Date: 2022-08-02T03:13:33+00:00

Actual source code: mpiviennacl.cxx

      2: /*
      3:    This file contains routines for Parallel vector operations.
      4:  */
      5: #include <petscconf.h>
      6: #include <../src/vec/vec/impls/mpi/pvecimpl.h>
      7: #include <../src/vec/vec/impls/seq/seqviennacl/viennaclvecimpl.h>

      9: /*MC
     10:    VECVIENNACL - VECVIENNACL = "viennacl" - A VECSEQVIENNACL on a single-process communicator, and VECMPIVIENNACL otherwise.

     12:    Options Database Keys:
     13: . -vec_type viennacl - sets the vector type to VECVIENNACL during a call to VecSetFromOptions()

     15:   Level: beginner

     17: .seealso: VecCreate(), VecSetType(), VecSetFromOptions(), VecCreateMPIWithArray(), VECSEQVIENNACL, VECMPIVIENNACL, VECSTANDARD, VecType, VecCreateMPI(), VecCreateMPI()
     18: M*/

     20: PetscErrorCode VecDestroy_MPIViennaCL(Vec v)
     21: {
     22:   try {
     23:     if (v->spptr) {
     24:       delete ((Vec_ViennaCL*)v->spptr)->GPUarray_allocated;
     25:       delete (Vec_ViennaCL*) v->spptr;
     26:     }
     27:   } catch(std::exception const & ex) {
     28:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"ViennaCL error: %s", ex.what());
     29:   }
     30:   VecDestroy_MPI(v);
     31:   return 0;
     32: }

     34: PetscErrorCode VecNorm_MPIViennaCL(Vec xin,NormType type,PetscReal *z)
     35: {
     36:   PetscReal sum,work = 0.0;

     38:   if (type == NORM_2 || type == NORM_FROBENIUS) {
     39:     VecNorm_SeqViennaCL(xin,NORM_2,&work);
     40:     work *= work;
     41:     MPIU_Allreduce(&work,&sum,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)xin));
     42:     *z    = PetscSqrtReal(sum);
     43:   } else if (type == NORM_1) {
     44:     /* Find the local part */
     45:     VecNorm_SeqViennaCL(xin,NORM_1,&work);
     46:     /* Find the global max */
     47:     MPIU_Allreduce(&work,z,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)xin));
     48:   } else if (type == NORM_INFINITY) {
     49:     /* Find the local max */
     50:     VecNorm_SeqViennaCL(xin,NORM_INFINITY,&work);
     51:     /* Find the global max */
     52:     MPIU_Allreduce(&work,z,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)xin));
     53:   } else if (type == NORM_1_AND_2) {
     54:     PetscReal temp[2];
     55:     VecNorm_SeqViennaCL(xin,NORM_1,temp);
     56:     VecNorm_SeqViennaCL(xin,NORM_2,temp+1);
     57:     temp[1] = temp[1]*temp[1];
     58:     MPIU_Allreduce(temp,z,2,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)xin));
     59:     z[1] = PetscSqrtReal(z[1]);
     60:   }
     61:   return 0;
     62: }

     64: PetscErrorCode VecDot_MPIViennaCL(Vec xin,Vec yin,PetscScalar *z)
     65: {
     66:   PetscScalar    sum,work;

     68:   VecDot_SeqViennaCL(xin,yin,&work);
     69:   MPIU_Allreduce(&work,&sum,1,MPIU_SCALAR,MPIU_SUM,PetscObjectComm((PetscObject)xin));
     70:   *z   = sum;
     71:   return 0;
     72: }

     74: PetscErrorCode VecTDot_MPIViennaCL(Vec xin,Vec yin,PetscScalar *z)
     75: {
     76:   PetscScalar    sum,work;

     78:   VecTDot_SeqViennaCL(xin,yin,&work);
     79:   MPIU_Allreduce(&work,&sum,1,MPIU_SCALAR,MPIU_SUM,PetscObjectComm((PetscObject)xin));
     80:   *z   = sum;
     81:   return 0;
     82: }

