Debug builds

Understanding Debug Builds in HPC

Debug builds are special configurations of your program that trade raw performance for better visibility into what the code is doing. In high performance computing this tradeoff is essential, because subtle bugs in parallel and optimized code can be extremely difficult to detect and reproduce. A debug build is what you run when you are still developing, verifying correctness, or tracking down strange behavior, not what you use for production performance runs.

This chapter focuses on what makes a build a "debug build," how to create such builds with common HPC compilers and build systems, and how debug builds interact with performance, optimization, and parallelism.

Goals and Characteristics of a Debug Build

The primary goal of a debug build is to make it easier to understand, inspect, and control the behavior of your program at runtime. For this purpose, debug builds usually have several characteristic features.

They include detailed debug information that debuggers can use. This information maps machine instructions back to source files, line numbers, functions, variables, and types. When you stop a program inside a debugger, you can see which line of source code is executing, inspect variable values, and step through the program in a meaningful way.

They disable or significantly reduce optimizations. Compiler optimizations can reorder instructions, inline functions, eliminate variables, and in general transform the code so that its structure no longer matches the original source. While this is good for speed, it can make debugging extremely confusing. A debug build usually turns most of these optimizations off so that the generated code remains close to what you wrote.

They include extra runtime checks. Many compilers and libraries can add internal checks in debug mode. These might catch array bounds violations, uninitialized variables, invalid pointer usage, or incorrect API calls at runtime and report them clearly. This increases the chance that bugs are detected immediately rather than silently corrupting data.

They often include extra logging and assertions. Application code itself often enables more diagnostics in debug mode. The code may print more verbose messages or enable assert statements that validate assumptions. In an optimized release build you might disable much of this to avoid overhead.

A debug build is therefore not just "a slower build." It is a build that is intentionally configured to maximize insight and error detection, even if that makes it unsuitable for large production runs on an HPC cluster.

Debug Symbols and Compiler Flags

Debug symbols link the binary back to your source code. Without them, a debugger can usually only show you raw addresses or assembly instructions, which are extremely hard to interpret.

For C, C++, and Fortran, most HPC compilers share similar flags for generating debug information.

With GCC and LLVM clang, the standard flag is -g. You compile as:

mpicc -g -O0 -Wall -o mycode_debug mycode.c

The flag -g tells the compiler to embed debug symbols. The program may run slightly larger in memory and binaries may be bigger, but debugging becomes possible.

With Intel oneAPI compilers, the same -g flag is commonly used:

icx -g -O0 -o mycode_debug mycode.c
ifx -g -O0 -o mycode_debug mycode.f90

The exact format and level of debug information can be tuned. For example, GCC supports -g, -g1, -g2, -g3, with higher levels including more detailed information such as macro definitions. Some systems also use -ggdb for debugging with gdb. In day to day HPC work, plain -g is usually adequate.

Debug symbols are especially important for parallel programs. When multiple MPI processes or many threads run concurrently, being able to attach a debugger and see correct call stacks and variable names is critical. Without debug symbols, parallel debugging tools cannot show meaningful high level information.

Disabling or Reducing Optimization

Optimization levels control how aggressively the compiler transforms your code. Typical flags are:

• -O0 for no optimization,
• -O1, -O2, -O3 for increasing levels of optimization.

For a pure debug build, you usually choose -O0. This choice keeps the compiled code structure close to the source, enabling predictable stepping in a debugger and making variable values easier to understand.

If you compile with -O2 or -O3 and also use -g, you still get debug symbols, but your debugging experience may be confusing. Variables can appear to change unexpectedly (due to reordering), or some variables cannot be inspected at all because the compiler removed them. Function calls might be inlined so that they do not appear as separate frames in a call stack.

Sometimes, however, you need to debug issues that only show up when optimizations are enabled, for example, race conditions or subtle undefined behavior. In that case, you may build with both -g and a moderate optimization level such as -O2. This is a compromise. The code behaves more like the optimized production build, but the debugging experience is more difficult.

Different compilers can also provide special flags to keep code debuggable at nonzero optimization levels, for example by limiting inlining. These are useful when code is very large or performance-sensitive even in the debug phase, which often happens in HPC applications.

Runtime Checks and Sanity Options

A key advantage of debug builds is the ability to turn on runtime checks that would be too expensive in production runs. These checks can reveal bugs long before they cause visible failures.

Compilers such as GCC provide warning and checking flags at compile time. For C and C++, using -Wall and possibly -Wextra can expose suspicious constructs. For runtime behavior, additional flags like -fsanitize=address or -fsanitize=undefined can instrument the code and check for memory errors or undefined behavior while the program runs. These sanitizer builds are very helpful during debugging and are usually built as special debug variants.

