11.1 Common HPC compilers

Overview of Common HPC Compilers

High performance computing relies heavily on compiled languages such as C, C++, and Fortran. On most clusters you will find several compiler families installed side by side. Each has different strengths, defaults, and extensions, and choosing between them can affect performance, portability, and sometimes even numerical results.

This chapter introduces the main compiler families you will encounter in HPC environments, explains their typical roles, and highlights what is distinctive about each. Details of optimization flags, build types, and build systems are covered in later chapters, so here the focus is on the compilers themselves and how they fit into the HPC landscape.

The Role of Compiler Families in HPC

On an HPC system, a "compiler" is usually part of a toolchain that also includes linkers, system libraries, MPI libraries, math libraries, and possibly vendor-tuned runtime libraries. Clusters often provide multiple toolchains that are combinations of:

GCC based toolchains, which are open source and highly portable.

Vendor compilers such as Intel or NVIDIA compilers, which target specific hardware features and provide tuned math and communication libraries.

LLVM based toolchains, which are becoming more prominent through projects such as Clang, Flang, and AOCC.

You typically select a compiler family with an environment module or a similar mechanism, for example module load gcc or module load intel. Once you choose a family you usually use its C, C++, and Fortran front ends consistently, for instance gcc, g++, and gfortran together.

GCC in HPC Context

GCC, the GNU Compiler Collection, is the default compiler on many Linux systems and appears on nearly every HPC cluster. GCC is fully open source and supports a wide range of architectures, from laptops to supercomputers.

In HPC, GCC is valued for portability and standard conformance. It often serves as the reference compiler for making sure code is legal C, C++, or Fortran. Many scientific codes and libraries are regularly tested with GCC, which makes it a safe baseline choice when first building an application on a new machine.

GCC offers separate front ends for different languages. The typical commands you will see in HPC documentation are:

gcc for C.

g++ for C++.

gfortran for Fortran.

Because GCC is widely used, most examples you find online for compiling and linking C, C++, and Fortran code will use GCC commands and options. On clusters, GCC is often available in several versions. Older versions might be kept for compatibility with legacy codes, while newer versions may support more recent language standards and improved optimization.

GCC also includes support for parallel programming models that are common in HPC. For example, it provides OpenMP support in all three major languages, and it supports various SIMD and vectorization options that let you hint to the compiler how to use the CPU’s vector units. GPU offloading support has also been added in recent GCC releases through OpenMP and other constructs, although the maturity and performance may depend on the version and target device.

Even when a system has strong vendor compilers, it is common to build at least one version of a code with GCC. This often helps discover nonstandard language usage because different compilers interpret extensions in different ways. GCC also tends to be the compiler used for building many open source numerical libraries that then become part of the cluster software stack.

Intel oneAPI Compilers in HPC

Intel compilers have a long history in HPC because many large systems use Intel CPUs. Today Intel distributes its compilers as part of the oneAPI toolkit. On clusters these compilers are often available as icc or icx for C, icpc or icpx for C++, and ifort or ifx for Fortran, depending on the generation of the toolchain.

Intel compilers are tightly tuned for Intel architectures. They include optimizations that exploit Intel specific vector instruction sets such as AVX2 and AVX-512, and they are often able to apply aggressive loop transformations that benefit numerical kernels like matrix multiplies or stencil operations. They also integrate with Intel’s performance libraries, such as the Intel Math Kernel Library, which provide highly optimized implementations of BLAS, FFTs, and other numerical primitives.

In practice, you may find that performance sensitive codes run faster when compiled with Intel compilers on Intel based systems, especially if the code uses vectorizable loops and has been written with attention to data layout. However, performance is very code dependent, and recent versions of GCC and LLVM have reduced this gap in many workloads.

Intel compilers also provide robust support for OpenMP, including newer features for tasking and SIMD directives. Some versions offer extensions for fine control of vectorization and memory alignment. In addition, Intel debuggers and profilers can provide compiler integrated diagnostics, such as vectorization reports that explain which loops were vectorized and why.

The introduction of the oneAPI toolchain also brought new front ends based on LLVM, such as icx and ifx. These aim to combine Intel’s optimization expertise with LLVM’s infrastructure. On a cluster, you may see both "classic" Intel compilers and the newer oneAPI compilers, so it is important to read the local documentation to understand naming and recommended versions.

LLVM and Clang based Compilers in HPC

LLVM is a modular compiler infrastructure that has become increasingly important in HPC. Clang is the C and C++ front end built on LLVM, and there are several Fortran front ends in development, such as Flang. Many vendor compilers now use LLVM internally, and some HPC centers expose LLVM based toolchains directly.

In an HPC environment, LLVM based compilers are interesting for several reasons. They often provide fast compilation, good diagnostics, and support for recent language standards. Clang is known for clear error messages, which is helpful when developing complex templates in C++ or porting legacy codes. It usually supports language features early, which can matter if your code targets modern C++ or Fortran standards.

