices;
     15:   PetscInt  *sliidx; /* slice index array, device pointer */
     16:   PetscInt   nonzerostate;
     17:   PetscInt   kernelchoice;
     18:   PetscInt   blocky;
     19:   PetscInt   chunksperblock;
     20:   PetscInt   totalchunks;
     21:   PetscInt  *chunk_slice_map; /* starting slice for each chunk, device pointer */
     22: } Mat_SeqSELLCUDA;

     24: static PetscErrorCode MatSeqSELLCUDA_Destroy(Mat_SeqSELLCUDA **cudastruct)
     25: {
     26:   PetscFunctionBegin;
     27:   if (*cudastruct) {
     28:     if ((*cudastruct)->colidx) { PetscCallCUDA(cudaFree((*cudastruct)->colidx)); }
     29:     if ((*cudastruct)->val) { PetscCallCUDA(cudaFree((*cudastruct)->val)); }
     30:     if ((*cudastruct)->sliidx) { PetscCallCUDA(cudaFree((*cudastruct)->sliidx)); }
     31:     if ((*cudastruct)->chunk_slice_map) { PetscCallCUDA(cudaFree((*cudastruct)->chunk_slice_map)); }
     32:     PetscCall(PetscFree(*cudastruct));
     33:   }
     34:   PetscFunctionReturn(PETSC_SUCCESS);
     35: }

     37: static PetscErrorCode MatSeqSELLCUDACopyToGPU(Mat A)
     38: {
     39:   Mat_SeqSELLCUDA *cudastruct = (Mat_SeqSELLCUDA *)A->spptr;
     40:   Mat_SeqSELL     *a          = (Mat_SeqSELL *)A->data;

     42:   PetscFunctionBegin;
     43:   if (A->offloadmask == PETSC_OFFLOAD_UNALLOCATED || A->offloadmask == PETSC_OFFLOAD_CPU) {
     44:     PetscCall(PetscLogEventBegin(MAT_CUDACopyToGPU, A, 0, 0, 0));
     45:     if (A->assembled && A->nonzerostate == cudastruct->nonzerostate) {
     46:       /* copy values only */
     47:       PetscCallCUDA(cudaMemcpy(cudastruct->val, a->val, a->sliidx[a->totalslices] * sizeof(MatScalar), cudaMemcpyHostToDevice));
     48:       PetscCall(PetscLogCpuToGpu(a->sliidx[a->totalslices] * (sizeof(MatScalar))));
     49:     } else {
     50:       if (cudastruct->colidx) { PetscCallCUDA(cudaFree(cudastruct->colidx)); }
     51:       if (cudastruct->val) { PetscCallCUDA(cudaFree(cudastruct->val)); }
     52:       if (cudastruct->sliidx) { PetscCallCUDA(cudaFree(cudastruct->sliidx)); }
     53:       if (cudastruct->chunk_slice_map) { PetscCallCUDA(cudaFree(cudastruct->chunk_slice_map)); }
     54:       cudastruct->maxallocmat  = a->maxallocmat;
     55:       cudastruct->totalentries = a->sliidx[a->totalslices];
     56:       cudastruct->totalslices  = a->totalslices;
     57:       cudastruct->totalchunks  = a->totalchunks;
     58:       PetscCallCUDA(cudaMalloc((void **)&cudastruct->colidx, a->maxallocmat * sizeof(*cudastruct->colidx)));
     59:       PetscCallCUDA(cudaMalloc((void **)&cudastruct->val, a->maxallocmat * sizeof(*cudastruct->val)));
     60:       /* copy values, nz or maxallocmat? */
     61:       PetscCallCUDA(cudaMemcpy(cudastruct->colidx, a->colidx, a->sliidx[a->totalslices] * sizeof(*a->colidx), cudaMemcpyHostToDevice));
     62:       PetscCallCUDA(cudaMemcpy(cudastruct->val, a->val, a->sliidx[a->totalslices] * sizeof(*a->val), cudaMemcpyHostToDevice));

     64:       PetscCallCUDA(cudaMalloc((void **)&cudastruct->sliidx, (a->totalslices + 1) * sizeof(*cudastruct->sliidx)));
     65:       PetscCallCUDA(cudaMemcpy(cudastruct->sliidx, a->sliidx, (a->totalslices + 1) * sizeof(*a->sliidx), cudaMemcpyHostToDevice));
     66:       PetscCallCUDA(cudaMalloc((void **)&cudastruct->chunk_slice_map, a->totalchunks * sizeof(*cudastruct->chunk_slice_map)));
     67:       PetscCallCUDA(cudaMemcpy(cudastruct->chunk_slice_map, a->chunk_slice_map, a->totalchunks * sizeof(*a->chunk_slice_map), cudaMemcpyHostToDevice));
     68:       PetscCall(PetscLogCpuToGpu(a->sliidx[a->totalslices] * (sizeof(MatScalar) + sizeof(PetscInt)) + (a->totalslices + 1 + a->totalchunks) * sizeof(PetscInt)));
     69:     }
     70:     PetscCallCUDA(WaitForCUDA());
     71:     PetscCall(PetscLogEventEnd(MAT_CUDACopyToGPU, A, 0, 0, 0));
     72:     A->offloadmask = PETSC_OFFLOAD_BOTH;
     73:   }
     74:   PetscFunctionReturn(PETSC_SUCCESS);
     75: }

     77: static __global__ void matmult_seqsell_basic_kernel(PetscInt nrows, PetscInt sliceheight, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
     78: {
     79:   PetscInt  i, row, slice_id, row_in_slice;
     80:   MatScalar sum;
     81:   /* one thread per row. */
     82:   row = blockIdx.x * blockDim.x + threadIdx.x;
     83:   if (row < nrows) {
     84:     slice_id     = row / sliceheight;
     85:     row_in_slice = row % sliceheight;
     86:     sum          = 0.0;
     87:     for (i = sliidx[slice_id] + row_in_slice; i < sliidx[slice_id + 1]; i += sliceheight) sum += aval[i] * x[acolidx[i]];
     88:     y[row] = sum;
     89:   }
     90: }

     92: static __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)
     93: {
     94:   PetscInt  i, row, slice_id, row_in_slice;
     95:   MatScalar sum;
     96:   /* one thread per row. */
     97:   row = blockIdx.x * blockDim.x + threadIdx.x;
     98:   if (row < nrows) {
     99:     slice_id     = row / sliceheight;
    100:     row_in_slice = row % sliceheight;
    101:     sum          = 0.0;
    102:     for (i = sliidx[slice_id] + row_in_slice; i < sliidx[slice_id + 1]; i += sliceheight) sum += aval[i] * x[acolidx[i]];
    103:     z[row] = y[row] + sum;
    104:   }
    105: }

    107: #if !defined(PETSC_USE_COMPLEX)
    108: /* use 1 block per slice, suitable for large slice width */
    109: template <int BLOCKY>
    110: __global__ void matmult_seqsell_tiled_kernel9(PetscInt nrows, PetscInt sliceheight, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
    111: {
    112:   __shared__ MatScalar shared[32][BLOCKY];
    113:   PetscInt             i, row, slice_id = blockIdx.x;
    114:   int                  tid = threadIdx.x + threadIdx.y * 32;
    115:   /* transposed index */
    116:   int         tidx = tid % BLOCKY;
    117:   int         tidy = tid / BLOCKY;
    118:   PetscScalar t    = 0.0;

