Hello, If it can help qmake from Qtcreator LTS 6.2.4 does not work with clang 15.0.. You probably have to use clang 13.0

Hi, No I can't give all the codes of my applications. To give an idea, with and without Eigen library it is about factor analysis. I made C and C++ versions. The tests compute the factors of 64 questions of a survey . It gives a square matrix of 306 items . The method used to extract the factors is algorithmic. On MacBook pro Intel 2020 the total computation in C++ or C gives about 10 seconds with clang 13.x but 54 seconds with clang.15 . For C++ as I mentionned the complier option are -std=c++17 -Ofast -march=native -funroll-loops -flto -DNDEBUG For the C version idem gcc -Ofast -march=native -funroll-loops -flto -DBEBUG -o a file1.c file2.c cpp1.o file3.c -o a -lm -lstdc++ The computation time suffers a big regression. I do not see where it comes from. djm44

I'am afraid the algorithmic portion has nothing to do with the informations I gave to you. I can confirm in this sense. And more on my mac I have two virtual machines for simple use , vmware and virtualbox. The same code I mentionned earlier runs 12 seconds on vimware and 13 seconds on virtualbaox knowing that these two virtual machine use a lot less ressources than the host MacOs. I precise the regression appears with clang 15.0 not clang 13.x. . So 5 times slower for g++ clang 15.0 if you want a part of computational code here : template < typename T > int OAnacorr<T>::compute_afc_for_burt() { size_t n = 0, i = 0 ; T AKSI, ALAMBDA, PHI2, perc = 0 ; T CUMUL ; long TEST0, TEST1 ; T TOTA = M.sum() ; KT VL, VC ; VC.resize( M.getnc() ); VL.resize( M.getnl() ); Mtype M2( M.getnl(), M.getnc() ) ; KT PII ; M.peek_sum_rows( PII ) ; M.to_percent(); KT PJ ; M.peek_sum_cols( PJ ); Mtype K2( M ) ; KT KI( M.getnc() ) ; CUMUL = 0.0 ; PHI2 = K2.khi_deux_pond() ; if( Mtype::isnan(PHI2) || PHI2 <= 0 ) return 1; K2.peek_sum_cols( KI ) ; KT VVI( KI ) ; KT SVI( KI ) ; Mtype TH = M.get_theoric() ; /*****************************************/ KT VCSUP ; KT KSUP ; KT SKI ; KT A, D ; if( g_xsup > 0 ) { VCSUP.resize( TSC.getnl()); KSUP.resize( TSC.getnl() ); A.resize ( VCSUP.size() ); D.resize ( VCSUP.size() ) ; TSC.peek_sum_rows( KSUP ); Mtype STHEO ( TSC.getnl(), TSC.getnc() ) ; for( size_t i = 0 ; i < TSC.getnl(); i++ ) for( size_t j = 0; j < TSC.getnc(); j++ ) STHEO(i,j) = (PII[j] * KSUP[i]) / TOTA ; Mtype ECA = TSC - STHEO; Mtype SKI2 = ( ECA * ECA ) / STHEO ; SKI2 /= TOTA ; SKI2.peek_sum_rows( SKI ); for( size_t i = 0; i < VCSUP.size(); i++ ) D[i] = KSUP[i] / TOTA ; TSC.to_percent(); vect_to_percent( KSUP ); inth( TSC, KSUP, PJ, 1.0 ); } os << "\nAnalyse des correspondances (AFC)" << std::endl << std::endl ; os << "Phi Deux = " << std::setw(8) << std::setprecision(6) << std::fixed << PHI2 << std::endl; for ( n = 0 ; n < g_nbf ; n++ ) { if( ( 100.00 - CUMUL ) < 0.0000001 ) goto tend ; vect_zero( M, VL ) ; i=0 ; do { TEST0 = (VL[0] * 10000000.0) ; prod_by_cols( M, VC, VL ) ; AKSI = reduce_by_pond( PJ, VC) ; i++; if( i > 20000 ) { return 1 ; } prod_by_rows( M, VL, VC ) ; AKSI = reduce_by_pond( PJ, VL ) ; TEST1 = (VL[0] * 10000000.0) ; } while ( TEST1 != TEST0 ); if( g_xsup > 0 ) { prod_by_rows( TSC, VCSUP, VC ); T RX = reduce_by_pond( KSUP, VCSUP ); mul_vect( VCSUP, RX ); for ( size_t i = 0 ; i < VCSUP.size(); i++ ) if( Mtype::isnan(VCSUP[i])) VCSUP[i] = 0 ; WSUP.push_back( VCSUP ); } mul_vect( VC, AKSI ); WWC.push_back( VC ) ; ALAMBDA = ( n != 0 ) ? AKSI * AKSI : PHI2 ; rebuild_pond( M2, VC, PJ, AKSI ) ; M -= M2 ; if( n == 0 ) { WWC.push_back( PJ ); if( g_xsup > 0 ) { WSUP.push_back(VCSUP); } } if( n != 0 ) { mul_and_div( M2, TH ); M2.peek_sum_cols( KI ) ; SVI = KI ; div_vect( SVI, VVI ); WWC.push_back( SVI ); perc = (ALAMBDA / PHI2) * 100 ; CUMUL += perc ; g_nbvectors += 1 ; if( g_xsup > 0 ) { for( size_t i = 0 ; i < VCSUP.size(); i++ ) { A[i] = VCSUP[i] * VCSUP[i] * D[i] / SKI[i] ; // cos2 if ( Mtype::isnan(A[i])) A[i] = 0 ; if( A[i] >= 1 ) A[i] = 0.999; } WSUP.push_back(A); } std::ostringstream ox ; ox << "F" << n ; os << std::setw(5) << std::setfill(' ') << std::left << ox.str() << " Val Propre = " << std::setw(8) << std::setprecision(6) << std::fixed << ALAMBDA << " Pourcent= " << std::setw(5) << std::setprecision(2) << std::right << std::fixed << perc << " Cumulé= " << std::setw(6) << std::setprecision(2) << std::right << CUMUL << " Nb iter= " << std::setw(5) << std::right << ((n>0) ? i : i) << std::endl ; } div_vect( KI, ALAMBDA ); WWC.push_back( KI); if( g_xsup > 0 ) { for( size_t i = 0 ; i < VCSUP.size(); i++ ) { A[i] = VCSUP[i] * VCSUP[i] * D[i] / ALAMBDA ; // cpf if ( Mtype::isnan(A[i])) A[i] = 0 ; if( A[i] >= 1 ) A[i] = 0.999; } WSUP.push_back(A); } } tend: g_nbf = n ; os << std::endl; return 0; } djm44

