Compiler optimization flags

Introduction

Compiler optimization flags control how aggressively a compiler transforms your source code into faster, smaller, or more specialized machine code. For HPC work this often has a larger effect on performance than any single code tweak you might make by hand. Understanding the basic optimization levels and a few key options is essential before you move on to more advanced performance work.

This chapter focuses on practical use of optimization flags in typical HPC compilers, what they do conceptually, and how to apply them safely.

Optimization levels: the core concept

Every major HPC compiler offers a hierarchy of general optimization levels. Although the exact names differ, they all follow a similar pattern. For GCC and LLVM (Clang), the common levels are -O0, -O1, -O2, -O3, and -Ofast. Intel compilers and others provide analogous levels such as -O2, -O3, and -Ofast or vendor specific variants.

At a very high level:

-O0 means no optimization. The compiler translates each statement almost literally. This is useful for debugging because the generated code closely matches your source, but performance is usually very poor. In HPC you normally avoid -O0 unless you are chasing a tricky bug.

-O1 enables basic optimizations that rarely change program behavior in subtle ways. The generated code is somewhat faster than -O0, but the compiler remains conservative. This level is sometimes useful when higher levels interfere with debugging, but you still want more realistic performance.

-O2 is the default for many HPC builds. It enables a wide range of optimizations, such as inlining of small functions, basic loop transformations, and some vectorization attempts. For many real applications, -O2 provides a good tradeoff between performance, compile time, and correctness assumptions.

-O3 adds more aggressive optimizations, especially on loops and inlining. The compiler may fully unroll loops, inline larger functions, and reorder computations when it believes it is safe to do so. This can yield additional speedups for numerically intensive kernels, but also increases compilation time and sometimes code size.

-Ofast goes further and tells the compiler to ignore some language standard rules and numerical edge cases in order to maximize performance. This usually implies very aggressive floating point optimizations. It can be very effective for well behaved numerical codes, but can break strict IEEE behavior or change results for ill conditioned problems.

Vendor compilers such as Intel oneAPI compilers often tune these levels for their own hardware. For example, Intel -O2 is already quite aggressive on Intel CPUs and is often recommended as the baseline.

Debug, release, and the role of optimizations

In HPC projects you will often see separate configurations such as "debug build" and "optimized build." The key difference is the combination of optimization and debugging flags.

A debug build assigns priority to ease of debugging and includes checks that can slow code down. It typically uses -O0 or at most -O1, and includes debug information such as -g. It may also enable extra runtime checks, for instance bounds checking in Fortran or sanitizers in C and C++. On an HPC system you use such builds on small test cases or on a development node, not for large production runs.

An optimized or release build turns on -O2 or higher, and usually disables extra checks that cost runtime. It still may include debug symbols if you want to profile or debug optimized code, but the generated code will be much more difficult to step through line by line.

It is important to understand that optimization can change the apparent order of operations, can inline functions, and can remove variables that appear unused. All of this can make debugging more challenging. A common workflow is to reproduce a bug with a less optimized build, then confirm if it is still present with higher optimization before you start detailed inspection.

Typical optimization flags in GCC and Clang

GCC and Clang provide a large family of options that refine the behavior of the basic -O levels. In HPC you will most often encounter flags related to vectorization, loop transformations, floating point behavior, and architecture tuning.

A typical set of flags for a CPU specific optimized build might look like:

gcc -O3 -march=native -ffast-math -funroll-loops code.c -o code

Each of these flags instructs the compiler to perform a specific category of transformation, on top of the selected optimization level.

-march=native tells the compiler to use instructions available on the current machine and to tune for its microarchitecture. On a cluster this makes sense only if the login node matches the compute nodes. Otherwise you might build with -march=skylake-avx512, -march=znver4, or similar, depending on the cluster documentation.

-funroll-loops allows the compiler to unroll loops more aggressively than it would by default, especially at -O3. Loop unrolling can reduce branch overhead and expose more opportunities for vectorization, but it can increase code size and instruction cache pressure.

-ffast-math relaxes some floating point rules and usually implies a group of sub options that allow faster but potentially less precise or less predictable arithmetic. This can include reordering operations, treating special values in simplified ways, and assuming associativity.

There are many more fine grained flags such as -fstrict-aliasing, -fno-exceptions, -fno-rtti, and others that can influence performance. In this introductory context it is enough to understand that -O levels select groups of optimizations, and additional flags refine their behavior to match the characteristics of your code and hardware.

Architecture specific tuning

In HPC, hardware specific optimization is particularly important. A cluster node may have particular vector instruction sets such as AVX2 or AVX-512, or may come from a specific CPU family such as Intel Skylake, AMD Zen, or ARM based designs. Compilers provide flags to tune for these architectures.

GCC and Clang use -march and -mtune. -march=<arch> selects which instructions are allowed in the generated code. -mtune=<arch> chooses how to schedule instructions and layout code for best performance on a given microarchitecture, but keeps the instruction set compatible with older hardware.

For instance, to generate code that uses AVX2 instructions and assumes an Intel Haswell like core, you might use:

gcc -O3 -march=haswell code.c -o code

Intel compilers provide similar concepts through flags such as -xHost or specific -xCORE-AVX2, -xCORE-AVX512, and so on. These instruct the compiler to use instructions optimized for a given Intel architecture. Vendor documentation usually lists recommended flags for the processors installed in their clusters.

Some clusters provide multiple executable variants for different instruction sets through build systems or environment modules. In that case, the optimization flags are responsible for enabling those variations, and the runtime environment chooses which one to execute.