    120:   row = slice_id * sliceheight + threadIdx.x % sliceheight;
    121:   if (row < nrows) {
    122:     for (i = sliidx[slice_id] + threadIdx.x + 32 * threadIdx.y; i < sliidx[slice_id + 1]; i += 32 * BLOCKY) t += aval[i] * x[acolidx[i]];
    123:   }
    124:   #pragma unroll
    125:   for (int offset = 16; offset >= sliceheight; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset); }
    126:   /* transpose layout to reduce each row using warp shfl */
    127:   if (threadIdx.x < sliceheight) shared[threadIdx.x][threadIdx.y] = t;
    128:   __syncthreads();
    129:   if (tidy < sliceheight) t = shared[tidy][tidx];
    130:   #pragma unroll
    131:   for (int offset = BLOCKY / 2; offset > 0; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset, BLOCKY); }
    132:   if (tidx == 0 && tidy < sliceheight) { shared[0][tidy] = t; }
    133:   __syncthreads();
    134:   if (row < nrows && threadIdx.y == 0 && threadIdx.x < sliceheight) { y[row] = shared[0][threadIdx.x]; }
    135: }

    137: /* use 1 block per slice, suitable for large slice width */
    138: template <int BLOCKY>
    139: __global__ void matmultadd_seqsell_tiled_kernel9(PetscInt nrows, PetscInt sliceheight, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
    140: {
    141:   __shared__ MatScalar shared[32][BLOCKY];
    142:   PetscInt             i, row, slice_id = blockIdx.x;
    143:   int                  tid = threadIdx.x + threadIdx.y * 32;
    144:   /* transposed index */
    145:   int         tidx = tid % BLOCKY;
    146:   int         tidy = tid / BLOCKY;
    147:   PetscScalar t    = 0.0;

    149:   row = slice_id * sliceheight + threadIdx.x % sliceheight;
    150:   if (row < nrows) {
    151:     for (i = sliidx[slice_id] + threadIdx.x + 32 * threadIdx.y; i < sliidx[slice_id + 1]; i += 32 * BLOCKY) t += aval[i] * x[acolidx[i]];
    152:   }
    153:   #pragma unroll
    154:   for (int offset = 16; offset >= sliceheight; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset); }
    155:   /* transpose layout to reduce each row using warp shfl */
    156:   if (threadIdx.x < sliceheight) shared[threadIdx.x][threadIdx.y] = t;
    157:   __syncthreads();
    158:   if (tidy < sliceheight) t = shared[tidy][tidx];
    159:   #pragma unroll
    160:   for (int offset = BLOCKY / 2; offset > 0; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset, BLOCKY); }
    161:   if (tidx == 0 && tidy < sliceheight) { shared[0][tidy] = t; }
    162:   __syncthreads();
    163:   if (row < nrows && threadIdx.y == 0 && threadIdx.x < sliceheight) { z[row] = y[row] + shared[0][threadIdx.x]; }
    164: }

    166: template <int BLOCKY>
    167: __device__ __forceinline__ static bool segment_scan(PetscInt flag[], MatScalar shared[], PetscScalar *val)
    168: {
    169:   bool head = true;
    170:   #pragma unroll
    171:   for (int i = 1; i < BLOCKY * 2; i <<= 1) {
    172:     int halfwarpid                         = threadIdx.y * 2 + threadIdx.x / 16;
    173:     shared[threadIdx.x + threadIdx.y * 32] = 0;
    174:     if (halfwarpid >= i && flag[halfwarpid - i] == flag[halfwarpid]) {
    175:       shared[threadIdx.x + threadIdx.y * 32] = *val;
    176:       if (i == 1) head = false;
    177:     }
    178:     __syncthreads();
    179:     if (halfwarpid < BLOCKY * 2 - i) *val += shared[threadIdx.x + threadIdx.y * 32 + i * 16];
    180:     __syncthreads();
    181:   }
    182:   return head;
    183: }

    185: /* load-balancing version. Chunksize is equal to the number of threads per block */
    186: template <int BLOCKY>
    187: __global__ void matmult_seqsell_tiled_kernel8(PetscInt nrows, PetscInt sliceheight, PetscInt chunksperblock, PetscInt totalchunks, const PetscInt *chunk_slice_map, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
    188: {
    189:   __shared__ MatScalar shared[BLOCKY * 32];
    190:   PetscInt             gid, row, start_slice, cid;
    191:   PetscScalar          t = 0.0;
    192:   AtomicAdd<MatScalar> atomAdd;
    193:   /* zero out y */
    194:   for (int iter = 0; iter < 1 + (nrows - 1) / (gridDim.x * 32 * BLOCKY); iter++) {
    195:     gid = gridDim.x * 32 * BLOCKY * iter + blockIdx.x * BLOCKY * 32 + threadIdx.y * 32 + threadIdx.x;
    196:     if (gid < nrows) y[gid] = 0.0;
    197:   }
    198:   for (int iter = 0; iter < chunksperblock; iter++) {
    199:     cid = blockIdx.x * chunksperblock + iter; /* chunk id */
    200:     if (cid < totalchunks) {
    201:       start_slice = chunk_slice_map[cid]; /* starting slice at each iteration */
    202:       gid         = cid * BLOCKY * 32 + threadIdx.y * 32 + threadIdx.x;
    203:       if ((cid + 1) * BLOCKY * 32 > sliidx[start_slice + 1]) { /* this iteration covers more than one slice */
    204:         __shared__ PetscInt flag[BLOCKY * 2];
    205:         bool                write;
    206:         PetscInt            slice_id = start_slice, totalslices = PetscCeilIntMacro(nrows, sliceheight), totalentries = sliidx[totalslices];
    207:         /* find out the slice that this element belongs to */
    208:         while (gid < totalentries && gid >= sliidx[slice_id + 1]) slice_id++;
    209:         if (threadIdx.x % 16 == 0) flag[threadIdx.y * 2 + threadIdx.x / 16] = slice_id;
    210:         row = slice_id * sliceheight + threadIdx.x % sliceheight;
    211:         if (row < nrows && gid < totalentries) t = aval[gid] * x[acolidx[gid]];
    212:         __syncthreads();
    213:         write = segment_scan<BLOCKY>(flag, shared, &t);
    214:         if (row < nrows && gid < totalentries && write) atomAdd(y[row], t);
    215:         t = 0.0;
    216:       } else { /* this iteration covers only one slice */
    217:         row = start_slice * sliceheight + threadIdx.x % sliceheight;
    218:         if (row < nrows) t += aval[gid] * x[acolidx[gid]];
    219:         if (iter == chunksperblock - 1 || (cid + 2) * BLOCKY * 32 > sliidx[start_slice + 1]) { /* last iteration or next iteration covers more than one slice */
    220:           int tid = threadIdx.x + threadIdx.y * 32, tidx = tid % BLOCKY, tidy = tid / BLOCKY;
    221:   /* reduction and write to output vector */
    222:   #pragma unroll
    223:           for (int offset = 16; offset >= sliceheight; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset); }
    224:           /* transpose layout to reduce each row using warp shfl */
    225:           if (threadIdx.x < sliceheight) shared[threadIdx.x * BLOCKY + threadIdx.y] = t; /* shared[threadIdx.x][threadIdx.y] = t */
    226:           __syncthreads();
    227:           if (tidy < sliceheight) t = shared[tidy * BLOCKY + tidx]; /* shared[tidy][tidx] */
    228:   #pragma unroll
    229:           for (int offset = BLOCKY / 2; offset > 0; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset, BLOCKY); }
    230:           if (tidx == 0 && tidy < sliceheight) { shared[tidy] = t; /* shared[0][tidy] = t */ }
    231:           __syncthreads();
    232:           if (row < nrows && threadIdx.y == 0 && threadIdx.x < sliceheight) atomAdd(y[row], shared[threadIdx.x]); /* shared[0][threadIdx.x] */
    233:           t = 0.0;
    234:         }
    235:       }
    236:     }
    237:   }
    238: }

