11.4 Optimized builds

Overview

An optimized build is a version of your program that has been compiled with performance in mind. The compiler is instructed to spend effort analyzing and transforming your code so that the resulting executable runs faster, uses fewer resources, or both. In HPC this is the kind of build you normally run on the cluster for production simulations and large experiments, in contrast to debug builds that prioritize ease of debugging.

This chapter focuses on what distinguishes an optimized build from a debug build, which options matter most in practice, and how to balance optimization against correctness and debuggability on typical HPC systems.

Goals of an optimized build

The main purpose of an optimized build is to improve performance without changing the numerical results beyond acceptable limits. In HPC workflows this usually means:

Improving time to solution. The same scientific or engineering task should complete faster, or allow you to run bigger problems in the same wall time.

Better use of hardware. Optimized builds aim to exploit vector units, multiple cores, fast cache, and specialized instructions more effectively.

Reducing resource cost. Faster jobs often use fewer CPU hours and energy for the same task, which can matter both for queue limits and sustainability.

An optimized build does not guarantee that performance will be good. It simply enables the compiler to apply transformations that would be unsafe or inconvenient when debugging. You still need algorithmic efficiency and good parallelization, which are covered elsewhere in the course.

Key differences from debug builds

Debug builds, covered in their own chapter, enable features that help you find bugs. Optimized builds reverse many of those choices.

A typical debug build might:

Use no or low optimization, such as -O0 or -O1.

Include full debug information, such as -g.

Enable runtime checks and assertions, such as bounds checking or sanitizers.

Disable inlining or other transformations that confuse debuggers.

An optimized build usually:

Uses higher optimization levels, such as -O2 or -O3.

Often disables heavy runtime checking and sanitizers.

May omit some debug information or keep it but accept that debugging is harder.

Allows aggressive inlining and code motion that change the apparent structure of the code.

You can sometimes keep a mixture, for example an optimized build that still contains debug symbols for basic profiling and post-mortem analysis.

Typical optimization levels

All major HPC compilers provide a set of optimization levels that are selected by simple flags. The exact transformations differ between compilers, but some general patterns are common. Here we describe the idea, not implementation details of specific compilers, which are covered elsewhere.

-O0 turns off optimizations. The compiler focuses on translating each line of code more literally, which makes debugging easier but performance poor.

-O1 performs a basic set of safe optimizations such as simple inlining, constant folding, and elimination of dead code. It offers a modest speedup with relatively little compilation overhead.

-O2 is often the default for optimized builds. It enables a larger set of optimizations that are still considered reasonably safe and stable. For many HPC codes, -O2 gives a good balance of performance and compilation time without increasing the risk of incorrect transformations too much.

-O3 adds more aggressive optimizations, such as more inlining, more loop transformations, and sometimes more speculative vectorization. It can improve performance significantly for compute intensive kernels, but may also increase build time and sometimes code size.

Many vendors introduce additional levels such as -Ofast or provide fine grained flags for math and vectorization. These often relax numerical guarantees in order to speed up floating point operations.

Optimization and numerical accuracy

Floating point arithmetic is not mathematically exact. Reordering operations can change results slightly, especially for large sums or poorly conditioned problems. High optimization levels and relaxed math flags allow the compiler to reorder arithmetic more freely, vectorize reductions more aggressively, or use hardware specific approximations.

For example, an associative reordering of a sum

$$
s = \sum_{i=1}^n a_i
$$

is not guaranteed to produce bit identical results in floating point arithmetic, even though the mathematical sum is well defined. An optimized build might compute

$$
s = \left(\sum_{i=1}^{n/2} a_i\right) + \left(\sum_{i=n/2+1}^n a_i\right)
$$

in parallel, while an unoptimized build follows the original sequential order.

In practice you should:

Establish a reference build. Use a conservative configuration, for example -O2 without aggressive math flags, and store known good outputs for representative test cases.

Define acceptable tolerances. For floating point outputs, compare with relative or absolute tolerances, for example $|x_{\text{opt}} - x_{\text{ref}}| \leq \epsilon (1 + |x_{\text{ref}}|)$.

Test all optimized configurations. After changing optimization settings, run tests and compare to the reference. If differences exceed your tolerance, reconsider the flags or investigate potential bugs.

Balancing optimization and debuggability

Fully optimized builds can be hard to debug. The compiler may inline functions, remove variables, reorder instructions, and even eliminate entire code paths that it proves unnecessary. When a crash occurs, the call stack may be difficult to interpret and stepping through code can be confusing.

In HPC practice, a common compromise for production binaries is:

Compile with -g to keep debug information, even for optimized builds.

