Date: 2026-05-01T10:02:23+00:00

Actual source code: fnutil.c

      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-, 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:    Utility subroutines common to several impls
     12: */

     14: #include <slepc/private/fnimpl.h>
     15: #include <slepcblaslapack.h>

     17: /*
     18:    Compute the square root of an upper quasi-triangular matrix T,
     19:    using Higham's algorithm (LAA 88, 1987). T is overwritten with sqrtm(T).
     20:  */
     21: static PetscErrorCode SlepcMatDenseSqrt(PetscBLASInt n,PetscScalar *T,PetscBLASInt ld)
     22: {
     23:   PetscScalar  one=1.0,mone=-1.0;
     24:   PetscReal    scal;
     25:   PetscBLASInt i,j,si,sj,r,ione=1,info;
     26: #if !defined(PETSC_USE_COMPLEX)
     27:   PetscReal    alpha,theta,mu,mu2;
     28: #endif

     30:   PetscFunctionBegin;
     31:   for (j=0;j<n;j++) {
     32: #if defined(PETSC_USE_COMPLEX)
     33:     sj = 1;
     34:     T[j+j*ld] = PetscSqrtScalar(T[j+j*ld]);
     35: #else
     36:     sj = (j==n-1 || T[j+1+j*ld] == 0.0)? 1: 2;
     37:     if (sj==1) {
     38:       PetscCheck(T[j+j*ld]>=0.0,PETSC_COMM_SELF,PETSC_ERR_USER_INPUT,"Matrix has a real negative eigenvalue, no real primary square root exists");
     39:       T[j+j*ld] = PetscSqrtReal(T[j+j*ld]);
     40:     } else {
     41:       /* square root of 2x2 block */
     42:       theta = (T[j+j*ld]+T[j+1+(j+1)*ld])/2.0;
     43:       mu    = (T[j+j*ld]-T[j+1+(j+1)*ld])/2.0;
     44:       mu2   = -mu*mu-T[j+1+j*ld]*T[j+(j+1)*ld];
     45:       mu    = PetscSqrtReal(mu2);
     46:       if (theta>0.0) alpha = PetscSqrtReal((theta+PetscSqrtReal(theta*theta+mu2))/2.0);
     47:       else alpha = mu/PetscSqrtReal(2.0*(-theta+PetscSqrtReal(theta*theta+mu2)));
     48:       T[j+j*ld]       /= 2.0*alpha;
     49:       T[j+1+(j+1)*ld] /= 2.0*alpha;
     50:       T[j+(j+1)*ld]   /= 2.0*alpha;
     51:       T[j+1+j*ld]     /= 2.0*alpha;
     52:       T[j+j*ld]       += alpha-theta/(2.0*alpha);
     53:       T[j+1+(j+1)*ld] += alpha-theta/(2.0*alpha);
     54:     }
     55: #endif
     56:     for (i=j-1;i>=0;i--) {
     57: #if defined(PETSC_USE_COMPLEX)
     58:       si = 1;
     59: #else
     60:       si = (i==0 || T[i+(i-1)*ld] == 0.0)? 1: 2;
     61:       if (si==2) i--;
     62: #endif
     63:       /* solve Sylvester equation of order si x sj */
     64:       r = j-i-si;
     65:       if (r) PetscCallBLAS("BLASgemm",BLASgemm_("N","N",&si,&sj,&r,&mone,T+i+(i+si)*ld,&ld,T+i+si+j*ld,&ld,&one,T+i+j*ld,&ld));
     66:       PetscCallBLAS("LAPACKtrsyl",LAPACKtrsyl_("N","N",&ione,&si,&sj,T+i+i*ld,&ld,T+j+j*ld,&ld,T+i+j*ld,&ld,&scal,&info));
     67:       SlepcCheckLapackInfo("trsyl",info);
     68:       PetscCheck(scal==1.0,PETSC_COMM_SELF,PETSC_ERR_SUP,"Current implementation cannot handle scale factor %g",(double)scal);
     69:     }
     70:     if (sj==2) j++;
     71:   }
     72:   PetscFunctionReturn(PETSC_SUCCESS);
     73: }

     75: #define BLOCKSIZE 64

     77: /*
     78:    Schur method for the square root of an upper quasi-triangular matrix T.
     79:    T is overwritten with sqrtm(T).
     80:    If firstonly then only the first column of T will contain relevant values.
     81:  */
     82: PetscErrorCode FNSqrtmSchur(FN fn,PetscBLASInt n,PetscScalar *T,PetscBLASInt ld,PetscBool firstonly)
     83: {
     84:   PetscBLASInt   i,j,k,r,ione=1,sdim,lwork,*s,*p,info,bs=BLOCKSIZE;
     85:   PetscScalar    *wr,*W,*Q,*work,one=1.0,zero=0.0,mone=-1.0;
     86:   PetscInt       m,nblk;
     87:   PetscReal      scal;
     88: #if defined(PETSC_USE_COMPLEX)
     89:   PetscReal      *rwork;
     90: #else
     91:   PetscReal      *wi;
     92: #endif

     94:   PetscFunctionBegin;
     95:   m     = n;
     96:   nblk  = (m+bs-1)/bs;
     97:   lwork = 5*n;
     98:   k     = firstonly? 1: n;