    240: /* load-balancing version. Chunksize is equal to the number of threads per block */
    241: template <int BLOCKY>
    242: __global__ void matmultadd_seqsell_tiled_kernel8(PetscInt nrows, PetscInt sliceheight, PetscInt chunksperblock, PetscInt totalchunks, const PetscInt *chunk_slice_map, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
    243: {
    244:   __shared__ MatScalar shared[BLOCKY * 32];
    245:   PetscInt             gid, row, start_slice, cid;
    246:   PetscScalar          t = 0.0;
    247:   AtomicAdd<MatScalar> atomAdd;
    248:   /* copy y to z */
    249:   for (int iter = 0; iter < 1 + (nrows - 1) / (gridDim.x * 32 * BLOCKY); iter++) {
    250:     gid = gridDim.x * 32 * BLOCKY * iter + blockIdx.x * BLOCKY * 32 + threadIdx.y * 32 + threadIdx.x;
    251:     if (gid < nrows) z[gid] = y[gid];
    252:   }
    253:   for (int iter = 0; iter < chunksperblock; iter++) {
    254:     cid = blockIdx.x * chunksperblock + iter; /* chunk id */
    255:     if (cid < totalchunks) {
    256:       start_slice = chunk_slice_map[cid]; /* starting slice at each iteration */
    257:       gid         = cid * BLOCKY * 32 + threadIdx.y * 32 + threadIdx.x;
    258:       if ((cid + 1) * BLOCKY * 32 > sliidx[start_slice + 1]) { /* this iteration covers more than one slice */
    259:         __shared__ PetscInt flag[BLOCKY * 2];
    260:         bool                write;
    261:         PetscInt            slice_id = start_slice, totalslices = PetscCeilIntMacro(nrows, sliceheight), totalentries = sliidx[totalslices];
    262:         /* find out the slice that this element belongs to */
    263:         while (gid < totalentries && gid >= sliidx[slice_id + 1]) slice_id++;
    264:         if (threadIdx.x % 16 == 0) flag[threadIdx.y * 2 + threadIdx.x / 16] = slice_id;
    265:         row = slice_id * sliceheight + threadIdx.x % sliceheight;
    266:         if (row < nrows && gid < totalentries) t = aval[gid] * x[acolidx[gid]];
    267:         __syncthreads();
    268:         write = segment_scan<BLOCKY>(flag, shared, &t);
    269:         if (row < nrows && gid < totalentries && write) atomAdd(z[row], t);
    270:         t = 0.0;
    271:       } else { /* this iteration covers only one slice */
    272:         row = start_slice * sliceheight + threadIdx.x % sliceheight;
    273:         if (row < nrows) t += aval[gid] * x[acolidx[gid]];
    274:         if (iter == chunksperblock - 1 || (cid + 2) * BLOCKY * 32 > sliidx[start_slice + 1]) { /* last iteration or next iteration covers more than one slice */
    275:           int tid = threadIdx.x + threadIdx.y * 32, tidx = tid % BLOCKY, tidy = tid / BLOCKY;
    276:   /* reduction and write to output vector */
    277:   #pragma unroll
    278:           for (int offset = 16; offset >= sliceheight; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset); }
    279:           /* transpose layout to reduce each row using warp shfl */
    280:           if (threadIdx.x < sliceheight) shared[threadIdx.x * BLOCKY + threadIdx.y] = t; /* shared[threadIdx.x][threadIdx.y] = t */
    281:           __syncthreads();
    282:           if (tidy < sliceheight) t = shared[tidy * BLOCKY + tidx]; /* shared[tidy][tidx] */
    283:   #pragma unroll
    284:           for (int offset = BLOCKY / 2; offset > 0; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset, BLOCKY); }
    285:           if (tidx == 0 && tidy < sliceheight) { shared[tidy] = t; /* shared[0][tidy] = t */ }
    286:           __syncthreads();
    287:           if (row < nrows && threadIdx.y == 0 && threadIdx.x < sliceheight) atomAdd(z[row], shared[threadIdx.x]); /* shared[0][threadIdx.x] */
    288:           t = 0.0;
    289:         }
    290:       }
    291:     }
    292:   }
    293: }

    295: /* use 1 warp per slice, suitable for small slice width */
    296: static __global__ void matmult_seqsell_tiled_kernel7(PetscInt nrows, PetscInt sliceheight, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
    297: {
    298:   PetscInt i, row, slice_id;
    299:   slice_id = blockIdx.x * blockDim.y + threadIdx.y;
    300:   row      = slice_id * sliceheight + threadIdx.x % sliceheight;
    301:   double t = 0.0;
    302:   if (row < nrows) {
    303:     for (i = sliidx[slice_id] + threadIdx.x; i < sliidx[slice_id + 1]; i += 32) t += aval[i] * x[acolidx[i]];
    304:   }
    305:   #pragma unroll
    306:   for (int offset = 16; offset >= sliceheight; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset); }
    307:   if (row < nrows && threadIdx.x < sliceheight) { y[row] = t; }
    308: }

    310: /* use 1 warp per slice, suitable for small slice width */
    311: static __global__ void matmultadd_seqsell_tiled_kernel7(PetscInt nrows, PetscInt sliceheight, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
    312: {
    313:   PetscInt i, row, slice_id;
    314:   slice_id = blockIdx.x * blockDim.y + threadIdx.y;
    315:   row      = slice_id * sliceheight + threadIdx.x % sliceheight;
    316:   double t = 0.0;
    317:   if (row < nrows) {
    318:     for (i = sliidx[slice_id] + threadIdx.x; i < sliidx[slice_id + 1]; i += 32) t += aval[i] * x[acolidx[i]];
    319:   }
    320:   #pragma unroll
    321:   for (int offset = 16; offset >= sliceheight; offset /= 2) { t += __shfl_down_sync(0xffffffff, t, offset); }
    322:   if (row < nrows && threadIdx.x < sliceheight) { z[row] = y[row] + t; }
    323: }
    324: #endif

    326: /***********  Kernel 2-6  are tied to slice height 16. They are kept only for performance comparison  **********/

