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Diagnostic 15521: loop was not vectorized: explicitly compute the iteration count before executing the loop.

Product Version: Intel® Fortran Compiler 15.0 and above

Criado por Devorah H. (Intel) Última atualização em 30/05/2018 - 07:40
Article

Diagnostic 15523: Loop was not vectorized: cannot compute loop iteration count before executing the loop.

Product Version: Intel(R) Visual Fortran Compiler XE 15.0 or a later version

Criado por Devorah H. (Intel) Última atualização em 25/05/2018 - 15:30
Article

Diagnostic 15522: Loop was not vectorized: loop control flow is too complex.

Product Version: Intel® Fortran Compiler 15.0 and above

Criado por Devorah H. (Intel) Última atualização em 25/05/2018 - 15:30
Article

Diagnostic 15524: Loop was not vectorized: search loop cannot be vectorized unless all memory references can be aligned vector load.

Product Version: Intel(R) Visual Fortran Compiler XE 15.0 or a later version

Criado por Devorah H. (Intel) Última atualização em 25/05/2018 - 15:30
Article

Diagnostic 15527: loop was not vectorized: function call to xxx cannot be vectorized

Product Version: Intel(R) Visual Fortran Compiler XE 15.0 or a later version

Criado por Devorah H. (Intel) Última atualização em 25/05/2018 - 15:30
Article

Diagnostic 15532: Loop was not vectorized: compile time constraints prevent loop optimization

Product Version: Intel(R) Visual Fortran Compiler XE 15.0 or a later version

Criado por Devorah H. (Intel) Última atualização em 05/07/2019 - 14:23
Article

Diagnostic 15537: Loop was not vectorized: implied FP exception model prevents usage of SVML library.

Product Version: Intel® Visual Fortran Compiler XE 15.0 or a later version

Criado por Devorah H. (Intel) Última atualização em 25/05/2018 - 15:30
Article

Diagnostic 15541: Loop was not vectorized: outer loop was not auto-vectorized: consider using SIMD directive.

Product Version: Intel® Visual Fortran Compiler XE 15.0 or a later version

Criado por Devorah H. (Intel) Última atualização em 25/05/2018 - 15:30
Mensagem de blog

New optimizations for X86 in upcoming GCC 5.0

 

Criado por Evgeny Stupachenko (Intel) Última atualização em 24/01/2018 - 12:12
Article

Fast Gathering-based SpMxV for Linear Feature Extraction

This algorithm can be used to improve sparse matrix-vector and matrix-matrix multiplication in any numerical computation. As we know, there are lots of applications involving semi-sparse matrix computation in High Performance Computing. Additionally, in popular perceptual computing low-level engines, especially speech and facial recognition, semi-sparse matrices are found to be very common....
Criado por Última atualização em 12/12/2018 - 18:00