Use -O2 or -O3 depending on testing and performance.

Disable only the heaviest runtime checks that change behavior or cost too much in production.

This configuration allows tools such as profilers and some debuggers to work reasonably well, although single stepping and inspecting all variables is not always possible.

For very difficult bugs that appear only in optimized builds, you may also:

Reduce optimization for selected files or functions. For example, compile everything at -O3 except one suspicious file at -O0 or -O1.

Use compiler flags that limit specific transformations, such as disabling vectorization in a problematic routine.

Insert extra logging or assertions temporarily, then rebuild with the same optimization level.

This selective approach lets you keep most performance while making a small region easier to inspect.

Architecture specific optimization

On HPC clusters, optimized builds often target specific CPU features such as particular vector instruction sets. The compiler can generate code that uses these instructions, for example AVX2 or AVX-512, if you tell it which architecture you are aiming for.

Common patterns include:

Using a generic target for portability. This produces code that can run on different CPUs at some performance cost.

Using an architecture flag that matches the cluster nodes where the code will run. This can significantly improve performance by enabling wider vector instructions or specialized math operations.

Sometimes teams maintain multiple optimized builds: one generic, one tuned for a particular partition. Build systems such as Make or CMake can help manage these variants.

When you use architecture specific flags in optimized builds:

Make sure you know which nodes your binary will run on. A binary tuned for one microarchitecture may not run at all on an older one.

Document the compiler version and flags used for the build. This is essential for reproducibility and for interpreting performance results.

Test each architecture specific build independently. Different instruction sets can expose different bugs.

Optimization, vectorization, and parallelism

Optimized builds are tightly connected to vectorization and parallel execution, both of which are core to HPC. The compiler can automatically vectorize loops, unroll them, or change iteration orders. It can also improve the performance of explicit parallel constructs from OpenMP or other models.

For automatic vectorization, optimized builds:

Enable analyses that detect loops which are safe to vectorize.

Apply transformations such as loop interchange or alignment hints to help vector units.

May require additional hints from you in the form of pragmas or simple code restructuring.

For explicit parallelism, optimized builds:

Influence performance of thread creation and synchronization.

Improve efficiency of computational kernels inside parallel regions.

Can expose data races or undefined behavior more readily, because optimized code may reorder memory operations within the rules of the memory model.

The correctness of parallel code should never depend on the optimization level. If a race or other undefined behavior appears only at high optimization, it usually indicates a latent bug, not a compiler error.

Building optimized executables on clusters

On HPC systems, optimized builds are often created inside batch jobs or login nodes with limited runtime policies. The process itself is straightforward but benefits from discipline.

A typical pattern is:

Load the appropriate compiler and libraries using the environment module system.

Configure your build system to select the optimized configuration, for example a Release or RelWithDebInfo build type.

Use consistent flags for all compilation units. Mixing object files compiled with very different optimization assumptions can lead to confusing performance behavior.

Record build logs, including exact compiler invocations and versions. This makes it possible to reproduce a particular build months later.

In cluster environments, large projects may separate the build step from the run step, sometimes building on specialized compile nodes. Optimized builds often take longer to compile than debug builds, especially at very high optimization levels or for large templates in C++. This compilation cost usually pays for itself as soon as you run substantial production workloads.

Testing and validating optimized builds

Every change of optimization settings should be treated as a significant change to your software. In HPC, where long runs and large datasets are common, catching problems early saves enormous time.

Basic practices include:

Maintain an automated test suite that runs quickly for small input cases. After building an optimized executable, run this suite before submitting large jobs.

Compare key outputs against references. Focus on scientifically meaningful quantities, not just raw bytes, and use appropriate tolerances for floating point data.

Monitor runtime behavior. Optimized builds can change memory usage patterns and timings, sometimes exposing scaling issues or race conditions previously hidden.

When a new compiler version arrives on the cluster, repeat tests with your optimized configuration. Compiler updates can alter optimization behavior even with the same flags.

Summary

Optimized builds are the standard for running serious workloads on HPC systems. They differ from debug builds by enabling higher optimization levels, more aggressive transformations, and usually fewer runtime checks. This delivers better performance and resource efficiency, but at the cost of more complex debugging and a greater need for careful testing.

For practical work you should:

Use -O2 as a baseline for optimized builds.

Consider -O3 or more aggressive options only after measuring performance and verifying correctness.

Keep debug information in optimized builds where possible to help profiling and limited debugging.

Tailor architecture specific flags to the hardware you actually use on the cluster.

Systematically test and validate any new optimized build before trusting it for large jobs.

With these practices, optimized builds become a reliable tool for extracting high performance from HPC systems without compromising scientific credibility.