LLVM

Role of LLVM in HPC

LLVM is both a compiler infrastructure and a family of compilers that are widely used in HPC, often alongside GCC and vendor compilers. In an HPC context, LLVM typically appears through the clang C and C++ front ends, flang for Fortran, and as the backend technology inside other compilers, including some vendor toolchains and GPU toolchains.

What makes LLVM interesting for HPC users is its modular design and its focus on modern language features and aggressive optimization. Many clusters now provide clang as a standard module, and some performance critical codes are compiled with LLVM based compilers either for their diagnostics, for specific architectures, or for certain language features such as modern C++.

This chapter focuses on practical use of LLVM based compilers on HPC systems, and what is specific to them compared to other compilers.

LLVM toolchain components relevant to HPC users

From an HPC user perspective, the most visible parts of LLVM are the command line compilers and a small set of tools that help with performance and diagnostics.

clang is the C, C++, and Objective C compiler front end in the LLVM ecosystem. On clusters it is usually available as clang for C and clang++ for C++. It accepts many GCC style options but also has its own extensions and diagnostics. For OpenMP offload and GPU targets, clang is often the active part of the toolchain that produces both host and device code.

flang is the emerging LLVM based Fortran compiler. Some systems ship an older, non-LLVM flang, while others ship the newer LLVM Flang. From a user perspective, flang aims to be compatible with Fortran standards and to support OpenMP, OpenACC, and modern Fortran features. Its availability and maturity differ between systems, so you will often see it side by side with other Fortran compilers.

lld is the LLVM linker. On many Linux HPC systems, linking still goes through the system linker, but in some environments you may see lld used for faster linking times, which matters for very large applications with many object files and libraries.

llvm-ar, llvm-ranlib, and related tools are LLVM versions of common binary tools. On most clusters these are used internally by the compiler and you rarely call them directly unless you manage your own libraries at a low level.

LLVM provides a rich set of analysis and instrumentation tools, many of which are exposed through clang command line options. These tools are especially useful in debugging, profiling, and optimizing code, and they integrate with the general performance analysis techniques covered elsewhere.

Using clang on HPC clusters

On a typical HPC cluster, LLVM based compilers are provided through environment modules. You might see modules such as llvm, clang, llvm/15, or vendor toolchains that internally use LLVM. Once you load the module, you can use clang similarly to gcc for C and clang++ for C++.

A basic compilation with clang looks like:

clang -O2 -march=native -o myprog myprog.c

For C++:

clang++ -O3 -std=c++20 -o mycode mycode.cpp

Most GCC compatible flags for language selection, include paths, library paths, and linking are accepted. Existing build systems that are written with GCC in mind often work with clang with little or no modification.

You may find that clang is stricter about the language standard and diagnostics. Some code that compiles with GCC by default may need minor adjustments or explicit standard flags when compiled with clang. This strictness can help catch undefined behavior and portability issues that impact numerical correctness and scaling on large systems.

In many MPI environments, wrapper scripts like mpicc can be configured to use clang as the underlying compiler instead of gcc or vendor compilers. If you want MPI to use clang, you might have to load a specific MPI module or reconfigure your build so that CC=mpicc and mpicc itself uses clang internally.

Optimization options specific to LLVM

LLVM based compilers offer optimization levels and architecture tuning flags similar to other compilers, but their exact behavior and default passes are specific to LLVM.

Standard optimization levels are:

$O0$ means no optimization, focus on fast compilation and ease of debugging.

$O1$ is basic optimization with limited compile time overhead.

$O2$ is the default high optimization level that balances compile time and runtime performance.

$O3$ enables more aggressive optimizations which can increase compile times and sometimes code size, but can also provide higher performance for compute intensive kernels.

-Ofast enables -O3 and additionally relaxes strict compliance with language and IEEE floating point rules. It can enable transformations that assume certain algebraic properties, such as reassociation of floating point operations, which might change numerical results.

You select the target microarchitecture with -march and -mtune. For generic code you might see:

clang -O3 -march=x86-64 -mtune=native ...

For code that is meant to fully exploit the local node architecture, users often use:

clang -O3 -march=native ...

This instructs the compiler to generate instructions using the full capabilities of the host CPU. Such binaries may not run on older CPUs, so this is best used when the target nodes are homogeneous and well known.

LLVM has its own set of fine grained optimization switches and pass controls, but for typical HPC users the combination of optimization level, architecture flags, and vectorization or OpenMP flags is sufficient. HPC specific flags and behaviors can differ between compiler versions, so it is common practice to examine release notes and cluster documentation to find recommended flag sets for a particular system.

Vectorization and SIMD with LLVM

Vectorization is crucial to performance on modern CPUs, and LLVM's vectorizer is one of its central components. When you compile with clang -O2 or higher, LLVM automatically tries to vectorize loops and straight line code where it can prove that this is safe and beneficial.

To produce vector instructions appropriate for your CPU, you should specify a suitable -march flag. For example on an x86 system:

clang -O3 -march=skylake-avx512 mykernel.c

will enable AVX512 vector instructions if your CPU supports them. Without a suitable -march, the compiler may restrict itself to a more conservative instruction set to preserve compatibility.

You can give hints to the LLVM vectorizer through pragmas that are compatible with OpenMP or other directive based models. For example:

#pragma clang loop vectorize(enable)
for (int i = 0; i < N; ++i) {
    a[i] = b[i] * c[i];
}

In some cases, especially in numerical HPC codes, LLVM may refuse to vectorize loops where it cannot prove the absence of pointer aliasing or out of bounds accesses. You can often assist the compiler with language features like restrict in C, or by writing code in a way that clarifies memory access patterns.

