Date: 2021-02-01T14:41:47+00:00

Actual source code: bvorthogcuda.cu
slepc-3.14.2 2021-02-01
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      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-2020, Universitat Politecnica de Valencia, Spain

      6:    This file is part of SLEPc.
      7:    SLEPc is distributed under a 2-clause BSD license (see LICENSE).
      8:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      9: */
     10: /*
     11:    BV orthogonalization routines (CUDA)
     12: */

     14: #include <slepc/private/bvimpl.h>
     15: #include <slepcblaslapack.h>
     16: #include <petsccublas.h>

     18: /*
     19:    BV_CleanCoefficients_CUDA - Sets to zero all entries of column j of the bv buffer
     20: */
     21: PetscErrorCode BV_CleanCoefficients_CUDA(BV bv,PetscInt j,PetscScalar *h)
     22: {
     24:   PetscScalar    *d_hh,*d_a;
     25:   PetscInt       i;
     26:   cudaError_t    cerr;

     29:   if (!h) {
     30:     VecCUDAGetArray(bv->buffer,&d_a);
     31:     d_hh = d_a + j*(bv->nc+bv->m);
     32:     cerr = cudaMemset(d_hh,0,(bv->nc+j)*sizeof(PetscScalar));CHKERRCUDA(cerr);
     33:     cerr = WaitForCUDA();CHKERRCUDA(cerr);
     34:     VecCUDARestoreArray(bv->buffer,&d_a);
     35:   } else { /* cpu memory */
     36:     for (i=0;i<bv->nc+j;i++) h[i] = 0.0;
     37:   }
     38:   return(0);
     39: }

     41: /*
     42:    BV_AddCoefficients_CUDA - Add the contents of the scratch (0-th column) of the bv buffer
     43:    into column j of the bv buffer
     44:  */
     45: PetscErrorCode BV_AddCoefficients_CUDA(BV bv,PetscInt j,PetscScalar *h,PetscScalar *c)
     46: {
     48:   PetscScalar    *d_h,*d_c,sone=1.0;
     49:   PetscInt       i;
     50:   PetscBLASInt   idx=0,one=1;
     51:   cublasStatus_t cberr;
     52:   cublasHandle_t cublasv2handle;

     55:   if (!h) {
     56:     PetscCUBLASGetHandle(&cublasv2handle);
     57:     VecCUDAGetArray(bv->buffer,&d_c);
     58:     d_h = d_c + j*(bv->nc+bv->m);
     59:     PetscBLASIntCast(bv->nc+j,&idx);
     60:     PetscLogGpuTimeBegin();
     61:     cberr = cublasXaxpy(cublasv2handle,idx,&sone,d_c,one,d_h,one);CHKERRCUBLAS(cberr);
     62:     PetscLogGpuTimeEnd();
     63:     PetscLogGpuFlops(1.0*bv->nc+j);
     64:     VecCUDARestoreArray(bv->buffer,&d_c);
     65:   } else { /* cpu memory */
     66:     for (i=0;i<bv->nc+j;i++) h[i] += c[i];
     67:     PetscLogFlops(1.0*bv->nc+j);
     68:   }
     69:   return(0);
     70: }

     72: /*
     73:    BV_SetValue_CUDA - Sets value in row j (counted after the constraints) of column k
     74:    of the coefficients array
     75: */
     76: PetscErrorCode BV_SetValue_CUDA(BV bv,PetscInt j,PetscInt k,PetscScalar *h,PetscScalar value)
     77: {
     79:   PetscScalar    *d_h,*a;
     80:   cudaError_t    cerr;

     83:   if (!h) {
     84:     VecCUDAGetArray(bv->buffer,&a);
     85:     d_h = a + k*(bv->nc+bv->m) + bv->nc+j;
     86:     cerr = cudaMemcpy(d_h,&value,sizeof(PetscScalar),cudaMemcpyHostToDevice);CHKERRCUDA(cerr);
     87:     PetscLogCpuToGpu(sizeof(PetscScalar));
     88:     cerr = WaitForCUDA();CHKERRCUDA(cerr);
     89:     VecCUDARestoreArray(bv->buffer,&a);
     90:   } else { /* cpu memory */
     91:     h[bv->nc+j] = value;
     92:   }
     93:   return(0);
     94: }

     96: /*
     97:    BV_SquareSum_CUDA - Returns the value h'*h, where h represents the contents of the
     98:    coefficients array (up to position j)
     99: */
    100: PetscErrorCode BV_SquareSum_CUDA(BV bv,PetscInt j,PetscScalar *h,PetscReal *sum)
    101: {
    102:   PetscErrorCode    ierr;
    103:   const PetscScalar *d_h;
    104:   PetscScalar       dot;
    105:   PetscInt          i;
    106:   PetscBLASInt      idx=0,one=1;
    107:   cublasStatus_t    cberr;
    108:   cublasHandle_t    cublasv2handle;