KT is std::vector < double > Mtype is std::vector < double > with different subscript T is double There no assembler instructions -03 makes better than -Ofast 3 times slower instead of 5 times slower. You pointed a right thing. It seems the -Ofast option does not work any more My opinion there is a security addon which blocks the code , something that blocks the memory access or that verify systematically array access or array bounds ? Or some new default options that makes code slower ? The codes of the two versions of my code in C and C++ has been tested with valgrind on Linux and gives no errors ( memory, and array bounds ).

I am not used to use mailing list. I read the release notes of Apple clang 15.0 . It's very bulky. I did not notice any thing about the changes in the -Ofast command option. What are the specific flags added to -O3 in -Ofast ? For my test -Ofast was compatible with the computations.

Hi, You say slow compile time for Xcode which also uses command-line-tools. But did you notice a slower execution or run time ? Well if you can test . I did.

Hi , Yes I compare things that are comparable . Clang 15.0 with the previous version using command line tools not Xcode. On the same macbook pro intel 2020. My previous toochain was Command_Line_Tools_for_Xcode_14.3.1_Release_Candidate. What I'm measuring is run time not compile time. My code has sense being fast , I do not bother about compile time. So eliminating -march=native I get a less worse performance . For now with different modifications I get 2 times slower than the previous clang. Some things have changed in this last version of clang compiler that is not documented. I confirm the same application in C or C++ works about 2 times faster on the linux guests vmware and virtualBox. And the same 2 times faster with mingw c++ on Windows Bootcamp. The only one which lacks performance is the last Apple clang 15.0 g++ or clang . With the previous toolchain I used : g++ -std=c++17 -Ofast -march=native -funroll-loops -lfto -DNDEBUG -o a prog.cpp With clang 15.0 to get less worse performance : g++ -std=c++17 -Ofast -funroll-loops -lfto -DNDEBUG -o a prog.cpp the perf : On linux guests about 11 seconds On previous clang about 10 seconds On Windows BootCamp about 11 seconds On last Apple clang 15.0 about 22 seconds.

Hi , I don't see what you mean with clang -###. I won't try all command line options. I am not able to search in disassemblies . On Linux it is 15.0.7 clang version but I use gnu gcc . What is -ld_classic? I notice a global loss of run time performance i between Apple clang version 14.0.3 (clang-1403.0.22.14.1) and Apple clang version 15.0.0 (clang-1500.0.40.1) And Apple made it impossible to revert to 14.0.3 command line tools.

So -O3 makes code slower than -Ofast I tried -Wl,-ld_classic gives no difference I notice -march=native make code very much slower. I guess what changed with this last version is -march=native May be less support for Intel processor With the previous version of clang -march=native made code faster. I don't know about assembly. The fact is that my code and the compiler flags have not changed but the changes are in clang 15.0 and are not documented . Same codes run faster on Linux guests vmware and virtualbox. with gnu gcc or g++ And on Windows BootCamp with Mingw gcc g++ I precise my codes do not use graphical UI. It gives only results on the terminal.

So -O3 makes code slower than -Ofast I tried -Wl,-ld_classic gives no difference I notice -march=native make code very much slower. I guess what changed with this last version is -march=native May be less support for Intel processor With the previous version of clang -march=native made code faster. I don't know about assembly. The fact is that my code and the compiler flags have not changed but the changes are in clang 15.0 and are not documented . Same codes run faster on Linux guests vmware and virtualbox. with gnu gcc or g++ And on Windows BootCamp with Mingw gcc g++ I precise my codes do not use graphical UI. It gives only results on the terminal.

diiscard

I checked with with option -### apple-macosx14.0.0 is invoqued is it right ? or shoud it be apple-macosx15.0.0 ? the -march=native is effective but it makes code slower . With the previous version -march=native made the code faster. Is the disk crypted by défault with Sonoma which could make code slower ? sorry I better put this in a reply than a comment. If you want to i can send you one of my examples a group of about 15 small files in a zip format.

Hi , the FB number is FB13252912 . I sent a zip file wihich can run a test merely in C language The same code compiled with fastest options gives a better run time on Linux VirtualBox vmWare guests and Windows Boot Camp . You'll see difference by comparing clang 15 vs clang 14 on a MacBook Pro Intel 2020

Impossible to re-upload so a new FB FB13253046

Tanks for these informations But on string manipulation with -march=native or -march=haswell I get a very poor performance compared with Linux guests vmWare or VirtualBox or Windows BootCamp. It's allmost 2 times slower I can send you the test. I puted it on the same number of FB as test2.zip FB13253046 or FB13256895