     84: PetscErrorCode VecMDot_MPIViennaCL(Vec xin,PetscInt nv,const Vec y[],PetscScalar *z)
     85: {
     86:   PetscScalar    awork[128],*work = awork;

     88:   if (nv > 128) {
     89:     PetscMalloc1(nv,&work);
     90:   }
     91:   VecMDot_SeqViennaCL(xin,nv,y,work);
     92:   MPIU_Allreduce(work,z,nv,MPIU_SCALAR,MPIU_SUM,PetscObjectComm((PetscObject)xin));
     93:   if (nv > 128) {
     94:     PetscFree(work);
     95:   }
     96:   return 0;
     97: }

     99: /*MC
    100:    VECMPIVIENNACL - VECMPIVIENNACL = "mpiviennacl" - The basic parallel vector, modified to use ViennaCL

    102:    Options Database Keys:
    103: . -vec_type mpiviennacl - sets the vector type to VECMPIVIENNACL during a call to VecSetFromOptions()

    105:   Level: beginner

    107: .seealso: VecCreate(), VecSetType(), VecSetFromOptions(), VecCreateMPIWithArray(), VECMPI, VecType, VecCreateMPI(), VecCreateMPI()
    108: M*/

    110: PetscErrorCode VecDuplicate_MPIViennaCL(Vec win,Vec *v)
    111: {
    112:   Vec_MPI        *vw,*w = (Vec_MPI*)win->data;
    113:   PetscScalar    *array;

    115:   VecCreate(PetscObjectComm((PetscObject)win),v);
    116:   PetscLayoutReference(win->map,&(*v)->map);

    118:   VecCreate_MPI_Private(*v,PETSC_FALSE,w->nghost,0);
    119:   vw   = (Vec_MPI*)(*v)->data;
    120:   PetscMemcpy((*v)->ops,win->ops,sizeof(struct _VecOps));

    122:   /* save local representation of the parallel vector (and scatter) if it exists */
    123:   if (w->localrep) {
    124:     VecGetArray(*v,&array);
    125:     VecCreateSeqWithArray(PETSC_COMM_SELF,1,win->map->n+w->nghost,array,&vw->localrep);
    126:     PetscMemcpy(vw->localrep->ops,w->localrep->ops,sizeof(struct _VecOps));
    127:     VecRestoreArray(*v,&array);
    128:     PetscLogObjectParent((PetscObject)*v,(PetscObject)vw->localrep);
    129:     vw->localupdate = w->localupdate;
    130:     if (vw->localupdate) {
    131:       PetscObjectReference((PetscObject)vw->localupdate);
    132:     }
    133:   }

    135:   /* New vector should inherit stashing property of parent */
    136:   (*v)->stash.donotstash   = win->stash.donotstash;
    137:   (*v)->stash.ignorenegidx = win->stash.ignorenegidx;

    139:   /* change type_name appropriately */
    140:   PetscObjectChangeTypeName((PetscObject)(*v),VECMPIVIENNACL);

    142:   PetscObjectListDuplicate(((PetscObject)win)->olist,&((PetscObject)(*v))->olist);
    143:   PetscFunctionListDuplicate(((PetscObject)win)->qlist,&((PetscObject)(*v))->qlist);
    144:   (*v)->map->bs   = PetscAbs(win->map->bs);
    145:   (*v)->bstash.bs = win->bstash.bs;
    146:   return 0;
    147: }

    149: PetscErrorCode VecDotNorm2_MPIViennaCL(Vec s,Vec t,PetscScalar *dp,PetscScalar *nm)
    150: {
    151:   PetscScalar    work[2],sum[2];

    153:   VecDotNorm2_SeqViennaCL(s,t,work,work+1);
    154:   MPIU_Allreduce((void*)&work,(void*)&sum,2,MPIU_SCALAR,MPIU_SUM,PetscObjectComm((PetscObject)s));
    155:   *dp  = sum[0];
    156:   *nm  = sum[1];
    157:   return 0;
    158: }

    160: PetscErrorCode VecBindToCPU_MPIViennaCL(Vec vv, PetscBool bind)
    161: {
    162:   vv->boundtocpu = bind;