    100:   /* compute Schur decomposition A*Q = Q*T */
    101: #if !defined(PETSC_USE_COMPLEX)
    102:   PetscCall(PetscMalloc7(m,&wr,m,&wi,m*k,&W,m*m,&Q,lwork,&work,nblk,&s,nblk,&p));
    103:   PetscCallBLAS("LAPACKgees",LAPACKgees_("V","N",NULL,&n,T,&ld,&sdim,wr,wi,Q,&ld,work,&lwork,NULL,&info));
    104: #else
    105:   PetscCall(PetscMalloc7(m,&wr,m,&rwork,m*k,&W,m*m,&Q,lwork,&work,nblk,&s,nblk,&p));
    106:   PetscCallBLAS("LAPACKgees",LAPACKgees_("V","N",NULL,&n,T,&ld,&sdim,wr,Q,&ld,work,&lwork,rwork,NULL,&info));
    107: #endif
    108:   SlepcCheckLapackInfo("gees",info);

    110:   /* determine block sizes and positions, to avoid cutting 2x2 blocks */
    111:   j = 0;
    112:   p[j] = 0;
    113:   do {
    114:     s[j] = PetscMin(bs,n-p[j]);
    115: #if !defined(PETSC_USE_COMPLEX)
    116:     if (p[j]+s[j]!=n && T[p[j]+s[j]+(p[j]+s[j]-1)*ld]!=0.0) s[j]++;
    117: #endif
    118:     if (p[j]+s[j]==n) break;
    119:     j++;
    120:     p[j] = p[j-1]+s[j-1];
    121:   } while (1);
    122:   nblk = j+1;

    124:   for (j=0;j<nblk;j++) {
    125:     /* evaluate f(T_jj) */
    126:     PetscCall(SlepcMatDenseSqrt(s[j],T+p[j]+p[j]*ld,ld));
    127:     for (i=j-1;i>=0;i--) {
    128:       /* solve Sylvester equation for block (i,j) */
    129:       r = p[j]-p[i]-s[i];
    130:       if (r) PetscCallBLAS("BLASgemm",BLASgemm_("N","N",s+i,s+j,&r,&mone,T+p[i]+(p[i]+s[i])*ld,&ld,T+p[i]+s[i]+p[j]*ld,&ld,&one,T+p[i]+p[j]*ld,&ld));
    131:       PetscCallBLAS("LAPACKtrsyl",LAPACKtrsyl_("N","N",&ione,s+i,s+j,T+p[i]+p[i]*ld,&ld,T+p[j]+p[j]*ld,&ld,T+p[i]+p[j]*ld,&ld,&scal,&info));
    132:       SlepcCheckLapackInfo("trsyl",info);
    133:       PetscCheck(scal==1.0,PETSC_COMM_SELF,PETSC_ERR_SUP,"Current implementation cannot handle scale factor %g",(double)scal);
    134:     }
    135:   }

    137:   /* backtransform B = Q*T*Q' */
    138:   PetscCallBLAS("BLASgemm",BLASgemm_("N","C",&n,&k,&n,&one,T,&ld,Q,&ld,&zero,W,&ld));
    139:   PetscCallBLAS("BLASgemm",BLASgemm_("N","N",&n,&k,&n,&one,Q,&ld,W,&ld,&zero,T,&ld));

    141:   /* flop count: Schur decomposition, triangular square root, and backtransform */
    142:   PetscCall(PetscLogFlops(25.0*n*n*n+n*n*n/3.0+4.0*n*n*k));

    144: #if !defined(PETSC_USE_COMPLEX)
    145:   PetscCall(PetscFree7(wr,wi,W,Q,work,s,p));
    146: #else
    147:   PetscCall(PetscFree7(wr,rwork,W,Q,work,s,p));
    148: #endif
    149:   PetscFunctionReturn(PETSC_SUCCESS);
    150: }

    152: #define DBMAXIT 25

    154: /*
    155:    Computes the principal square root of the matrix T using the product form
    156:    of the Denman-Beavers iteration.
    157:    T is overwritten with sqrtm(T) or inv(sqrtm(T)) depending on flag inv.
    158:  */
    159: PetscErrorCode FNSqrtmDenmanBeavers(FN fn,PetscBLASInt n,PetscScalar *T,PetscBLASInt ld,PetscBool inv)
    160: {
    161:   PetscScalar        *Told,*M=NULL,*invM,*work,work1,prod,alpha;
    162:   PetscScalar        szero=0.0,sone=1.0,smone=-1.0,spfive=0.5,sp25=0.25;
    163:   PetscReal          tol,Mres=0.0,detM,g,reldiff,fnormdiff,fnormT,rwork[1];
    164:   PetscBLASInt       N,i,it,*piv=NULL,info,query=-1,lwork;
    165:   const PetscBLASInt one=1;
    166:   PetscBool          converged=PETSC_FALSE,scale;
    167:   unsigned int       ftz;

    169:   PetscFunctionBegin;
    170:   N = n*n;
    171:   tol = PetscSqrtReal((PetscReal)n)*PETSC_MACHINE_EPSILON/2;
    172:   scale = PetscDefined(USE_REAL_SINGLE)? PETSC_FALSE: PETSC_TRUE;
    173:   PetscCall(SlepcSetFlushToZero(&ftz));