    111:   if (!h) {
    112:     PetscCUBLASGetHandle(&cublasv2handle);
    113:     VecCUDAGetArrayRead(bv->buffer,&d_h);
    114:     PetscBLASIntCast(bv->nc+j,&idx);
    115:     PetscLogGpuTimeBegin();
    116:     cberr = cublasXdotc(cublasv2handle,idx,d_h,one,d_h,one,&dot);CHKERRCUBLAS(cberr);
    117:     PetscLogGpuTimeEnd();
    118:     PetscLogGpuFlops(2.0*bv->nc+j);
    119:     *sum = PetscRealPart(dot);
    120:     VecCUDARestoreArrayRead(bv->buffer,&d_h);
    121:   } else { /* cpu memory */
    122:     *sum = 0.0;
    123:     for (i=0;i<bv->nc+j;i++) *sum += PetscRealPart(h[i]*PetscConj(h[i]));
    124:     PetscLogFlops(2.0*bv->nc+j);
    125:   }
    126:   return(0);
    127: }

    129: #define X_AXIS        0
    130: #define BLOCK_SIZE_X 64
    131: #define TILE_SIZE_X  16 /* work to be done by any thread on axis x */

    133: /*
    134:    Set the kernels grid dimensions
    135:    xcount: number of kernel calls needed for the requested size
    136:  */
    137: PetscErrorCode SetGrid1D(PetscInt n, dim3 *dimGrid, dim3 *dimBlock,PetscInt *xcount)
    138: {
    139:   PetscInt              one=1;
    140:   PetscBLASInt          card;
    141:   struct cudaDeviceProp devprop;
    142:   cudaError_t           cerr;

    145:   *xcount = 1;
    146:   if (n>BLOCK_SIZE_X) {
    147:     dimBlock->x = BLOCK_SIZE_X;
    148:     dimGrid->x = (n+BLOCK_SIZE_X*TILE_SIZE_X-one)/BLOCK_SIZE_X*TILE_SIZE_X;
    149:   } else {
    150:     dimBlock->x = (n+TILE_SIZE_X-one)/TILE_SIZE_X;
    151:     dimGrid->x = one;
    152:   }
    153:   cerr = cudaGetDevice(&card);CHKERRCUDA(cerr);
    154:   cerr = cudaGetDeviceProperties(&devprop,card);CHKERRCUDA(cerr);
    155:   if (dimGrid->x>(unsigned)devprop.maxGridSize[X_AXIS]) {
    156:     *xcount = (dimGrid->x+devprop.maxGridSize[X_AXIS]-one)/devprop.maxGridSize[X_AXIS];
    157:     dimGrid->x = devprop.maxGridSize[X_AXIS];
    158:   }
    159:   return(0);
    160: }

    162: /* pointwise multiplication */
    163: __global__ void PointwiseMult_kernel(PetscInt xcount,PetscScalar *a,const PetscScalar *b,PetscInt n)
    164: {
    165:   PetscInt i,x;

    167:   x = xcount*gridDim.x*blockDim.x+blockIdx.x*blockDim.x*TILE_SIZE_X+threadIdx.x*TILE_SIZE_X;
    168:   for (i=x;i<x+TILE_SIZE_X&&i<n;i++) {
    169:     a[i] *= PetscRealPart(b[i]);
    170:   }
    171: }

    173: /* pointwise division */
    174: __global__ void PointwiseDiv_kernel(PetscInt xcount,PetscScalar *a,const PetscScalar *b,PetscInt n)
    175: {
    176:   PetscInt i,x;

    178:   x = xcount*gridDim.x*blockDim.x+blockIdx.x*blockDim.x*TILE_SIZE_X+threadIdx.x*TILE_SIZE_X;
    179:   for (i=x;i<x+TILE_SIZE_X&&i<n;i++) {
    180:     a[i] /= PetscRealPart(b[i]);
    181:   }
    182: }

    184: /*
    185:    BV_ApplySignature_CUDA - Computes the pointwise product h*omega, where h represents
    186:    the contents of the coefficients array (up to position j) and omega is the signature;
    187:    if inverse=TRUE then the operation is h/omega
    188: */
    189: PetscErrorCode BV_ApplySignature_CUDA(BV bv,PetscInt j,PetscScalar *h,PetscBool inverse)
    190: {
    191:   PetscErrorCode    ierr;
    192:   PetscScalar       *d_h;
    193:   const PetscScalar *d_omega,*omega;
    194:   PetscInt          i,xcount;
    195:   dim3              blocks3d, threads3d;
    196:   cudaError_t       cerr;