    164:   if (bind) {
    165:     VecViennaCLCopyFromGPU(vv);
    166:     vv->offloadmask = PETSC_OFFLOAD_CPU; /* since the CPU code will likely change values in the vector */
    167:     vv->ops->dotnorm2               = NULL;
    168:     vv->ops->waxpy                  = VecWAXPY_Seq;
    169:     vv->ops->dot                    = VecDot_MPI;
    170:     vv->ops->mdot                   = VecMDot_MPI;
    171:     vv->ops->tdot                   = VecTDot_MPI;
    172:     vv->ops->norm                   = VecNorm_MPI;
    173:     vv->ops->scale                  = VecScale_Seq;
    174:     vv->ops->copy                   = VecCopy_Seq;
    175:     vv->ops->set                    = VecSet_Seq;
    176:     vv->ops->swap                   = VecSwap_Seq;
    177:     vv->ops->axpy                   = VecAXPY_Seq;
    178:     vv->ops->axpby                  = VecAXPBY_Seq;
    179:     vv->ops->maxpy                  = VecMAXPY_Seq;
    180:     vv->ops->aypx                   = VecAYPX_Seq;
    181:     vv->ops->axpbypcz               = VecAXPBYPCZ_Seq;
    182:     vv->ops->pointwisemult          = VecPointwiseMult_Seq;
    183:     vv->ops->setrandom              = VecSetRandom_Seq;
    184:     vv->ops->placearray             = VecPlaceArray_Seq;
    185:     vv->ops->replacearray           = VecReplaceArray_Seq;
    186:     vv->ops->resetarray             = VecResetArray_Seq;
    187:     vv->ops->dot_local              = VecDot_Seq;
    188:     vv->ops->tdot_local             = VecTDot_Seq;
    189:     vv->ops->norm_local             = VecNorm_Seq;
    190:     vv->ops->mdot_local             = VecMDot_Seq;
    191:     vv->ops->pointwisedivide        = VecPointwiseDivide_Seq;
    192:     vv->ops->getlocalvector         = NULL;
    193:     vv->ops->restorelocalvector     = NULL;
    194:     vv->ops->getlocalvectorread     = NULL;
    195:     vv->ops->restorelocalvectorread = NULL;
    196:     vv->ops->getarraywrite          = NULL;
    197:   } else {
    198:     vv->ops->dotnorm2        = VecDotNorm2_MPIViennaCL;
    199:     vv->ops->waxpy           = VecWAXPY_SeqViennaCL;
    200:     vv->ops->duplicate       = VecDuplicate_MPIViennaCL;
    201:     vv->ops->dot             = VecDot_MPIViennaCL;
    202:     vv->ops->mdot            = VecMDot_MPIViennaCL;
    203:     vv->ops->tdot            = VecTDot_MPIViennaCL;
    204:     vv->ops->norm            = VecNorm_MPIViennaCL;
    205:     vv->ops->scale           = VecScale_SeqViennaCL;
    206:     vv->ops->copy            = VecCopy_SeqViennaCL;
    207:     vv->ops->set             = VecSet_SeqViennaCL;
    208:     vv->ops->swap            = VecSwap_SeqViennaCL;
    209:     vv->ops->axpy            = VecAXPY_SeqViennaCL;
    210:     vv->ops->axpby           = VecAXPBY_SeqViennaCL;
    211:     vv->ops->maxpy           = VecMAXPY_SeqViennaCL;
    212:     vv->ops->aypx            = VecAYPX_SeqViennaCL;
    213:     vv->ops->axpbypcz        = VecAXPBYPCZ_SeqViennaCL;
    214:     vv->ops->pointwisemult   = VecPointwiseMult_SeqViennaCL;
    215:     vv->ops->setrandom       = VecSetRandom_SeqViennaCL;
    216:     vv->ops->dot_local       = VecDot_SeqViennaCL;
    217:     vv->ops->tdot_local      = VecTDot_SeqViennaCL;
    218:     vv->ops->norm_local      = VecNorm_SeqViennaCL;
    219:     vv->ops->mdot_local      = VecMDot_SeqViennaCL;
    220:     vv->ops->destroy         = VecDestroy_MPIViennaCL;
    221:     vv->ops->pointwisedivide = VecPointwiseDivide_SeqViennaCL;
    222:     vv->ops->placearray      = VecPlaceArray_SeqViennaCL;
    223:     vv->ops->replacearray    = VecReplaceArray_SeqViennaCL;
    224:     vv->ops->resetarray      = VecResetArray_SeqViennaCL;
    225:     vv->ops->getarraywrite   = VecGetArrayWrite_SeqViennaCL;
    226:     vv->ops->getarray        = VecGetArray_SeqViennaCL;
    227:     vv->ops->restorearray    = VecRestoreArray_SeqViennaCL;
    228:   }
    229:   return 0;
    230: }