    175:   /* query work size */
    176:   PetscCallBLAS("LAPACKgetri",LAPACKgetri_(&n,M,&ld,piv,&work1,&query,&info));
    177:   PetscCall(PetscBLASIntCast((PetscInt)PetscRealPart(work1),&lwork));
    178:   PetscCall(PetscMalloc5(lwork,&work,n,&piv,n*n,&Told,n*n,&M,n*n,&invM));
    179:   PetscCall(PetscArraycpy(M,T,n*n));

    181:   if (inv) {  /* start recurrence with I instead of A */
    182:     PetscCall(PetscArrayzero(T,n*n));
    183:     for (i=0;i<n;i++) T[i+i*ld] += 1.0;
    184:   }

    186:   for (it=0;it<DBMAXIT && !converged;it++) {

    188:     if (scale) {  /* g = (abs(det(M)))^(-1/(2*n)) */
    189:       PetscCall(PetscArraycpy(invM,M,n*n));
    190:       PetscCallBLAS("LAPACKgetrf",LAPACKgetrf_(&n,&n,invM,&ld,piv,&info));
    191:       SlepcCheckLapackInfo("getrf",info);
    192:       prod = invM[0];
    193:       for (i=1;i<n;i++) prod *= invM[i+i*ld];
    194:       detM = PetscAbsScalar(prod);
    195:       g = (detM>PETSC_MAX_REAL)? 0.5: PetscPowReal(detM,-1.0/(2.0*n));
    196:       alpha = g;
    197:       PetscCallBLAS("BLASscal",BLASscal_(&N,&alpha,T,&one));
    198:       alpha = g*g;
    199:       PetscCallBLAS("BLASscal",BLASscal_(&N,&alpha,M,&one));
    200:       PetscCall(PetscLogFlops(2.0*n*n*n/3.0+2.0*n*n));
    201:     }

    203:     PetscCall(PetscArraycpy(Told,T,n*n));
    204:     PetscCall(PetscArraycpy(invM,M,n*n));

    206:     PetscCallBLAS("LAPACKgetrf",LAPACKgetrf_(&n,&n,invM,&ld,piv,&info));
    207:     SlepcCheckLapackInfo("getrf",info);
    208:     PetscCallBLAS("LAPACKgetri",LAPACKgetri_(&n,invM,&ld,piv,work,&lwork,&info));
    209:     SlepcCheckLapackInfo("getri",info);
    210:     PetscCall(PetscLogFlops(2.0*n*n*n/3.0+4.0*n*n*n/3.0));

    212:     for (i=0;i<n;i++) invM[i+i*ld] += 1.0;
    213:     PetscCallBLAS("BLASgemm",BLASgemm_("N","N",&n,&n,&n,&spfive,Told,&ld,invM,&ld,&szero,T,&ld));
    214:     for (i=0;i<n;i++) invM[i+i*ld] -= 1.0;

    216:     PetscCallBLAS("BLASaxpy",BLASaxpy_(&N,&sone,invM,&one,M,&one));
    217:     PetscCallBLAS("BLASscal",BLASscal_(&N,&sp25,M,&one));
    218:     for (i=0;i<n;i++) M[i+i*ld] -= 0.5;
    219:     PetscCall(PetscLogFlops(2.0*n*n*n+2.0*n*n));

    221:     Mres = LAPACKlange_("F",&n,&n,M,&n,rwork);
    222:     for (i=0;i<n;i++) M[i+i*ld] += 1.0;

    224:     if (scale) {
    225:       /* reldiff = norm(T - Told,'fro')/norm(T,'fro') */
    226:       PetscCallBLAS("BLASaxpy",BLASaxpy_(&N,&smone,T,&one,Told,&one));
    227:       fnormdiff = LAPACKlange_("F",&n,&n,Told,&n,rwork);
    228:       fnormT = LAPACKlange_("F",&n,&n,T,&n,rwork);
    229:       PetscCall(PetscLogFlops(7.0*n*n));
    230:       reldiff = fnormdiff/fnormT;
    231:       PetscCall(PetscInfo(fn,"it: %" PetscBLASInt_FMT " reldiff: %g scale: %g tol*scale: %g\n",it,(double)reldiff,(double)g,(double)(tol*g)));
    232:       if (reldiff<1e-2) scale = PETSC_FALSE;  /* Switch off scaling */
    233:     }

    235:     if (Mres<=tol) converged = PETSC_TRUE;
    236:   }

    238:   PetscCheck(Mres<=tol,PETSC_COMM_SELF,PETSC_ERR_LIB,"SQRTM not converged after %d iterations",DBMAXIT);
    239:   PetscCall(PetscFree5(work,piv,Told,M,invM));
    240:   PetscCall(SlepcResetFlushToZero(&ftz));
    241:   PetscFunctionReturn(PETSC_SUCCESS);
    242: }

    244: #define NSMAXIT 50

    246: /*
    247:    Computes the principal square root of the matrix A using the Newton-Schulz iteration.
    248:    T is overwritten with sqrtm(T) or inv(sqrtm(T)) depending on flag inv.
    249:  */
    250: PetscErrorCode FNSqrtmNewtonSchulz(FN fn,PetscBLASInt n,PetscScalar *A,PetscBLASInt ld,PetscBool inv)
    251: {
    252:   PetscScalar    *Y=A,*Yold,*Z,*Zold,*M;
    253:   PetscScalar    szero=0.0,sone=1.0,smone=-1.0,spfive=0.5,sthree=3.0;
    254:   PetscReal      sqrtnrm,tol,Yres=0.0,nrm,rwork[1],done=1.0;
    255:   PetscBLASInt   info,i,it,N,one=1,zero=0;
    256:   PetscBool      converged=PETSC_FALSE;
    257:   unsigned int   ftz;