LLVM provides reports that help you understand what loops were vectorized and why some were not. With clang, you can enable optimization remarks:

clang -O3 -Rpass=loop-vectorize -Rpass-missed=loop-vectorize mycode.c

This prints information about successful and missed vectorization opportunities, which is especially useful when tuning inner loops of computational kernels.

Diagnostics, sanitizers, and correctness tools

LLVM based compilers are known for detailed diagnostics and a powerful suite of runtime checkers called sanitizers. These tools are particularly useful in HPC development phases where correctness and robustness must be established before scaling up.

With clang, you can enable various sanitizers at compile and link time. Some of the most relevant are:

AddressSanitizer, enabled with -fsanitize=address, detects many memory related bugs such as out of bounds accesses, use after free, and memory leaks.

UndefinedBehaviorSanitizer, enabled with -fsanitize=undefined, checks for operations with undefined behavior like signed integer overflow or invalid casts.

ThreadSanitizer, enabled with -fsanitize=thread, detects data races in multithreaded code.

A typical sanitized build might use:

clang -g -O1 -fsanitize=address,undefined -fno-omit-frame-pointer \
      -o myprog_asan myprog.c

On HPC systems, sanitizers can have significant runtime overhead and may require substantial memory. They are therefore usually used on smaller test inputs and in development environments rather than on large production runs.

LLVM also offers static analysis through clang subcommands or separate tools such as clang-tidy. These can detect common mistakes, dangerous patterns, and performance issues at compile time. Some clusters provide these tools directly, while in others you might install them locally or use container based environments.

Because these diagnostic tools are closely integrated with the compiler, they benefit from an understanding of the actual transformations and optimizations applied, which increases the reliability of their warnings about potential bugs in performance critical numerical kernels.

LLVM in GPU and accelerator toolchains

LLVM is a key technology in many GPU and accelerator toolchains. Several vendors build their compilers on top of LLVM backends to support architectures such as AMD GPUs, NVIDIA GPUs, and emerging accelerators.

On some clusters, you may use clang directly to target GPUs with OpenMP offloading. A simplified example could look like:

clang -O3 -fopenmp -fopenmp-targets=nvptx64-nvidia-cuda \
      -Xopenmp-target=nvptx64-nvidia-cuda -march=sm_80 \
      myoffload.c -o myoffload

or similar options targeting AMD GPUs. The exact flags depend on the cluster configuration and the compiler version, so you should consult local documentation. In these setups, clang compiles both host code and device code, and the LLVM backend produces the GPU ISA or intermediate representation.

Vendor toolchains like AMD's ROCm and some oneAPI components also rely on LLVM internally. From the user perspective you may not interact directly with LLVM, but understanding that the toolchain is LLVM based can make it easier to interpret optimization reports, error messages, and available flags, especially when they reuse clang conventions.

In hybrid CPU GPU applications that combine MPI, OpenMP, and accelerator directives, LLVM based compilers are often one of the few technologies that support the full stack, including C++ templates and advanced language features on both host and device.

Using LLVM in large HPC codebases and build systems

In large HPC projects that use Make, CMake, or other build systems, switching to LLVM based compilers is usually controlled by environment variables or configuration options.

With Make based builds, you often select LLVM by setting variables:

make CC=clang CXX=clang++

or by editing a configuration file that sets CC and CXX. For Fortran, when available:

make FC=flang

With CMake, you can choose LLVM at configure time:

cmake -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ \
      -DCMAKE_Fortran_COMPILER=flang ..

Once selected, CMake generates build files that use these compilers consistently for all targets.

Because LLVM is compatible with many GCC style flags, existing build scripts often work without changes. However, there are differences in default standard versions, warning sets, and extension handling. It is common practice to:

Compile with -Wall -Wextra or similar, to expose potential issues.

Gradually fix or silence warnings that indicate undefined or non portable behavior.

Use explicit -std=c11, -std=c++17, or -std=c++20 to avoid relying on defaults.

When combining LLVM with external numerical libraries such as BLAS or LAPACK, you usually link the same way you do with GCC, for example:

clang -O3 mycode.c -lblas -llapack -lm -o mycode

The main requirement is that the application binary interface for the libraries matches your compiler and target platform. On most Linux HPC clusters, using LLVM based compilers with system or vendor provided math libraries works without special steps.

Portability and mixed compiler environments

It is common to find clusters where different compiler families coexist, and large codes may be compiled with a mixture of compilers, for example vendor Fortran compilers combined with LLVM based C++ compilers. This is possible, but requires attention to application binary interfaces, name mangling, and Fortran C interoperability.

LLVM itself aims at strong cross platform portability. If code compiles cleanly with clang on one system, it often builds on others with minimal changes. This portability is useful when you develop on a local machine that has LLVM installed and then run on a cluster that also offers LLVM, or when you prepare portable build configurations that can switch between compilers through simple options.

One particular strength of LLVM in this context is its adherence to language standards and its detailed diagnostics. By using clang in continuous integration pipelines or local development, you can detect non standard constructs and potential undefined behavior early, before they cause errors on large scale runs with different compilers or architectures.

Finally, because LLVM is continuously evolving, its behavior, optimizations, and supported features can change between versions. On HPC systems, multiple LLVM versions can be provided side by side through modules. When you care about reproducibility and long term support, you should record not only your compiler flags but also the exact LLVM version and module configuration that was used to build your production binaries.