    232: PETSC_EXTERN PetscErrorCode VecCreate_MPIViennaCL(Vec vv)
    233: {
    234:   PetscLayoutSetUp(vv->map);
    235:   VecViennaCLAllocateCheck(vv);
    236:   VecCreate_MPIViennaCL_Private(vv,PETSC_FALSE,0,((Vec_ViennaCL*)(vv->spptr))->GPUarray);
    237:   VecViennaCLAllocateCheckHost(vv);
    238:   VecSet(vv,0.0);
    239:   VecSet_Seq(vv,0.0);
    240:   vv->offloadmask = PETSC_OFFLOAD_BOTH;
    241:   return 0;
    242: }

    244: PETSC_EXTERN PetscErrorCode VecCreate_ViennaCL(Vec v)
    245: {
    246:   PetscMPIInt    size;

    248:   MPI_Comm_size(PetscObjectComm((PetscObject)v),&size);
    249:   if (size == 1) {
    250:     VecSetType(v,VECSEQVIENNACL);
    251:   } else {
    252:     VecSetType(v,VECMPIVIENNACL);
    253:   }
    254:   return 0;
    255: }

    257: /*@C
    258:    VecCreateMPIViennaCLWithArray - Creates a parallel, array-style vector,
    259:    where the user provides the viennacl vector to store the vector values.

    261:    Collective

    263:    Input Parameters:
    264: +  comm  - the MPI communicator to use
    265: .  bs    - block size, same meaning as VecSetBlockSize()
    266: .  n     - local vector length, cannot be PETSC_DECIDE
    267: .  N     - global vector length (or PETSC_DECIDE to have calculated)
    268: -  array - the user provided GPU array to store the vector values

    270:    Output Parameter:
    271: .  vv - the vector

    273:    Notes:
    274:    Use VecDuplicate() or VecDuplicateVecs() to form additional vectors of the
    275:    same type as an existing vector.

    277:    If the user-provided array is NULL, then VecViennaCLPlaceArray() can be used
    278:    at a later stage to SET the array for storing the vector values.

    280:    PETSc does NOT free the array when the vector is destroyed via VecDestroy().
    281:    The user should not free the array until the vector is destroyed.

    283:    Level: intermediate

    285: .seealso: VecCreateSeqViennaCLWithArray(), VecCreateMPIWithArray(), VecCreateSeqWithArray(),
    286:           VecCreate(), VecCreateMPI(), VecCreateGhostWithArray(), VecViennaCLPlaceArray()

    288: @*/
    289: PetscErrorCode  VecCreateMPIViennaCLWithArray(MPI_Comm comm,PetscInt bs,PetscInt n,PetscInt N,const ViennaCLVector *array,Vec *vv)
    290: {
    292:   PetscSplitOwnership(comm,&n,&N);
    293:   VecCreate(comm,vv);
    294:   VecSetSizes(*vv,n,N);
    295:   VecSetBlockSize(*vv,bs);
    296:   VecCreate_MPIViennaCL_Private(*vv,PETSC_FALSE,0,array);
    297:   return 0;
    298: }

    300: /*@C
    301:    VecCreateMPIViennaCLWithArrays - Creates a parallel, array-style vector,
    302:    where the user provides the ViennaCL vector to store the vector values.