    259:   PetscFunctionBegin;
    260:   N = n*n;
    261:   tol = PetscSqrtReal((PetscReal)n)*PETSC_MACHINE_EPSILON/2;
    262:   PetscCall(SlepcSetFlushToZero(&ftz));

    264:   PetscCall(PetscMalloc4(N,&Yold,N,&Z,N,&Zold,N,&M));

    266:   /* scale */
    267:   PetscCall(PetscArraycpy(Z,A,N));
    268:   for (i=0;i<n;i++) Z[i+i*ld] -= 1.0;
    269:   nrm = LAPACKlange_("fro",&n,&n,Z,&n,rwork);
    270:   sqrtnrm = PetscSqrtReal(nrm);
    271:   PetscCallBLAS("LAPACKlascl",LAPACKlascl_("G",&zero,&zero,&nrm,&done,&N,&one,A,&N,&info));
    272:   SlepcCheckLapackInfo("lascl",info);
    273:   tol *= nrm;
    274:   PetscCall(PetscInfo(fn,"||I-A||_F = %g, new tol: %g\n",(double)nrm,(double)tol));
    275:   PetscCall(PetscLogFlops(2.0*n*n));

    277:   /* Z = I */
    278:   PetscCall(PetscArrayzero(Z,N));
    279:   for (i=0;i<n;i++) Z[i+i*ld] = 1.0;

    281:   for (it=0;it<NSMAXIT && !converged;it++) {
    282:     /* Yold = Y, Zold = Z */
    283:     PetscCall(PetscArraycpy(Yold,Y,N));
    284:     PetscCall(PetscArraycpy(Zold,Z,N));

    286:     /* M = (3*I-Zold*Yold) */
    287:     PetscCall(PetscArrayzero(M,N));
    288:     for (i=0;i<n;i++) M[i+i*ld] = sthree;
    289:     PetscCallBLAS("BLASgemm",BLASgemm_("N","N",&n,&n,&n,&smone,Zold,&ld,Yold,&ld,&sone,M,&ld));

    291:     /* Y = (1/2)*Yold*M, Z = (1/2)*M*Zold */
    292:     PetscCallBLAS("BLASgemm",BLASgemm_("N","N",&n,&n,&n,&spfive,Yold,&ld,M,&ld,&szero,Y,&ld));
    293:     PetscCallBLAS("BLASgemm",BLASgemm_("N","N",&n,&n,&n,&spfive,M,&ld,Zold,&ld,&szero,Z,&ld));

    295:     /* reldiff = norm(Y-Yold,'fro')/norm(Y,'fro') */
    296:     PetscCallBLAS("BLASaxpy",BLASaxpy_(&N,&smone,Y,&one,Yold,&one));
    297:     Yres = LAPACKlange_("fro",&n,&n,Yold,&n,rwork);
    298:     PetscCheck(!PetscIsNanReal(Yres),PETSC_COMM_SELF,PETSC_ERR_FP,"The computed norm is not-a-number");
    299:     if (Yres<=tol) converged = PETSC_TRUE;
    300:     PetscCall(PetscInfo(fn,"it: %" PetscBLASInt_FMT " res: %g\n",it,(double)Yres));

    302:     PetscCall(PetscLogFlops(6.0*n*n*n+2.0*n*n));
    303:   }

    305:   PetscCheck(Yres<=tol,PETSC_COMM_SELF,PETSC_ERR_LIB,"SQRTM not converged after %d iterations",NSMAXIT);

    307:   /* undo scaling */
    308:   if (inv) {
    309:     PetscCall(PetscArraycpy(A,Z,N));
    310:     PetscCallBLAS("LAPACKlascl",LAPACKlascl_("G",&zero,&zero,&sqrtnrm,&done,&N,&one,A,&N,&info));
    311:   } else PetscCallBLAS("LAPACKlascl",LAPACKlascl_("G",&zero,&zero,&done,&sqrtnrm,&N,&one,A,&N,&info));
    312:   SlepcCheckLapackInfo("lascl",info);

    314:   PetscCall(PetscFree4(Yold,Z,Zold,M));
    315:   PetscCall(SlepcResetFlushToZero(&ftz));
    316:   PetscFunctionReturn(PETSC_SUCCESS);
    317: }

    319: #if defined(PETSC_HAVE_CUDA)
    320: #include "../src/sys/classes/fn/impls/cuda/fnutilcuda.h"
    321: #include <slepccupmblas.h>