    328: static __global__ void matmult_seqsell_tiled_kernel6(PetscInt nrows, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
    329: {
    330:   __shared__ MatScalar shared[512];
    331:   PetscInt             i, row, slice_id, row_in_slice;
    332:   /* multiple threads per row. */
    333:   row = blockIdx.x * blockDim.x + threadIdx.x;
    334:   if (row < nrows) {
    335:     slice_id     = row / SLICE_HEIGHT;
    336:     row_in_slice = row % SLICE_HEIGHT;

    338:     shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    339:     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]];
    340:     __syncthreads();
    341:     if (threadIdx.y < 16) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 16) * blockDim.x + threadIdx.x]; }
    342:     __syncthreads();
    343:     if (threadIdx.y < 8) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 8) * blockDim.x + threadIdx.x]; }
    344:     __syncthreads();
    345:     if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    346:     __syncthreads();
    347:     if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    348:     __syncthreads();
    349:     if (threadIdx.y < 1) {
    350:       shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
    351:       y[row] = shared[threadIdx.x];
    352:     }
    353:   }
    354: }

    356: static __global__ void matmult_seqsell_tiled_kernel5(PetscInt nrows, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
    357: {
    358:   __shared__ MatScalar shared[512];
    359:   PetscInt             i, row, slice_id, row_in_slice;
    360:   /* multiple threads per row. */
    361:   row = blockIdx.x * blockDim.x + threadIdx.x;
    362:   if (row < nrows) {
    363:     slice_id     = row / SLICE_HEIGHT;
    364:     row_in_slice = row % SLICE_HEIGHT;

    366:     shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    367:     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]];
    368:     __syncthreads();
    369:     if (threadIdx.y < 8) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 8) * blockDim.x + threadIdx.x]; }
    370:     __syncthreads();
    371:     if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    372:     __syncthreads();
    373:     if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    374:     __syncthreads();
    375:     if (threadIdx.y < 1) {
    376:       shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
    377:       y[row] = shared[threadIdx.x];
    378:     }
    379:   }
    380: }

    382: static __global__ void matmult_seqsell_tiled_kernel4(PetscInt nrows, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
    383: {
    384:   __shared__ MatScalar shared[512];
    385:   PetscInt             i, row, slice_id, row_in_slice;
    386:   /* multiple threads per row. */
    387:   row = blockIdx.x * blockDim.x + threadIdx.x;
    388:   if (row < nrows) {
    389:     slice_id     = row / SLICE_HEIGHT;
    390:     row_in_slice = row % SLICE_HEIGHT;

    392:     shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    393:     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]];
    394:     __syncthreads();
    395:     if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    396:     __syncthreads();
    397:     if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    398:     __syncthreads();
    399:     if (threadIdx.y < 1) {
    400:       shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
    401:       y[row] = shared[threadIdx.x];
    402:     }
    403:   }
    404: }

    406: static __global__ void matmult_seqsell_tiled_kernel3(PetscInt nrows, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
    407: {
    408:   __shared__ MatScalar shared[512];
    409:   PetscInt             i, row, slice_id, row_in_slice;
    410:   /* multiple threads per row. */
    411:   row = blockIdx.x * blockDim.x + threadIdx.x;
    412:   if (row < nrows) {
    413:     slice_id     = row / SLICE_HEIGHT;
    414:     row_in_slice = row % SLICE_HEIGHT;

    416:     shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    417:     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]];
    418:     __syncthreads();
    419:     if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    420:     __syncthreads();
    421:     if (threadIdx.y < 1) {
    422:       shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
    423:       y[row] = shared[threadIdx.x];
    424:     }
    425:   }
    426: }

    428: static __global__ void matmult_seqsell_tiled_kernel2(PetscInt nrows, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, PetscScalar *y)
    429: {
    430:   __shared__ MatScalar shared[512];
    431:   PetscInt             i, row, slice_id, row_in_slice;
    432:   /* multiple threads per row. */
    433:   row = blockIdx.x * blockDim.x + threadIdx.x;
    434:   if (row < nrows) {
    435:     slice_id     = row / SLICE_HEIGHT;
    436:     row_in_slice = row % SLICE_HEIGHT;

    438:     shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    439:     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]];
    440:     __syncthreads();
    441:     if (threadIdx.y < 1) {
    442:       shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
    443:       y[row] = shared[threadIdx.x];
    444:     }
    445:   }
    446: }

    448: static __global__ void matmultadd_seqsell_tiled_kernel6(PetscInt nrows, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
    449: {
    450:   __shared__ MatScalar shared[512];
    451:   PetscInt             i, row, slice_id, row_in_slice;
    452:   /* multiple threads per row. */
    453:   row = blockIdx.x * blockDim.x + threadIdx.x;
    454:   if (row < nrows) {
    455:     slice_id     = row / SLICE_HEIGHT;
    456:     row_in_slice = row % SLICE_HEIGHT;

    458:     shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    459:     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]];
    460:     __syncthreads();
    461:     if (threadIdx.y < 16) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 16) * blockDim.x + threadIdx.x]; }
    462:     __syncthreads();
    463:     if (threadIdx.y < 8) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 8) * blockDim.x + threadIdx.x]; }
    464:     __syncthreads();
    465:     if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    466:     __syncthreads();
    467:     if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    468:     __syncthreads();
    469:     if (threadIdx.y < 1) {
    470:       shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
    471:       z[row] = y[row] + shared[threadIdx.x];
    472:     }
    473:   }
    474: }

    476: static __global__ void matmultadd_seqsell_tiled_kernel5(PetscInt nrows, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
    477: {
    478:   __shared__ MatScalar shared[512];
    479:   PetscInt             i, row, slice_id, row_in_slice;
    480:   /* multiple threads per row. */
    481:   row = blockIdx.x * blockDim.x + threadIdx.x;
    482:   if (row < nrows) {
    483:     slice_id     = row / SLICE_HEIGHT;
    484:     row_in_slice = row % SLICE_HEIGHT;

    486:     shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    487:     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]];
    488:     __syncthreads();
    489:     if (threadIdx.y < 8) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 8) * blockDim.x + threadIdx.x]; }
    490:     __syncthreads();
    491:     if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    492:     __syncthreads();
    493:     if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    494:     __syncthreads();
    495:     if (threadIdx.y < 1) {
    496:       shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
    497:       z[row] = y[row] + shared[threadIdx.x];
    498:     }
    499:   }
    500: }

    502: static __global__ void matmultadd_seqsell_tiled_kernel4(PetscInt nrows, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
    503: {
    504:   __shared__ MatScalar shared[512];
    505:   PetscInt             i, row, slice_id, row_in_slice;
    506:   /* multiple threads per row. */
    507:   row = blockIdx.x * blockDim.x + threadIdx.x;
    508:   if (row < nrows) {
    509:     slice_id     = row / SLICE_HEIGHT;
    510:     row_in_slice = row % SLICE_HEIGHT;