In practice, it is important to ensure that use of architecture specific flags still allows your binary to run on all intended nodes. Building with -march=native on a login node that differs from the compute nodes can produce a binary that fails to run. Cluster documentation or support staff can usually tell you which architectures to target.

Floating point optimization flags

Many HPC applications are dominated by floating point operations, and compilers provide extensive controls for how aggressively they optimize numerical code. The key tension is between strict IEEE semantics and performance.

In GCC, the umbrella flag -ffast-math enables a set of options such as -fno-math-errno, -funsafe-math-optimizations, -fno-trapping-math, -ffinite-math-only, and -fno-signed-zeros. Each of these relaxes a specific aspect of floating point behavior. For example, allowing the compiler to assume that there are no NaN values can simplify and speed up arithmetic.

Intel oneAPI and other vendor compilers offer similar families of flags. For instance, Intel compilers support -fp-model with values such as precise, fast, or strict. A setting like -fp-model fast often corresponds conceptually to something like -ffast-math. It is common for vendor documentation to recommend a particular -fp-model for HPC workloads, often combined with -O2 or -O3.

A simple conceptual rule is that strict floating point models preserve properties such as:

$$
(a + b) + c \approx a + (b + c)
$$

only to the extent allowed by the language and IEEE rules. Fast models allow the compiler to assume associativity and to reorder operations more freely, which can change the rounding behavior and the treatment of edge cases. While each individual difference is small, they can accumulate in long running simulations.

Vectorization flags

Modern CPUs execute multiple floating point operations in a single instruction through SIMD capabilities. Compilers attempt to exploit this automatically during optimization. Vectorization flags control how aggressively they do so and under what assumptions.

GCC and Clang typically enable auto vectorization at -O3, and there are flags such as -ftree-vectorize, -fno-tree-vectorize, or -fvect-cost-model that influence this process. Intel compilers provide options like -qopt-zmm-usage, -qopt-report, and related settings that affect vector code generation.

In the simplest terms, vectorization requires that iterations of a loop are independent, or that any dependencies can be handled safely. The compiler analyzes the loop to detect such independence. Sometimes the compiler is too conservative and fails to vectorize a loop that you know is safe.

For this reason, compilers offer flags to generate vectorization reports. For example, with Intel compilers you can request detailed optimization reports and inspect whether a hot loop has been vectorized. GCC also has options to provide messages about vectorization decisions. These reports are essential when you start tuning performance critical loops in HPC codes, but they work on top of your chosen optimization level.

In combination with architecture flags such as -march=haswell, vectorization flags control both whether loops are converted into vector instructions and which vector width is used. If your code is not vectorized, you may fail to take advantage of SIMD hardware even at high -O levels.

Tradeoffs: compile time, code size, and performance

As optimization levels and flags become more aggressive, several tradeoffs become apparent.

Compile time increases. -O3 or -Ofast can take significantly longer to compile large applications compared to -O2 or -O1. On interactive development this may slow you down. Build systems sometimes use lower optimization levels for frequent incremental builds and reserve the highest levels for production builds.

Code size can increase. Loop unrolling, aggressive inlining, and vectorization can inflate the size of the binary. Very large binaries consume more instruction cache and may hurt performance in parts of the code that are not directly optimized.

Performance can be irregular. In some cases -O3 or -Ofast actually reduces performance compared to -O2, for example due to cache effects or over aggressive inlining. It is therefore important to measure performance with different flag combinations on realistic workloads rather than assuming that a higher level is always better.

Correctness risks grow. Fast math flags and similar aggressive options may change numerical behavior in unexpected ways, particularly for codes that rely on special values or that struggle with conditioning. This is why HPC practitioners often maintain a carefully tested baseline flag set and introduce more aggressive changes gradually.

In practice, an effective approach is to define a small set of named build configurations in your build system, for example "debug", "test", and "production", with progressively more aggressive optimization flags and correspondingly stronger validation requirements.

Practical combinations and testing strategy

Although every application is unique, a few patterns are common in HPC projects.

A conservative and general default might be:

gcc -O2 -g code.c -o code

This gives decent performance, preserves IEEE behavior by default, and includes debugging symbols. It is suitable for most development and smaller scale runs.

For performance evaluation on numerically robust kernels you might try:

gcc -O3 -march=native -funroll-loops code.c -o code

Here you accept some potential portability limitations in exchange for exploiting the full hardware capabilities. Before deploying such a binary on a cluster, you confirm that the compute nodes share the same architecture or you adjust -march to a specific target.

For highly performance critical codes on well understood numerical problems, a more aggressive configuration could be:

gcc -Ofast -march=native code.c -o code

or the analogous set of flags in vendor compilers. This configuration requires careful comparison of key outputs against a baseline built with stricter flags, and it may not be suitable for all problem classes.

A useful practice is to systematically benchmark your application with a small set of plausible configurations. For example, measure runtime and check numerical outputs for -O2, -O3, and -Ofast, and possibly add or remove fast math related options. This empirical testing often reveals surprising behavior and helps you pick a robust compromise between speed and reliability.

Summary

Compiler optimization flags are one of the most powerful tools for improving HPC application performance without changing source code. The core concept is the optimization level, such as -O2 or -O3, which selects broad sets of transformations. Additional flags refine this behavior, control architecture specific tuning, adjust floating point semantics, and influence vectorization.

Effective use of these flags requires awareness of the tradeoffs between performance, portability, compile time, code size, and numerical behavior. By combining a small number of well chosen flag sets with systematic testing and benchmarking, you can obtain large performance gains while maintaining confidence in your results.