    323: /*
    324:  * Matrix square root by Newton-Schulz iteration. CUDA version.
    325:  * Computes the principal square root of the matrix A using the
    326:  * Newton-Schulz iteration. A is overwritten with sqrtm(A).
    327:  */
    328: PetscErrorCode FNSqrtmNewtonSchulz_CUDA(FN fn,PetscBLASInt n,PetscScalar *d_A,PetscBLASInt ld,PetscBool inv)
    329: {
    330:   PetscScalar        *d_Yold,*d_Z,*d_Zold,*d_M,alpha;
    331:   PetscReal          nrm,sqrtnrm,tol,Yres=0.0;
    332:   const PetscScalar  szero=0.0,sone=1.0,smone=-1.0,spfive=0.5,sthree=3.0;
    333:   PetscInt           it;
    334:   PetscBLASInt       N;
    335:   const PetscBLASInt one=1;
    336:   PetscBool          converged=PETSC_FALSE;
    337:   cublasHandle_t     cublasv2handle;

    339:   PetscFunctionBegin;
    340:   PetscCall(PetscDeviceInitialize(PETSC_DEVICE_CUDA)); /* For CUDA event timers */
    341:   PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
    342:   N = n*n;
    343:   tol = PetscSqrtReal((PetscReal)n)*PETSC_MACHINE_EPSILON/2;

    345:   PetscCallCUDA(cudaMalloc((void **)&d_Yold,sizeof(PetscScalar)*N));
    346:   PetscCallCUDA(cudaMalloc((void **)&d_Z,sizeof(PetscScalar)*N));
    347:   PetscCallCUDA(cudaMalloc((void **)&d_Zold,sizeof(PetscScalar)*N));
    348:   PetscCallCUDA(cudaMalloc((void **)&d_M,sizeof(PetscScalar)*N));

    350:   PetscCall(PetscLogGpuTimeBegin());

    352:   /* Z = I; */
    353:   PetscCallCUDA(cudaMemset(d_Z,0,sizeof(PetscScalar)*N));
    354:   PetscCall(set_diagonal(n,d_Z,ld,sone));

    356:   /* scale */
    357:   PetscCallCUBLAS(cublasXaxpy(cublasv2handle,N,&smone,d_A,one,d_Z,one));
    358:   PetscCallCUBLAS(cublasXnrm2(cublasv2handle,N,d_Z,one,&nrm));
    359:   sqrtnrm = PetscSqrtReal(nrm);
    360:   alpha = 1.0/nrm;
    361:   PetscCallCUBLAS(cublasXscal(cublasv2handle,N,&alpha,d_A,one));
    362:   tol *= nrm;
    363:   PetscCall(PetscInfo(fn,"||I-A||_F = %g, new tol: %g\n",(double)nrm,(double)tol));
    364:   PetscCall(PetscLogGpuFlops(2.0*n*n));

    366:   /* Z = I; */
    367:   PetscCallCUDA(cudaMemset(d_Z,0,sizeof(PetscScalar)*N));
    368:   PetscCall(set_diagonal(n,d_Z,ld,sone));

    370:   for (it=0;it<NSMAXIT && !converged;it++) {
    371:     /* Yold = Y, Zold = Z */
    372:     PetscCallCUDA(cudaMemcpy(d_Yold,d_A,sizeof(PetscScalar)*N,cudaMemcpyDeviceToDevice));
    373:     PetscCallCUDA(cudaMemcpy(d_Zold,d_Z,sizeof(PetscScalar)*N,cudaMemcpyDeviceToDevice));

    375:     /* M = (3*I - Zold*Yold) */
    376:     PetscCallCUDA(cudaMemset(d_M,0,sizeof(PetscScalar)*N));
    377:     PetscCall(set_diagonal(n,d_M,ld,sthree));
    378:     PetscCallCUBLAS(cublasXgemm(cublasv2handle,CUBLAS_OP_N,CUBLAS_OP_N,n,n,n,&smone,d_Zold,ld,d_Yold,ld,&sone,d_M,ld));

    380:     /* Y = (1/2) * Yold * M, Z = (1/2) * M * Zold */
    381:     PetscCallCUBLAS(cublasXgemm(cublasv2handle,CUBLAS_OP_N,CUBLAS_OP_N,n,n,n,&spfive,d_Yold,ld,d_M,ld,&szero,d_A,ld));
    382:     PetscCallCUBLAS(cublasXgemm(cublasv2handle,CUBLAS_OP_N,CUBLAS_OP_N,n,n,n,&spfive,d_M,ld,d_Zold,ld,&szero,d_Z,ld));

    384:     /* reldiff = norm(Y-Yold,'fro')/norm(Y,'fro') */
    385:     PetscCallCUBLAS(cublasXaxpy(cublasv2handle,N,&smone,d_A,one,d_Yold,one));
    386:     PetscCallCUBLAS(cublasXnrm2(cublasv2handle,N,d_Yold,one,&Yres));
    387:     PetscCheck(!PetscIsNanReal(Yres),PETSC_COMM_SELF,PETSC_ERR_FP,"The computed norm is not-a-number");
    388:     if (Yres<=tol) converged = PETSC_TRUE;
    389:     PetscCall(PetscInfo(fn,"it: %" PetscInt_FMT " res: %g\n",it,(double)Yres));

    391:     PetscCall(PetscLogGpuFlops(6.0*n*n*n+2.0*n*n));
    392:   }

    394:   PetscCheck(Yres<=tol,PETSC_COMM_SELF,PETSC_ERR_LIB,"SQRTM not converged after %d iterations", NSMAXIT);