For Fortran, flags like -fcheck=all with some compilers add checks for array bounds, type consistency, and other runtime errors.

The tradeoff is that these checks can slow down the code substantially and increase memory usage. For short runs and smaller test problems this cost is acceptable and often pays off quickly by revealing subtle errors.

HPC numerical libraries sometimes have a similar notion of debug modes, where extra consistency checks are enabled. In your own code, you might follow the same pattern, using preprocessor macros or build configuration options to include or exclude assertions and validation code depending on whether this is a debug or release build.

Interaction with Parallelism in Debug Builds

Debug builds and parallelism interact in ways that are specific to HPC environments. When you work with MPI or threads, a debug build does more than just help with single-process bugs. It also changes timing and behavior, which can influence parallel bugs like race conditions and deadlocks.

In MPI programs, you typically compile with an MPI compiler wrapper such as mpicc or mpif90, and pass debug flags through to the underlying compiler. A typical debug compile line looks like:

mpicc -g -O0 -Wall -o my_mpi_debug my_mpi_code.c

This build is suitable for use with parallel debuggers or tools that can attach to multiple MPI processes.

Threaded programs, for example those using OpenMP, also benefit from debug builds. Many OpenMP issues are timing sensitive. While a debug build changes execution speed and may mask some bugs, it still allows you to inspect thread behavior, identify incorrect variable sharing, and verify logic on a smaller number of threads.

You should be aware that some bugs surface only in optimized parallel builds. A race condition might disappear in a debug build because extra checks or the slower code change the ordering of operations. For this reason, it is common to use both pure debug builds and "debug with optimization" builds when investigating issues in parallel code.

Running debug builds on large numbers of cores is usually not necessary and often counterproductive. You typically run with fewer ranks or threads, on smaller input data, and use interactive or short batch jobs, focusing on correctness instead of performance.

Using Debug and Release Configurations in Build Systems

In complex HPC projects, you rarely compile everything manually. Instead, you rely on build systems such as Make or CMake. These systems usually encode the distinction between debug and release builds as named configurations.

In a simple Make-based project, you might define variables like:

CFLAGS_DEBUG = -g -O0 -Wall
CFLAGS_RELEASE = -O3 -march=native

Then you select CFLAGS_DEBUG for debug targets and CFLAGS_RELEASE for performance builds. The specifics belong to the build system chapter, but for this chapter it is important to understand that debug builds are normally set up as a separate, coherent configuration. This prevents accidental mixing of debug and release object files and libraries, which can cause strange behavior.

With CMake, you explicitly choose a build type when configuring:

cmake -DCMAKE_BUILD_TYPE=Debug ..
cmake -DCMAKE_BUILD_TYPE=Release ..

The Debug build type adds -g and turns optimizations off by default. The Release type enables aggressive optimizations and usually disables debug flags. There are also combined modes such as RelWithDebInfo, which keep optimizations but still include debug symbols.

Keeping distinct build directories for debug and release configurations is a common practice on HPC systems. It allows you to switch quickly between correctness-focused and performance-focused runs without reconfiguring everything each time.

When and How to Use Debug Builds in HPC Practice

In HPC workflows, debug builds have a clear place and must be used intentionally. You rarely run debug builds at full scale. Instead, you use them with smaller problem sizes and fewer resources to validate logic and investigate failures.

Typical situations where you choose a debug build include:

You are developing new code and want to validate that algorithms behave correctly on small test cases. The ability to step through the code, inspect local variables, and see call stacks is crucial.

Your production run failed, crashed, or produced suspicious results. You then reproduce the problem on a reduced input and with a debug build, run with a debugger or extra logging, and try to narrow down the cause.

You are integrating external libraries or new parallel patterns. A debug build with additional runtime checks helps ensure that you call APIs correctly and respect data sharing rules.

On clusters with batch schedulers, you generally do not submit long, large-scale jobs with debug builds. Instead, you run shorter tests, often through interactive sessions if available. This prevents wasting shared resources and aligns with responsible computing practices.

In many teams, there is a formal separation between debug and release binaries. Debug binaries are understood to be for development and testing only. Release binaries are the ones authorized for large, production runs, after passing tests. This separation is particularly important in HPC centers where large jobs can consume significant energy and waiting time.

Summary of the Role of Debug Builds

Debug builds are an essential tool in the HPC development lifecycle. They are specifically configured to help you understand and diagnose your code, by using debug symbols, reducing optimization, and enabling extensive runtime checks.

In practice, you maintain both debug and optimized builds of your code. You use the debug build for development, unit tests, and bug hunting, usually at small scale. You use the optimized build for performance evaluation and large production runs. Managing this distinction carefully through compiler flags and build system configurations is part of everyday HPC work and supports both correctness and efficiency.