Intel Fortran Compiler
Intel Fortran Compiler
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Intel Fortran Compiler

Intel Fortran Compiler, as part of Intel OneAPI HPC toolkit, is a group of Fortran compilers from Intel for Windows, macOS, and Linux.

The compilers generate code for IA-32 and Intel 64 processors and certain non-Intel but compatible processors, such as certain AMD processors. A specific release of the compiler (11.1) remains available for development of Linux-based applications for IA-64 (Itanium 2) processors. On Windows, it is known as Intel Visual Fortran. On macOS and Linux, it is known as Intel Fortran. In 2020 the existing compiler was renamed “Intel Fortran Compiler Classic” (ifort) and a new Intel Fortran Compiler for oneAPI (ifx) supporting GPU offload was introduced.

The 2021 release of the Classic compiler adds full Fortran support through the 2018 standard, full OpenMP* 4.5, and Initial Open MP 5.1 for CPU only. The 2021 beta compiler focuses on OpenMP for GPU Offload. When used with the Intel OneAPI HPC toolkit (see the "Description of Packaging" below) the compiler can also automatically generate Message Passing Interface calls for distributed memory multiprocessing from OpenMP directives.

For more information on Fortran standards, a number or resources are available, such as the Wikipedia Fortran entry or the Fortran wiki page.

The Intel Fortran package included the Intel Array Visualizer, a visualization tool for scientific formats such as FITS and netCDF, which can produce x-y plots, contour plots, and image plots, and save them to other formats.

Intel compilers are optimized for computer systems using processors that support Intel architectures. They are designed to minimize stalls and to produce code that executes in the fewest possible number of cycles. Intel Fortran Compilers support three separate high-level techniques for optimizing the compiled program: interprocedural optimization (IPO), profile-guided optimization (PGO), and other high-level optimizations (HLO).

Interprocedural optimization applies typical compiler optimizations (such as constant propagation) but uses a broader scope that may include multiple procedures, multiple files, or the entire program.

Regarding profile-guided optimization, the compiler generates a dataset of performance-related information from using the application with representative workloads, which it then analyzes to find which parts of the application are executed more and less frequently. The compiler uses these data to organize application execution to optimize performance based on how the application is actually used. This is in contrast to IPO which optimizes applications according to the logical flow of the application independent of workloads. The two can be combined to provide workload-based optimizations within which the logical-flow is optimized. Thus, all optimizations can benefit from profile-guided feedback because they are less reliant on heuristics when making compilation decisions.

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