    396:   /* undo scaling */
    397:   if (inv) {
    398:     alpha = 1.0/sqrtnrm;
    399:     PetscCallCUBLAS(cublasXscal(cublasv2handle,N,&alpha,d_Z,one));
    400:     PetscCallCUDA(cudaMemcpy(d_A,d_Z,sizeof(PetscScalar)*N,cudaMemcpyDeviceToDevice));
    401:   } else {
    402:     alpha = sqrtnrm;
    403:     PetscCallCUBLAS(cublasXscal(cublasv2handle,N,&alpha,d_A,one));
    404:   }

    406:   PetscCall(PetscLogGpuTimeEnd());
    407:   PetscCallCUDA(cudaFree(d_Yold));
    408:   PetscCallCUDA(cudaFree(d_Z));
    409:   PetscCallCUDA(cudaFree(d_Zold));
    410:   PetscCallCUDA(cudaFree(d_M));
    411:   PetscFunctionReturn(PETSC_SUCCESS);
    412: }

    414: #if defined(PETSC_HAVE_MAGMA)
    415: #include <slepcmagma.h>

    417: /*
    418:  * Matrix square root by product form of Denman-Beavers iteration. CUDA version.
    419:  * Computes the principal square root of the matrix T using the product form
    420:  * of the Denman-Beavers iteration. T is overwritten with sqrtm(T).
    421:  */
    422: PetscErrorCode FNSqrtmDenmanBeavers_CUDAm(FN fn,PetscBLASInt n,PetscScalar *d_T,PetscBLASInt ld,PetscBool inv)
    423: {
    424:   PetscScalar    *d_Told,*d_M,*d_invM,*d_work,prod,szero=0.0,sone=1.0,smone=-1.0,spfive=0.5,sneg_pfive=-0.5,sp25=0.25,alpha;
    425:   PetscReal      tol,Mres=0.0,detM,g,reldiff,fnormdiff,fnormT;
    426:   PetscInt       it,lwork,nb;
    427:   PetscBLASInt   N,one=1,*piv=NULL;
    428:   PetscBool      converged=PETSC_FALSE,scale;
    429:   cublasHandle_t cublasv2handle;

    431:   PetscFunctionBegin;
    432:   PetscCall(PetscDeviceInitialize(PETSC_DEVICE_CUDA)); /* For CUDA event timers */
    433:   PetscCall(PetscCUBLASGetHandle(&cublasv2handle));
    434:   PetscCall(SlepcMagmaInit());
    435:   N = n*n;
    436:   scale = PetscDefined(USE_REAL_SINGLE)? PETSC_FALSE: PETSC_TRUE;
    437:   tol = PetscSqrtReal((PetscReal)n)*PETSC_MACHINE_EPSILON/2;

    439:   /* query work size */
    440:   nb = magma_get_xgetri_nb(n);
    441:   lwork = nb*n;
    442:   PetscCall(PetscMalloc1(n,&piv));
    443:   PetscCallCUDA(cudaMalloc((void **)&d_work,sizeof(PetscScalar)*lwork));
    444:   PetscCallCUDA(cudaMalloc((void **)&d_Told,sizeof(PetscScalar)*N));
    445:   PetscCallCUDA(cudaMalloc((void **)&d_M,sizeof(PetscScalar)*N));
    446:   PetscCallCUDA(cudaMalloc((void **)&d_invM,sizeof(PetscScalar)*N));

    448:   PetscCall(PetscLogGpuTimeBegin());
    449:   PetscCallCUDA(cudaMemcpy(d_M,d_T,sizeof(PetscScalar)*N,cudaMemcpyDeviceToDevice));
    450:   if (inv) {  /* start recurrence with I instead of A */
    451:     PetscCallCUDA(cudaMemset(d_T,0,sizeof(PetscScalar)*N));
    452:     PetscCall(set_diagonal(n,d_T,ld,1.0));
    453:   }

    455:   for (it=0;it<DBMAXIT && !converged;it++) {

    457:     if (scale) { /* g = (abs(det(M)))^(-1/(2*n)); */
    458:       PetscCallCUDA(cudaMemcpy(d_invM,d_M,sizeof(PetscScalar)*N,cudaMemcpyDeviceToDevice));
    459:       PetscCallMAGMA(magma_xgetrf_gpu,n,n,d_invM,ld,piv);
    460:       PetscCall(mult_diagonal(n,d_invM,ld,&prod));
    461:       detM = PetscAbsScalar(prod);
    462:       g = (detM>PETSC_MAX_REAL)? 0.5: PetscPowReal(detM,-1.0/(2.0*n));
    463:       alpha = g;
    464:       PetscCallCUBLAS(cublasXscal(cublasv2handle,N,&alpha,d_T,one));
    465:       alpha = g*g;
    466:       PetscCallCUBLAS(cublasXscal(cublasv2handle,N,&alpha,d_M,one));
    467:       PetscCall(PetscLogGpuFlops(2.0*n*n*n/3.0+2.0*n*n));
    468:     }

    470:     PetscCallCUDA(cudaMemcpy(d_Told,d_T,sizeof(PetscScalar)*N,cudaMemcpyDeviceToDevice));
    471:     PetscCallCUDA(cudaMemcpy(d_invM,d_M,sizeof(PetscScalar)*N,cudaMemcpyDeviceToDevice));

    473:     PetscCallMAGMA(magma_xgetrf_gpu,n,n,d_invM,ld,piv);
    474:     PetscCallMAGMA(magma_xgetri_gpu,n,d_invM,ld,piv,d_work,lwork);
    475:     PetscCall(PetscLogGpuFlops(2.0*n*n*n/3.0+4.0*n*n*n/3.0));