    512:     shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    513:     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]];
    514:     __syncthreads();
    515:     if (threadIdx.y < 4) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 4) * blockDim.x + threadIdx.x]; }
    516:     __syncthreads();
    517:     if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    518:     __syncthreads();
    519:     if (threadIdx.y < 1) {
    520:       shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
    521:       z[row] = y[row] + shared[threadIdx.x];
    522:     }
    523:   }
    524: }

    526: static __global__ void matmultadd_seqsell_tiled_kernel3(PetscInt nrows, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
    527: {
    528:   __shared__ MatScalar shared[512];
    529:   PetscInt             i, row, slice_id, row_in_slice;
    530:   /* multiple threads per row. */
    531:   row = blockIdx.x * blockDim.x + threadIdx.x;
    532:   if (row < nrows) {
    533:     slice_id     = row / SLICE_HEIGHT;
    534:     row_in_slice = row % SLICE_HEIGHT;

    536:     shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    537:     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]];
    538:     __syncthreads();
    539:     if (threadIdx.y < 2) { shared[threadIdx.y * blockDim.x + threadIdx.x] += shared[(threadIdx.y + 2) * blockDim.x + threadIdx.x]; }
    540:     __syncthreads();
    541:     if (threadIdx.y < 1) {
    542:       shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
    543:       z[row] = y[row] + shared[threadIdx.x];
    544:     }
    545:   }
    546: }

    548: static __global__ void matmultadd_seqsell_tiled_kernel2(PetscInt nrows, const PetscInt *acolidx, const MatScalar *aval, const PetscInt *sliidx, const PetscScalar *x, const PetscScalar *y, PetscScalar *z)
    549: {
    550:   __shared__ MatScalar shared[512];
    551:   PetscInt             i, row, slice_id, row_in_slice;
    552:   /* multiple threads per row. */
    553:   row = blockIdx.x * blockDim.x + threadIdx.x;
    554:   if (row < nrows) {
    555:     slice_id     = row / SLICE_HEIGHT;
    556:     row_in_slice = row % SLICE_HEIGHT;

    558:     shared[threadIdx.y * blockDim.x + threadIdx.x] = 0.0;
    559:     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]];
    560:     __syncthreads();
    561:     if (threadIdx.y < 1) {
    562:       shared[threadIdx.x] += shared[blockDim.x + threadIdx.x];
    563:       z[row] = y[row] + shared[threadIdx.x];
    564:     }
    565:   }
    566: }

    568: static PetscErrorCode MatMult_SeqSELLCUDA(Mat A, Vec xx, Vec yy)
    569: {
    570:   Mat_SeqSELL       *a          = (Mat_SeqSELL *)A->data;
    571:   Mat_SeqSELLCUDA   *cudastruct = (Mat_SeqSELLCUDA *)A->spptr;
    572:   PetscScalar       *y;
    573:   const PetscScalar *x;
    574:   PetscInt           nrows = A->rmap->n, sliceheight = a->sliceheight;
    575:   MatScalar         *aval;
    576:   PetscInt          *acolidx;
    577:   PetscInt          *sliidx;
    578:   PetscInt           nblocks, blocksize = 512; /* blocksize must be multiple of SLICE_HEIGHT*32 */
    579:   dim3               block2(256, 2), block4(128, 4), block8(64, 8), block16(32, 16), block32(16, 32);
    580: #if !defined(PETSC_USE_COMPLEX)
    581:   PetscInt  chunksperblock, nchunks, *chunk_slice_map;
    582:   PetscReal maxoveravg;
    583: #endif

    585:   PetscFunctionBegin;
    586:   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);
    587:   PetscCheck(!(cudastruct->kernelchoice >= 2 && cudastruct->kernelchoice <= 6 && sliceheight != SLICE_HEIGHT), PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Kernel choices {2-6} requires the slice height of the matrix be 16, but the current slice height is %" PetscInt_FMT, sliceheight);
    588:   PetscCall(MatSeqSELLCUDACopyToGPU(A));
    589:   /* cudastruct may not be available until MatSeqSELLCUDACopyToGPU() is called */
    590:   aval    = cudastruct->val;
    591:   acolidx = cudastruct->colidx;
    592:   sliidx  = cudastruct->sliidx;

    594:   PetscCall(VecCUDAGetArrayRead(xx, &x));
    595:   PetscCall(VecCUDAGetArrayWrite(yy, &y));
    596:   PetscCall(PetscLogGpuTimeBegin());

