/**
* Accumulate covariance matrix (ncycle * NBASE x ncycle * NBASE).
* Only lower triangular matrix is accumulated.
* Note: If V is NULL, the required memory is allocated.
* - p: Matrix of processed intensities
* - lambda: Brightness of cluster
* - base: Current base call
* - do_full: Calculate full covariance matrix, or just band suitable for fitting Omega
* - V: Covariance matrix used for accumulation
*/
static MAT accumulate_covariance( const real_t we, const MAT p, const real_t lambda, const NUC * base, const bool do_full, MAT V) {
validate(NULL!=p, NULL);
validate(NBASE==p->nrow, NULL);
validate(lambda>=0.0, NULL);
const uint_fast32_t ncycle = p->ncol;
const int lda = ncycle * NBASE;
// Allocate memory for V if necessary
if (NULL==V) {
V = new_MAT(lda, lda);
if (NULL==V) {
return NULL;
}
}
// Perform accumulation. V += R R^t
// Note: R = P - \lambda I_b, where I_b is unit vector with b'th elt = 1
for (uint_fast32_t cy = 0; cy < ncycle; cy++) {
if (!isambig(base[cy])) {
p->x[cy * NBASE + base[cy]] -= lambda;
}
}
if ( do_full ) {
syr(LAPACK_LOWER, &lda, &we, p->x, LAPACK_UNIT, V->x, &lda);
}
else {
// Only update base*base blocks on the diagonal or immediately above and below it
// Update more elements than necessary but excess are ignored later.
for ( int i=0 ; i<lda ; i++) {
for ( int j=i ; j<i+2*NBASE ; j++ ) {
if(j>=lda) {
break;
}
V->x[i*lda+j] += we * p->x[i] * p->x[j];
}
}
}
return V;
}
void MTLmarks::DmatVecRun(std::string benchmark) {
if(benchmark == "dmatvecmult"){
mtl_result = dmatvecmult(size, steps);
}
else if(benchmark == "gemv1"){
mtl_result = gemv1(size, steps);
}
else if(benchmark == "gemv2"){
mtl_result = gemv2(size, steps);
}
else if(benchmark == "ger1"){
mtl_result = ger1(size, steps);
}
else if(benchmark == "syr"){
mtl_result = syr(size, steps);
}
else if(benchmark == "trmv1"){
mtl_result = trmv1(size, steps);
}
else if(benchmark == "trmv2"){
mtl_result = trmv2(size, steps);
}
else if(benchmark == "cmajordmvmult"){
mtl_result = cmajordmvmult(size, steps);
}
else if(benchmark == "rmajordmvmult"){
mtl_result = rmajordmvmult(size, steps);
}
else if(benchmark == "custom"){
mtl_result = custom(size, steps);
}
else{
std::cerr << "Ublasmarks benchmark does not exist." << std::endl;
exit(1);
}
}