    477:     PetscCall(shift_diagonal(n,d_invM,ld,sone));
    478:     PetscCallCUBLAS(cublasXgemm(cublasv2handle,CUBLAS_OP_N,CUBLAS_OP_N,n,n,n,&spfive,d_Told,ld,d_invM,ld,&szero,d_T,ld));
    479:     PetscCall(shift_diagonal(n,d_invM,ld,smone));

    481:     PetscCallCUBLAS(cublasXaxpy(cublasv2handle,N,&sone,d_invM,one,d_M,one));
    482:     PetscCallCUBLAS(cublasXscal(cublasv2handle,N,&sp25,d_M,one));
    483:     PetscCall(shift_diagonal(n,d_M,ld,sneg_pfive));
    484:     PetscCall(PetscLogGpuFlops(2.0*n*n*n+2.0*n*n));

    486:     PetscCallCUBLAS(cublasXnrm2(cublasv2handle,N,d_M,one,&Mres));
    487:     PetscCall(shift_diagonal(n,d_M,ld,sone));

    489:     if (scale) {
    490:       /* reldiff = norm(T - Told,'fro')/norm(T,'fro'); */
    491:       PetscCallCUBLAS(cublasXaxpy(cublasv2handle,N,&smone,d_T,one,d_Told,one));
    492:       PetscCallCUBLAS(cublasXnrm2(cublasv2handle,N,d_Told,one,&fnormdiff));
    493:       PetscCallCUBLAS(cublasXnrm2(cublasv2handle,N,d_T,one,&fnormT));
    494:       PetscCall(PetscLogGpuFlops(7.0*n*n));
    495:       reldiff = fnormdiff/fnormT;
    496:       PetscCall(PetscInfo(fn,"it: %" PetscInt_FMT " reldiff: %g scale: %g tol*scale: %g\n",it,(double)reldiff,(double)g,(double)tol*g));
    497:       if (reldiff<1e-2) scale = PETSC_FALSE; /* Switch to no scaling. */
    498:     }

    500:     PetscCall(PetscInfo(fn,"it: %" PetscInt_FMT " Mres: %g\n",it,(double)Mres));
    501:     if (Mres<=tol) converged = PETSC_TRUE;
    502:   }

    504:   PetscCheck(Mres<=tol,PETSC_COMM_SELF,PETSC_ERR_LIB,"SQRTM not converged after %d iterations", DBMAXIT);
    505:   PetscCall(PetscLogGpuTimeEnd());
    506:   PetscCall(PetscFree(piv));
    507:   PetscCallCUDA(cudaFree(d_work));
    508:   PetscCallCUDA(cudaFree(d_Told));
    509:   PetscCallCUDA(cudaFree(d_M));
    510:   PetscCallCUDA(cudaFree(d_invM));
    511:   PetscFunctionReturn(PETSC_SUCCESS);
    512: }
    513: #endif /* PETSC_HAVE_MAGMA */

    515: #endif /* PETSC_HAVE_CUDA */

    517: #define ITMAX 5

    519: /*
    520:    Estimate norm(A^m,1) by block 1-norm power method (required workspace is 11*n)
    521: */
    522: static PetscErrorCode SlepcNormEst1(PetscBLASInt n,PetscScalar *A,PetscInt m,PetscScalar *work,PetscRandom rand,PetscReal *nrm)
    523: {
    524:   PetscScalar    *X,*Y,*Z,*S,*S_old,*aux,val,sone=1.0,szero=0.0;
    525:   PetscReal      est=0.0,est_old,vals[2]={0.0,0.0},*zvals,maxzval[2],raux;
    526:   PetscBLASInt   i,j,t=2,it=0,ind[2],est_j=0,m1;

    528:   PetscFunctionBegin;
    529:   X = work;
    530:   Y = work + 2*n;
    531:   Z = work + 4*n;
    532:   S = work + 6*n;
    533:   S_old = work + 8*n;
    534:   zvals = (PetscReal*)(work + 10*n);