    598:   switch (cudastruct->kernelchoice) {
    599: #if !defined(PETSC_USE_COMPLEX)
    600:   case 9:
    601:     nblocks = 1 + (nrows - 1) / sliceheight;
    602:     if (cudastruct->blocky == 2) {
    603:       matmult_seqsell_tiled_kernel9<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    604:     } else if (cudastruct->blocky == 4) {
    605:       matmult_seqsell_tiled_kernel9<4><<<nblocks, dim3(32, 4)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    606:     } else if (cudastruct->blocky == 8) {
    607:       matmult_seqsell_tiled_kernel9<8><<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    608:     } else if (cudastruct->blocky == 16) {
    609:       matmult_seqsell_tiled_kernel9<16><<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    610:     } else if (cudastruct->blocky == 32) {
    611:       matmult_seqsell_tiled_kernel9<32><<<nblocks, dim3(32, 32)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    612:     } else {
    613:       matmult_seqsell_tiled_kernel9<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    614:     }
    615:     break;
    616:   case 7:
    617:     nblocks = 1 + (nrows - 1) / (2 * sliceheight);
    618:     if (cudastruct->blocky == 2) {
    619:       matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    620:     } else if (cudastruct->blocky == 4) {
    621:       matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 4)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    622:     } else if (cudastruct->blocky == 8) {
    623:       matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    624:     } else if (cudastruct->blocky == 16) {
    625:       matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    626:     } else if (cudastruct->blocky == 32) {
    627:       matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 32)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    628:     } else {
    629:       matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    630:     }
    631:     break;
    632: #endif
    633:   case 6:
    634:     nblocks = 1 + (nrows - 1) / (blocksize / 32); /* 1 slice per block if blocksize=512 */
    635:     matmult_seqsell_tiled_kernel6<<<nblocks, block32>>>(nrows, acolidx, aval, sliidx, x, y);
    636:     break;
    637:   case 5:
    638:     nblocks = 1 + (nrows - 1) / (blocksize / 16); /* 2 slices per block if blocksize=512*/
    639:     matmult_seqsell_tiled_kernel5<<<nblocks, block16>>>(nrows, acolidx, aval, sliidx, x, y);
    640:     break;
    641:   case 4:
    642:     nblocks = 1 + (nrows - 1) / (blocksize / 8); /* 4 slices per block if blocksize=512 */
    643:     matmult_seqsell_tiled_kernel4<<<nblocks, block8>>>(nrows, acolidx, aval, sliidx, x, y);
    644:     break;
    645:   case 3:
    646:     nblocks = 1 + (nrows - 1) / (blocksize / 4); /* 8 slices per block if blocksize=512 */
    647:     matmult_seqsell_tiled_kernel3<<<nblocks, block4>>>(nrows, acolidx, aval, sliidx, x, y);
    648:     break;
    649:   case 2: /* 16 slices per block if blocksize=512 */
    650:     nblocks = 1 + (nrows - 1) / (blocksize / 2);
    651:     matmult_seqsell_tiled_kernel2<<<nblocks, block2>>>(nrows, acolidx, aval, sliidx, x, y);
    652:     break;
    653:   case 1: /* 32 slices per block if blocksize=512 */
    654:     nblocks = 1 + (nrows - 1) / blocksize;
    655:     matmult_seqsell_basic_kernel<<<nblocks, blocksize>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    656:     break;
    657: #if !defined(PETSC_USE_COMPLEX)
    658:   case 0:
    659:     maxoveravg = a->maxslicewidth / a->avgslicewidth;
    660:     if (maxoveravg > 12.0 && maxoveravg / nrows > 0.001) { /* important threshold */
    661:       /* each block handles approximately one slice */
    662:       PetscInt blocky = a->chunksize / 32;
    663:       nchunks         = cudastruct->totalchunks;
    664:       chunksperblock  = cudastruct->chunksperblock ? cudastruct->chunksperblock : 1 + (cudastruct->totalentries / cudastruct->totalslices - 1) / a->chunksize;
    665:       nblocks         = 1 + (nchunks - 1) / chunksperblock;
    666:       chunk_slice_map = cudastruct->chunk_slice_map;
    667:       if (blocky == 2) {
    668:         matmult_seqsell_tiled_kernel8<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y);
    669:       } else if (blocky == 4) {
    670:         matmult_seqsell_tiled_kernel8<4><<<nblocks, dim3(32, 4)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y);
    671:       } else if (blocky == 8) {
    672:         matmult_seqsell_tiled_kernel8<8><<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y);
    673:       } else if (blocky == 16) {
    674:         matmult_seqsell_tiled_kernel8<16><<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y);
    675:       } else if (blocky == 32) {
    676:         matmult_seqsell_tiled_kernel8<32><<<nblocks, dim3(32, 32)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y);
    677:       } else {
    678:         matmult_seqsell_tiled_kernel8<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y);
    679:       }
    680:     } else {
    681:       PetscInt avgslicesize = sliceheight * a->avgslicewidth;
    682:       if (avgslicesize <= 432) {
    683:         if (sliceheight * a->maxslicewidth < 2048 && nrows > 100000) {
    684:           nblocks = 1 + (nrows - 1) / (2 * sliceheight); /* two slices per block */
    685:           matmult_seqsell_tiled_kernel7<<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    686:         } else {
    687:           nblocks = 1 + (nrows - 1) / sliceheight;
    688:           matmult_seqsell_tiled_kernel9<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    689:         }
    690:       } else if (avgslicesize <= 2400) {
    691:         nblocks = 1 + (nrows - 1) / sliceheight;
    692:         matmult_seqsell_tiled_kernel9<8><<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    693:       } else {
    694:         nblocks = 1 + (nrows - 1) / sliceheight;
    695:         matmult_seqsell_tiled_kernel9<16><<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y);
    696:       }
    697:     }
    698:     break;
    699: #endif
    700:   default:
    701:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unsupported kernel choice %" PetscInt_FMT " for MatMult_SeqSELLCUDA.", cudastruct->kernelchoice);
    702:   }
    703:   PetscCall(PetscLogGpuTimeEnd());
    704:   PetscCall(VecCUDARestoreArrayRead(xx, &x));
    705:   PetscCall(VecCUDARestoreArrayWrite(yy, &y));
    706:   PetscCall(PetscLogGpuFlops(2.0 * a->nz - a->nonzerorowcnt));
    707:   PetscFunctionReturn(PETSC_SUCCESS);
    708: }

    710: static PetscErrorCode MatMultAdd_SeqSELLCUDA(Mat A, Vec xx, Vec yy, Vec zz)
    711: {
    712:   Mat_SeqSELL       *a          = (Mat_SeqSELL *)A->data;
    713:   Mat_SeqSELLCUDA   *cudastruct = (Mat_SeqSELLCUDA *)A->spptr;
    714:   PetscScalar       *z;
    715:   const PetscScalar *y, *x;
    716:   PetscInt           nrows = A->rmap->n, sliceheight = a->sliceheight;
    717:   MatScalar         *aval    = cudastruct->val;
    718:   PetscInt          *acolidx = cudastruct->colidx;
    719:   PetscInt          *sliidx  = cudastruct->sliidx;
    720: #if !defined(PETSC_USE_COMPLEX)
    721:   PetscReal maxoveravg;
    722:   PetscInt  chunksperblock, nchunks, *chunk_slice_map;
    723:   PetscInt  blocky = cudastruct->blocky;
    724: #endif

    726:   PetscFunctionBegin;
    727:   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);
    728:   PetscCheck(!(cudastruct->kernelchoice >= 2 && cudastruct->kernelchoice <= 6 && sliceheight != SLICE_HEIGHT), PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Kernel choices {2-6} requires the slice height of the matrix be 16, but the current slice height is %" PetscInt_FMT, sliceheight);
    729:   PetscCall(MatSeqSELLCUDACopyToGPU(A));
    730:   if (a->nz) {
    731:     PetscInt nblocks, blocksize = 512;
    732:     dim3     block2(256, 2), block4(128, 4), block8(64, 8), block16(32, 16), block32(16, 32);
    733:     PetscCall(VecCUDAGetArrayRead(xx, &x));
    734:     PetscCall(VecCUDAGetArrayRead(yy, &y));
    735:     PetscCall(VecCUDAGetArrayWrite(zz, &z));
    736:     PetscCall(PetscLogGpuTimeBegin());

