I hear this a lot, but if it's with equivalent optimizations it's quite pathological and should be reported as a bug; it's certainly not generally the case. They are typically similar at the 10-20% level in my experience (not always in ifort's favour). There are several significant HPC codes which recommend GCC where it matters. You also need to consider reliability; GCC is surprisingly better in my experience -- I've quite often solved users' problems with "just try GCC".
There used to be a published set of results from Polyhedron -- presumably to help to sell proprietary compilers -- which had a geometric mean favouring ifort by ~20%, if I remember right. However, the last I saw didn't use the latest GCC, required the same flags for each case (which didn't include profile-directed, for instance), and the cases with the biggest differences actually bottlenecked on libraries, not generated code. gfortran is infinitely faster on most architectures, of course.
Most benchmark results I see aren't useful because they don't supply the parameters needed to be reproducible and they don't have profile information to allow you to understand, and maybe improve, them.
I found that results from the Polyhedron benchmarks are online again <https://www.fortran.uk/fortran-compiler-comparisons/polyhedr.... Note that they compare gfortran from 2015 and ifort from 2017. I don't know how representative they are of the part of the workload on typical HPC systems for which the compiler might be important.
There used to be a published set of results from Polyhedron -- presumably to help to sell proprietary compilers -- which had a geometric mean favouring ifort by ~20%, if I remember right. However, the last I saw didn't use the latest GCC, required the same flags for each case (which didn't include profile-directed, for instance), and the cases with the biggest differences actually bottlenecked on libraries, not generated code. gfortran is infinitely faster on most architectures, of course.
Most benchmark results I see aren't useful because they don't supply the parameters needed to be reproducible and they don't have profile information to allow you to understand, and maybe improve, them.