    536:   for (i=0;i<n;i++) {  /* X has columns of unit 1-norm */
    537:     X[i] = 1.0/n;
    538:     PetscCall(PetscRandomGetValue(rand,&val));
    539:     if (PetscRealPart(val) < 0.5) X[i+n] = -1.0/n;
    540:     else X[i+n] = 1.0/n;
    541:   }
    542:   for (i=0;i<t*n;i++) S[i] = 0.0;
    543:   ind[0] = 0; ind[1] = 0;
    544:   est_old = 0;
    545:   while (1) {
    546:     it++;
    547:     for (j=0;j<m;j++) {  /* Y = A^m*X */
    548:       PetscCallBLAS("BLASgemm",BLASgemm_("N","N",&n,&t,&n,&sone,A,&n,X,&n,&szero,Y,&n));
    549:       if (j<m-1) SlepcSwap(X,Y,aux);
    550:     }
    551:     for (j=0;j<t;j++) {  /* vals[j] = norm(Y(:,j),1) */
    552:       vals[j] = 0.0;
    553:       for (i=0;i<n;i++) vals[j] += PetscAbsScalar(Y[i+j*n]);
    554:     }
    555:     if (vals[0]<vals[1]) {
    556:       SlepcSwap(vals[0],vals[1],raux);
    557:       m1 = 1;
    558:     } else m1 = 0;
    559:     est = vals[0];
    560:     if (est>est_old || it==2) est_j = ind[m1];
    561:     if (it>=2 && est<=est_old) {
    562:       est = est_old;
    563:       break;
    564:     }
    565:     est_old = est;
    566:     if (it>ITMAX) break;
    567:     SlepcSwap(S,S_old,aux);
    568:     for (i=0;i<t*n;i++) {  /* S = sign(Y) */
    569:       S[i] = (PetscRealPart(Y[i]) < 0.0)? -1.0: 1.0;
    570:     }
    571:     for (j=0;j<m;j++) {  /* Z = (A^T)^m*S */
    572:       PetscCallBLAS("BLASgemm",BLASgemm_("C","N",&n,&t,&n,&sone,A,&n,S,&n,&szero,Z,&n));
    573:       if (j<m-1) SlepcSwap(S,Z,aux);
    574:     }
    575:     maxzval[0] = -1; maxzval[1] = -1;
    576:     ind[0] = 0; ind[1] = 0;
    577:     for (i=0;i<n;i++) {  /* zvals[i] = norm(Z(i,:),inf) */
    578:       zvals[i] = PetscMax(PetscAbsScalar(Z[i+0*n]),PetscAbsScalar(Z[i+1*n]));
    579:       if (zvals[i]>maxzval[0]) {
    580:         maxzval[0] = zvals[i];
    581:         ind[0] = i;
    582:       } else if (zvals[i]>maxzval[1]) {
    583:         maxzval[1] = zvals[i];
    584:         ind[1] = i;
    585:       }
    586:     }
    587:     if (it>=2 && maxzval[0]==zvals[est_j]) break;
    588:     for (i=0;i<t*n;i++) X[i] = 0.0;
    589:     for (j=0;j<t;j++) X[ind[j]+j*n] = 1.0;
    590:   }
    591:   *nrm = est;
    592:   /* Flop count is roughly (it * 2*m * t*gemv) = 4*its*m*t*n*n */
    593:   PetscCall(PetscLogFlops(4.0*it*m*t*n*n));
    594:   PetscFunctionReturn(PETSC_SUCCESS);
    595: }

    597: #define SMALLN 100

    599: /*
    600:    Estimate norm(A^m,1) (required workspace is 2*n*n)
    601: */
    602: PetscErrorCode SlepcNormAm(PetscBLASInt n,PetscScalar *A,PetscInt m,PetscScalar *work,PetscRandom rand,PetscReal *nrm)
    603: {
    604:   PetscScalar    *v=work,*w=work+n*n,*aux,sone=1.0,szero=0.0;
    605:   PetscReal      rwork[1],tmp;
    606:   PetscBLASInt   i,j,one=1;
    607:   PetscBool      isrealpos=PETSC_TRUE;

    609:   PetscFunctionBegin;
    610:   if (n<SMALLN) {   /* compute matrix power explicitly */
    611:     if (m==1) {
    612:       *nrm = LAPACKlange_("O",&n,&n,A,&n,rwork);
    613:       PetscCall(PetscLogFlops(1.0*n*n));
    614:     } else {  /* m>=2 */
    615:       PetscCallBLAS("BLASgemm",BLASgemm_("N","N",&n,&n,&n,&sone,A,&n,A,&n,&szero,v,&n));
    616:       for (j=0;j<m-2;j++) {
    617:         PetscCallBLAS("BLASgemm",BLASgemm_("N","N",&n,&n,&n,&sone,A,&n,v,&n,&szero,w,&n));
    618:         SlepcSwap(v,w,aux);
    619:       }
    620:       *nrm = LAPACKlange_("O",&n,&n,v,&n,rwork);
    621:       PetscCall(PetscLogFlops(2.0*n*n*n*(m-1)+1.0*n*n));
    622:     }
    623:   } else {
    624:     for (i=0;i<n;i++)
    625:       for (j=0;j<n;j++)
    626: #if defined(PETSC_USE_COMPLEX)
    627:         if (PetscRealPart(A[i+j*n])<0.0 || PetscImaginaryPart(A[i+j*n])!=0.0) { isrealpos = PETSC_FALSE; break; }
    628: #else
    629:         if (A[i+j*n]<0.0) { isrealpos = PETSC_FALSE; break; }
    630: #endif
    631:     if (isrealpos) {   /* for positive matrices only */
    632:       for (i=0;i<n;i++) v[i] = 1.0;
    633:       for (j=0;j<m;j++) {  /* w = A'*v */
    634:         PetscCallBLAS("BLASgemv",BLASgemv_("C",&n,&n,&sone,A,&n,v,&one,&szero,w,&one));
    635:         SlepcSwap(v,w,aux);
    636:       }
    637:       PetscCall(PetscLogFlops(2.0*n*n*m));
    638:       *nrm = 0.0;
    639:       for (i=0;i<n;i++) if ((tmp = PetscAbsScalar(v[i])) > *nrm) *nrm = tmp;   /* norm(v,inf) */
    640:     } else PetscCall(SlepcNormEst1(n,A,m,work,rand,nrm));
    641:   }
    642:   PetscFunctionReturn(PETSC_SUCCESS);
    643: }