    738:     switch (cudastruct->kernelchoice) {
    739: #if !defined(PETSC_USE_COMPLEX)
    740:     case 9:
    741:       nblocks = 1 + (nrows - 1) / sliceheight;
    742:       if (blocky == 2) {
    743:         matmultadd_seqsell_tiled_kernel9<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    744:       } else if (blocky == 4) {
    745:         matmultadd_seqsell_tiled_kernel9<4><<<nblocks, dim3(32, 4)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    746:       } else if (blocky == 8) {
    747:         matmultadd_seqsell_tiled_kernel9<8><<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    748:       } else if (blocky == 16) {
    749:         matmultadd_seqsell_tiled_kernel9<16><<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    750:       } else if (blocky == 32) {
    751:         matmultadd_seqsell_tiled_kernel9<32><<<nblocks, dim3(32, 32)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    752:       } else {
    753:         matmultadd_seqsell_tiled_kernel9<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    754:       }
    755:       break;
    756:     case 8:
    757:       /* each block handles approximately one slice */
    758:       nchunks         = cudastruct->totalchunks;
    759:       blocky          = a->chunksize / 32;
    760:       chunksperblock  = cudastruct->chunksperblock ? cudastruct->chunksperblock : 1 + (cudastruct->totalentries / cudastruct->totalslices - 1) / a->chunksize;
    761:       nblocks         = 1 + (nchunks - 1) / chunksperblock;
    762:       chunk_slice_map = cudastruct->chunk_slice_map;
    763:       if (blocky == 2) {
    764:         matmultadd_seqsell_tiled_kernel8<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    765:       } else if (blocky == 4) {
    766:         matmultadd_seqsell_tiled_kernel8<4><<<nblocks, dim3(32, 4)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    767:       } else if (blocky == 8) {
    768:         matmultadd_seqsell_tiled_kernel8<8><<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    769:       } else if (blocky == 16) {
    770:         matmultadd_seqsell_tiled_kernel8<16><<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    771:       } else if (blocky == 32) {
    772:         matmultadd_seqsell_tiled_kernel8<32><<<nblocks, dim3(32, 32)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    773:       } else {
    774:         matmultadd_seqsell_tiled_kernel8<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    775:       }
    776:       break;
    777:     case 7:
    778:       nblocks = 1 + (nrows - 1) / (2 * sliceheight);
    779:       if (blocky == 2) {
    780:         matmultadd_seqsell_tiled_kernel7<<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    781:       } else if (blocky == 4) {
    782:         matmultadd_seqsell_tiled_kernel7<<<nblocks, dim3(32, 4)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    783:       } else if (blocky == 8) {
    784:         matmultadd_seqsell_tiled_kernel7<<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    785:       } else if (blocky == 16) {
    786:         matmultadd_seqsell_tiled_kernel7<<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    787:       } else if (blocky == 32) {
    788:         matmultadd_seqsell_tiled_kernel7<<<nblocks, dim3(32, 32)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    789:       } else {
    790:         matmultadd_seqsell_tiled_kernel7<<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    791:       }
    792:       break;
    793: #endif
    794:     case 6:
    795:       nblocks = 1 + (nrows - 1) / (blocksize / 32);
    796:       matmultadd_seqsell_tiled_kernel6<<<nblocks, block32>>>(nrows, acolidx, aval, sliidx, x, y, z);
    797:       break;
    798:     case 5:
    799:       nblocks = 1 + (nrows - 1) / (blocksize / 16);
    800:       matmultadd_seqsell_tiled_kernel5<<<nblocks, block16>>>(nrows, acolidx, aval, sliidx, x, y, z);
    801:       break;
    802:     case 4:
    803:       nblocks = 1 + (nrows - 1) / (blocksize / 8);
    804:       matmultadd_seqsell_tiled_kernel4<<<nblocks, block8>>>(nrows, acolidx, aval, sliidx, x, y, z);
    805:       break;
    806:     case 3:
    807:       nblocks = 1 + (nrows - 1) / (blocksize / 4);
    808:       matmultadd_seqsell_tiled_kernel3<<<nblocks, block4>>>(nrows, acolidx, aval, sliidx, x, y, z);
    809:       break;
    810:     case 2:
    811:       nblocks = 1 + (nrows - 1) / (blocksize / 2);
    812:       matmultadd_seqsell_tiled_kernel2<<<nblocks, block2>>>(nrows, acolidx, aval, sliidx, x, y, z);
    813:       break;
    814:     case 1:
    815:       nblocks = 1 + (nrows - 1) / blocksize;
    816:       matmultadd_seqsell_basic_kernel<<<nblocks, blocksize>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    817:       break;
    818: #if !defined(PETSC_USE_COMPLEX)
    819:     case 0:
    820:       maxoveravg = a->maxslicewidth / a->avgslicewidth;
    821:       if (maxoveravg > 12.0 && maxoveravg / nrows > 0.001) { /* important threshold */
    822:         /* each block handles approximately one slice */
    823:         nchunks         = cudastruct->totalchunks;
    824:         blocky          = a->chunksize / 32;
    825:         chunksperblock  = cudastruct->chunksperblock ? cudastruct->chunksperblock : 1 + (cudastruct->totalentries / cudastruct->totalslices - 1) / a->chunksize;
    826:         nblocks         = 1 + (nchunks - 1) / chunksperblock;
    827:         chunk_slice_map = cudastruct->chunk_slice_map;
    828:         if (blocky == 2) {
    829:           matmultadd_seqsell_tiled_kernel8<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    830:         } else if (blocky == 4) {
    831:           matmultadd_seqsell_tiled_kernel8<4><<<nblocks, dim3(32, 4)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    832:         } else if (blocky == 8) {
    833:           matmultadd_seqsell_tiled_kernel8<8><<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    834:         } else if (blocky == 16) {
    835:           matmultadd_seqsell_tiled_kernel8<16><<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    836:         } else if (blocky == 32) {
    837:           matmultadd_seqsell_tiled_kernel8<32><<<nblocks, dim3(32, 32)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    838:         } else {
    839:           matmultadd_seqsell_tiled_kernel8<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, chunksperblock, nchunks, chunk_slice_map, acolidx, aval, sliidx, x, y, z);
    840:         }
    841:       } else {
    842:         PetscInt avgslicesize = sliceheight * a->avgslicewidth;
    843:         if (avgslicesize <= 432) {
    844:           if (sliceheight * a->maxslicewidth < 2048 && nrows > 100000) {
    845:             nblocks = 1 + (nrows - 1) / (2 * sliceheight); /* two slices per block */
    846:             matmultadd_seqsell_tiled_kernel7<<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    847:           } else {
    848:             nblocks = 1 + (nrows - 1) / sliceheight;
    849:             matmultadd_seqsell_tiled_kernel9<2><<<nblocks, dim3(32, 2)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    850:           }
    851:         } else if (avgslicesize <= 2400) {
    852:           nblocks = 1 + (nrows - 1) / sliceheight;
    853:           matmultadd_seqsell_tiled_kernel9<8><<<nblocks, dim3(32, 8)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    854:         } else {
    855:           nblocks = 1 + (nrows - 1) / sliceheight;
    856:           matmultadd_seqsell_tiled_kernel9<16><<<nblocks, dim3(32, 16)>>>(nrows, sliceheight, acolidx, aval, sliidx, x, y, z);
    857:         }
    858:       }
    859:       break;
    860: #endif
    861:     default:
    862:       SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unsupported kernel choice %" PetscInt_FMT " for MatMult_SeqSELLCUDA.", cudastruct->kernelchoice);
    863:     }
    864:     PetscCall(PetscLogGpuTimeEnd());
    865:     PetscCall(VecCUDARestoreArrayRead(xx, &x));
    866:     PetscCall(VecCUDARestoreArrayRead(yy, &y));
    867:     PetscCall(VecCUDARestoreArrayWrite(zz, &z));
    868:     PetscCall(PetscLogGpuFlops(2.0 * a->nz));
    869:   } else {
    870:     PetscCall(VecCopy(yy, zz));
    871:   }
    872:   PetscFunctionReturn(PETSC_SUCCESS);
    873: }

    875: static PetscErrorCode MatSetFromOptions_SeqSELLCUDA(Mat A, PetscOptionItems PetscOptionsObject)
    876: {
    877:   Mat_SeqSELLCUDA *cudastruct = (Mat_SeqSELLCUDA *)A->spptr;
    878:   PetscInt         kernel, blocky;
    879:   PetscBool        flg;