    304:    Collective

    306:    Input Parameters:
    307: +  comm  - the MPI communicator to use
    308: .  bs    - block size, same meaning as VecSetBlockSize()
    309: .  n     - local vector length, cannot be PETSC_DECIDE
    310: .  N     - global vector length (or PETSC_DECIDE to have calculated)
    311: -  cpuarray - the user provided CPU array to store the vector values
    312: -  viennaclvec - ViennaCL vector where the Vec entries are to be stored on the device.

    314:    Output Parameter:
    315: .  vv - the vector

    317:    Notes:
    318:    If both cpuarray and viennaclvec are provided, the caller must ensure that
    319:    the provided arrays have identical values.

    321:    Use VecDuplicate() or VecDuplicateVecs() to form additional vectors of the
    322:    same type as an existing vector.

    324:    PETSc does NOT free the provided arrays when the vector is destroyed via
    325:    VecDestroy(). The user should not free the array until the vector is
    326:    destroyed.

    328:    Level: intermediate

    330: .seealso: VecCreateSeqViennaCLWithArrays(), VecCreateMPIWithArray()
    331:           VecCreate(), VecDuplicate(), VecDuplicateVecs(), VecCreateGhost(),
    332:           VecCreateMPI(), VecCreateGhostWithArray(), VecViennaCLPlaceArray(),
    333:           VecPlaceArray(), VecCreateMPICUDAWithArrays(),
    334:           VecViennaCLAllocateCheckHost()
    335: @*/
    336: PetscErrorCode  VecCreateMPIViennaCLWithArrays(MPI_Comm comm,PetscInt bs,PetscInt n,PetscInt N,const PetscScalar cpuarray[],const ViennaCLVector *viennaclvec,Vec *vv)
    337: {
    338:   VecCreateMPIViennaCLWithArray(comm,bs,n,N,viennaclvec,vv);
    339:   if (cpuarray && viennaclvec) {
    340:     Vec_MPI *s         = (Vec_MPI*)((*vv)->data);
    341:     s->array           = (PetscScalar*)cpuarray;
    342:     (*vv)->offloadmask = PETSC_OFFLOAD_BOTH;
    343:   } else if (cpuarray) {
    344:     Vec_MPI *s         = (Vec_MPI*)((*vv)->data);
    345:     s->array           = (PetscScalar*)cpuarray;
    346:     (*vv)->offloadmask =  PETSC_OFFLOAD_CPU;
    347:   } else if (viennaclvec) {
    348:     (*vv)->offloadmask = PETSC_OFFLOAD_GPU;
    349:   } else {
    350:     (*vv)->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
    351:   }
    352:   return 0;
    353: }

    355: PetscErrorCode VecCreate_MPIViennaCL_Private(Vec vv,PetscBool alloc,PetscInt nghost,const ViennaCLVector *array)
    356: {
    357:   Vec_ViennaCL   *vecviennacl;

    359:   VecCreate_MPI_Private(vv,PETSC_FALSE,0,0);
    360:   PetscObjectChangeTypeName((PetscObject)vv,VECMPIVIENNACL);

    362:   VecBindToCPU_MPIViennaCL(vv,PETSC_FALSE);
    363:   vv->ops->bindtocpu = VecBindToCPU_MPIViennaCL;

    365:   if (alloc && !array) {
    366:     VecViennaCLAllocateCheck(vv);
    367:     VecViennaCLAllocateCheckHost(vv);
    368:     VecSet(vv,0.0);
    369:     VecSet_Seq(vv,0.0);
    370:     vv->offloadmask = PETSC_OFFLOAD_BOTH;
    371:   }
    372:   if (array) {
    373:     if (!vv->spptr)
    374:       vv->spptr = new Vec_ViennaCL;
    375:     vecviennacl = (Vec_ViennaCL*)vv->spptr;
    376:     vecviennacl->GPUarray_allocated = 0;
    377:     vecviennacl->GPUarray = (ViennaCLVector*)array;
    378:     vv->offloadmask = PETSC_OFFLOAD_UNALLOCATED;
    379:   }

    381:   return 0;
    382: }