    199:   if (!(bv->nc+j)) return(0);
    200:   if (!h) {
    201:     VecCUDAGetArray(bv->buffer,&d_h);
    202:     VecCUDAGetArrayRead(bv->omega,&d_omega);
    203:     SetGrid1D(bv->nc+j,&blocks3d,&threads3d,&xcount);
    204:     PetscLogGpuTimeBegin();
    205:     if (inverse) {
    206:       for (i=0;i<xcount;i++) {
    207:         PointwiseDiv_kernel<<<blocks3d,threads3d>>>(i,d_h,d_omega,bv->nc+j);
    208:       }
    209:     } else {
    210:       for (i=0;i<xcount;i++) {
    211:         PointwiseMult_kernel<<<blocks3d,threads3d>>>(i,d_h,d_omega,bv->nc+j);
    212:       }
    213:     }
    214:     cerr = cudaGetLastError();CHKERRCUDA(cerr);
    215:     PetscLogGpuTimeEnd();
    216:     PetscLogGpuFlops(1.0*bv->nc+j);
    217:     cerr = WaitForCUDA();CHKERRCUDA(cerr);
    218:     VecCUDARestoreArrayRead(bv->omega,&d_omega);
    219:     VecCUDARestoreArray(bv->buffer,&d_h);
    220:   } else {
    221:     VecGetArrayRead(bv->omega,&omega);
    222:     if (inverse) for (i=0;i<bv->nc+j;i++) h[i] /= PetscRealPart(omega[i]);
    223:     else for (i=0;i<bv->nc+j;i++) h[i] *= PetscRealPart(omega[i]);
    224:     VecRestoreArrayRead(bv->omega,&omega);
    225:     PetscLogFlops(1.0*bv->nc+j);
    226:   }
    227:   return(0);
    228: }

    230: /*
    231:    BV_SquareRoot_CUDA - Returns the square root of position j (counted after the constraints)
    232:    of the coefficients array
    233: */
    234: PetscErrorCode BV_SquareRoot_CUDA(BV bv,PetscInt j,PetscScalar *h,PetscReal *beta)
    235: {
    236:   PetscErrorCode    ierr;
    237:   const PetscScalar *d_h;
    238:   PetscScalar       hh;
    239:   cudaError_t       cerr;

    242:   if (!h) {
    243:     VecCUDAGetArrayRead(bv->buffer,&d_h);
    244:     cerr = cudaMemcpy(&hh,d_h+bv->nc+j,sizeof(PetscScalar),cudaMemcpyDeviceToHost);CHKERRCUDA(cerr);
    245:     PetscLogGpuToCpu(sizeof(PetscScalar));
    246:     cerr = WaitForCUDA();CHKERRCUDA(cerr);
    247:     BV_SafeSqrt(bv,hh,beta);
    248:     VecCUDARestoreArrayRead(bv->buffer,&d_h);
    249:   } else {
    250:     BV_SafeSqrt(bv,h[bv->nc+j],beta);
    251:   }
    252:   return(0);
    253: }

    255: /*
    256:    BV_StoreCoefficients_CUDA - Copy the contents of the coefficients array to an array dest
    257:    provided by the caller (only values from l to j are copied)
    258: */
    259: PetscErrorCode BV_StoreCoefficients_CUDA(BV bv,PetscInt j,PetscScalar *h,PetscScalar *dest)
    260: {
    261:   PetscErrorCode    ierr;
    262:   const PetscScalar *d_h,*d_a;
    263:   PetscInt          i;
    264:   cudaError_t       cerr;

    267:   if (!h) {
    268:     VecCUDAGetArrayRead(bv->buffer,&d_a);
    269:     d_h = d_a + j*(bv->nc+bv->m)+bv->nc;
    270:     cerr = cudaMemcpy(dest-bv->l,d_h,(j-bv->l)*sizeof(PetscScalar),cudaMemcpyDeviceToHost);CHKERRCUDA(cerr);
    271:     PetscLogGpuToCpu((j-bv->l)*sizeof(PetscScalar));
    272:     cerr = WaitForCUDA();CHKERRCUDA(cerr);
    273:     VecCUDARestoreArrayRead(bv->buffer,&d_a);
    274:   } else {
    275:     for (i=bv->l;i<j;i++) dest[i-bv->l] = h[bv->nc+i];
    276:   }
    277:   return(0);
    278: }