Several vendors build their own HPC compilers on top of LLVM. For example:

Some ARM based HPC systems provide ARM optimized LLVM toolchains that tune for ARM specific vector instructions.

AMD’s AOCC toolchain is based on LLVM and adds optimizations for AMD CPUs.

NVIDIA uses LLVM components within its toolchains for CUDA and HPC compilers.

Because of this, learning basic LLVM and Clang usage can translate across hardware platforms. The typical commands for Clang are clang for C and clang++ for C++. For Fortran, the exact name depends on which front end is installed, and support may be more experimental than for C and C++.

LLVM also supports parallel programming models that are important in HPC. Clang includes OpenMP support and can generate code for multiple targets, including some GPU back ends. This makes LLVM a foundation for heterogeneous programming models, where parts of the code run on CPUs and other parts on accelerators.

In practice, on a given cluster you may see an LLVM based toolchain as an alternative to GCC or as the backend inside a vendor compiler. Understanding that multiple compilers might share the LLVM core helps explain why flags and behaviors can sometimes look similar even when the compilers carry different names.

Comparing Compiler Families in HPC Practice

Since multiple compiler families often coexist on a cluster, you must decide which one to use for a given project. That choice usually depends on a mix of portability, performance, and ecosystem considerations.

If you want maximal portability and broad community testing, GCC is often the first choice. Many open source packages assume GCC and provide build recipes that use it by default. When you need to get a code running quickly on a new system, GCC is frequently the simplest path.

If you target specific vendor hardware and care about squeezing out extra performance, vendor compilers such as Intel oneAPI on Intel CPUs or vendor tuned LLVM variants on ARM or AMD platforms may be more attractive. They may also integrate more closely with vendor math libraries and profilers, which makes performance tuning workflows smoother.

If your focus is on modern language features, sophisticated diagnostics, or heterogeneous programming models that rely on LLVM’s intermediate representation, then Clang and other LLVM based compilers become particularly relevant.

Many HPC practitioners do not fix a single compiler forever. Instead they:

Develop and debug initially with a compiler that has friendlier diagnostics.

Test for portability and correctness with GCC.

Benchmark and tune with compiler families that give the best performance on the target hardware.

This "multi compiler" approach can uncover bugs that only appear with certain optimization strategies and can help you find a good balance between stability and speed.

Practical Aspects of Using HPC Compilers

Although the exact commands and flags are covered in other chapters, some practical points about common HPC compilers are worth mentioning here.

On clusters, compilers are usually not invoked directly through their full path. Instead you load a module, which sets environment variables such as CC, CXX, and FC to point to the chosen compiler family. For example, loading a gcc module might set CC=gcc, while loading an intel module might set CC=icc or CC=icx. Build systems such as Make and CMake then query these variables to select the compiler automatically.

Compilers in HPC are often built with support for MPI compiler wrappers. These are commands such as mpicc, mpicxx, and mpif90 that call the underlying compiler with the MPI headers and libraries automatically included. The MPI wrappers are usually tied to a specific compiler family, so you must keep the MPI module consistent with the compiler module you load.

Because HPC codes can be very large, compile time and memory usage during compilation can be significant. Different compilers may have different trade offs here. For instance, Clang is often fast at compiling complex C++ templates, while some vendor compilers may spend more time in optimization passes to achieve better runtime performance. On a shared login node, very heavy compilation can impact other users, so some centers encourage or require large builds to be done as batch jobs on dedicated build nodes.

Another practical aspect is language standard support. Some HPC applications are written in older Fortran standards or in C with nonstandard extensions, while others adopt recent C++ or Fortran features. Compiler families differ in how quickly and how completely they implement new standards. On a production cluster, system administrators typically provide at least one "stable" combination of compiler and MPI suitable for long term runs, and one or more "experimental" combinations with newer language features.

Finally, many numerical and communication libraries on a cluster are built separately for each compiler family. For example, you might see BLAS or LAPACK modules that specify both the library and the compiler, such as intel-mkl for use with Intel compilers or openblas-gcc for use with GCC. Mixing objects from different compiler families can cause subtle ABI problems, so you should always follow the library documentation to pair each library with its intended compiler.

Summary

Common HPC compilers fall into a few major families: GCC, vendor specific compilers such as Intel oneAPI, and LLVM based compilers such as Clang and vendor tuned variants. Each family offers different trade offs in portability, performance, diagnostics, and hardware support. On modern clusters, you will usually have access to several of them, and effective HPC practice involves understanding how to select, combine, and compare these compilers within a consistent toolchain.

The next chapters focus on how to control these compilers through optimization flags, how to organize builds with tools such as Make and CMake, and how to use profiling and benchmarking to evaluate the performance impact of your compiler choices.