    881:   PetscFunctionBegin;
    882:   PetscOptionsHeadBegin(PetscOptionsObject, "SeqSELLCUDA options");
    883:   PetscCall(PetscOptionsGetInt(NULL, NULL, "-mat_sell_spmv_cuda_blocky", &blocky, &flg));
    884:   if (flg) {
    885:     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}", blocky);
    886:     cudastruct->blocky = blocky;
    887:   }
    888:   PetscCall(PetscOptionsGetInt(NULL, NULL, "-mat_sell_spmv_cuda_kernel", &kernel, &flg));
    889:   if (flg) {
    890:     PetscCheck(kernel >= 0 && kernel <= 9, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Wrong kernel choice: %" PetscInt_FMT " it should be in [0,9]", kernel);
    891:     cudastruct->kernelchoice = kernel;
    892:     if (kernel == 8) { PetscCall(PetscOptionsGetInt(NULL, NULL, "-mat_sell_spmv_cuda_chunksperblock", &cudastruct->chunksperblock, &flg)); }
    893:   }
    894:   PetscOptionsHeadEnd();
    895:   PetscFunctionReturn(PETSC_SUCCESS);
    896: }

    898: PETSC_INTERN PetscErrorCode MatAssemblyEnd_SpMV_Preprocessing_Private(Mat A)
    899: {
    900:   Mat_SeqSELL *a = (Mat_SeqSELL *)A->data;

    902:   PetscFunctionBegin;
    903:   PetscCall(MatSeqSELLGetAvgSliceWidth(A, &a->avgslicewidth));
    904:   PetscCall(MatSeqSELLGetMaxSliceWidth(A, &a->maxslicewidth));
    905:   PetscCall(MatSeqSELLGetFillRatio(A, &a->fillratio));
    906:   PetscFunctionReturn(PETSC_SUCCESS);
    907: }

    909: static PetscErrorCode MatAssemblyEnd_SeqSELLCUDA(Mat A, MatAssemblyType mode)
    910: {
    911:   PetscFunctionBegin;
    912:   PetscCall(MatAssemblyEnd_SeqSELL(A, mode));
    913:   PetscCall(MatAssemblyEnd_SpMV_Preprocessing_Private(A));
    914:   if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
    915:   if (A->factortype == MAT_FACTOR_NONE) { PetscCall(MatSeqSELLCUDACopyToGPU(A)); }
    916:   A->ops->mult    = MatMult_SeqSELLCUDA;
    917:   A->ops->multadd = MatMultAdd_SeqSELLCUDA;
    918:   PetscFunctionReturn(PETSC_SUCCESS);
    919: }

    921: static PetscErrorCode MatZeroEntries_SeqSELLCUDA(Mat A)
    922: {
    923:   PetscBool    both = PETSC_FALSE;
    924:   Mat_SeqSELL *a    = (Mat_SeqSELL *)A->data;

    926:   PetscFunctionBegin;
    927:   if (A->factortype == MAT_FACTOR_NONE) {
    928:     Mat_SeqSELLCUDA *cudastruct = (Mat_SeqSELLCUDA *)A->spptr;
    929:     if (cudastruct->val) {
    930:       both = PETSC_TRUE;
    931:       PetscCallCUDA(cudaMemset(cudastruct->val, 0, a->sliidx[a->totalslices] * sizeof(*cudastruct->val)));
    932:     }
    933:   }
    934:   PetscCall(PetscArrayzero(a->val, a->sliidx[a->totalslices]));
    935:   PetscCall(MatSeqSELLInvalidateDiagonal(A));
    936:   if (both) A->offloadmask = PETSC_OFFLOAD_BOTH;
    937:   else A->offloadmask = PETSC_OFFLOAD_CPU;
    938:   PetscFunctionReturn(PETSC_SUCCESS);
    939: }

    941: static PetscErrorCode MatDestroy_SeqSELLCUDA(Mat A)
    942: {
    943:   PetscFunctionBegin;
    944:   if (A->factortype == MAT_FACTOR_NONE && A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) PetscCall(MatSeqSELLCUDA_Destroy((Mat_SeqSELLCUDA **)&A->spptr));
    945:   PetscCall(MatDestroy_SeqSELL(A));
    946:   PetscFunctionReturn(PETSC_SUCCESS);
    947: }

    949: PETSC_INTERN PetscErrorCode MatConvert_SeqSELL_SeqSELLCUDA(Mat);
    950: static PetscErrorCode       MatDuplicate_SeqSELLCUDA(Mat A, MatDuplicateOption cpvalues, Mat *B)
    951: {
    952:   PetscFunctionBegin;
    953:   PetscCall(MatDuplicate_SeqSELL(A, cpvalues, B));
    954:   PetscCall(MatConvert_SeqSELL_SeqSELLCUDA(*B));
    955:   PetscFunctionReturn(PETSC_SUCCESS);
    956: }

    958: PETSC_INTERN PetscErrorCode MatConvert_SeqSELL_SeqSELLCUDA(Mat B)
    959: {
    960:   Mat_SeqSELLCUDA *cudastruct;

    962:   PetscFunctionBegin;
    963:   PetscCall(PetscFree(B->defaultvectype));
    964:   PetscCall(PetscStrallocpy(VECCUDA, &B->defaultvectype));

    966:   if (!B->spptr) {
    967:     if (B->factortype == MAT_FACTOR_NONE) {
    968:       PetscCall(PetscNew(&cudastruct));
    969:       B->spptr = cudastruct;
    970:     }
    971:   }

    973:   B->ops->assemblyend    = MatAssemblyEnd_SeqSELLCUDA;
    974:   B->ops->destroy        = MatDestroy_SeqSELLCUDA;
    975:   B->ops->setfromoptions = MatSetFromOptions_SeqSELLCUDA;
    976:   B->ops->mult           = MatMult_SeqSELLCUDA;
    977:   B->ops->multadd        = MatMultAdd_SeqSELLCUDA;
    978:   B->ops->duplicate      = MatDuplicate_SeqSELLCUDA;
    979:   B->ops->zeroentries    = MatZeroEntries_SeqSELLCUDA;

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

    984:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQSELLCUDA));
    985:   B->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
    986:   PetscFunctionReturn(PETSC_SUCCESS);
    987: }

    989: /*MC
    990:   MATSEQSELLCUDA - MATSELLCUDA = "(seq)sellcuda" - A matrix type to be used for sparse matrices on NVIDIA GPUs.

    992:   Options Database Keys:
    993: +  -mat_type seqsellcuda - sets the matrix type to "seqsellcuda" during a call to `MatSetFromOptions()`
    994: .  -mat_sell_spmv_cuda_kernel - selects a spmv kernel for MatSELLCUDA
    995: -  -mat_sell_spmv_cuda_blocky - sets the y dimension of the block size of the spmv kernels. These kernels use a 2D block with the x dimension being 32

    997:   Level: beginner

    999: .seealso: [](ch_matrices), `Mat`, `MATSELLCUDA`
    1000: M*/

    1002: PETSC_EXTERN PetscErrorCode MatCreate_SeqSELLCUDA(Mat B)
    1003: {
    1004:   PetscFunctionBegin;
    1005:   PetscCall(MatCreate_SeqSELL(B));
    1006:   PetscCall(MatConvert_SeqSELL_SeqSELLCUDA(B));
    1007:   PetscCall(MatSetFromOptions(B));
    1008:   PetscFunctionReturn(PETSC_SUCCESS);
    1009: }