Assembly-Level Performance Analysis: Detecting and Measuring Software Performance Debt in C/C++

Introduction: Why assembly code matters for performance optimization

When optimizing embedded software, most developers reach for profiling tools that measure execution time on running hardware. While useful, these tools have a fundamental limitation: they tell you what happened during one specific execution, not what could happen with different cache states, task scheduling, or processor conditions.

For C and C++ embedded developers, there's a more reliable approach: analyzing the assembly code your compiler generates. This methodology provides deterministic, hardware-specific insights that traditional profiling cannot match. By examining assembly instructions and their associated costs, you can detect performance debt objectively and compare implementations with confidence.

This article explores the technical methodology for measuring software performance debt through assembly analysis, combining static code examination with dynamic execution data to reveal exactly where optimization opportunities exist in your embedded systems.

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The Foundation: Linking source code to assembly instructions

Performance debt detection begins with understanding the relationship between your C/C++ source code and the machine instructions your processor executes. This linkage is essential because performance optimization ultimately happens at the hardware level: every CPU cycle, every memory access, every branch matters in resource-constrained embedded systems.

Why assembly analysis over traditional profiling?

Traditional profiling measures actual execution time, which varies based on numerous execution hazards. There are many hazards to running a program on a processor:

• Memory and cache misses: Data access patterns affect timing
• Out-of-order mechanism: The processor may reorder instruction execution
• Parallelization: Concurrent execution affects timing
• Task preemption: Higher-priority tasks interrupt execution

These factors mean that running the same code twice can produce different execution times. For complex architectures, there's a good chance that each execution will result in a different number of cycles. When you're trying to determine whether an optimization actually improved performance, this uncertainty makes reliable comparisons nearly impossible.

Traditional profiling tools measure what happened during one specific execution. They can't tell you whether your optimization made things better or whether you just measured a favorable execution path.

Assembly-level analysis eliminates this uncertainty by examining what the compiler generates before execution. The instruction sequence is deterministic as the same source code with the same compiler flags always produces the same assembly output. This determinism is the foundation of reliable performance debt measurement.

The two-layer representation

The methodology requires maintaining two synchronized versions of your code:

Layer 1: C/C++ source code 

Your original implementation with full readability and maintainability.

Layer 2: Compiler-generated assembly code 

The exact instructions your target processor will execute, generated using your production build configuration (optimization level, compiler flags, target architecture).

The critical connection between these layers comes from compiler debug information. When you compile with debugging symbols enabled, the compiler embeds metadata that maps each assembly instruction back to its originating source code line. This mapping allows you to:

• Identify which source code lines generate the most expensive instruction sequences
• Compare different C/C++ implementations by examining their assembly output
• Quantify performance improvements in concrete terms (CPU cycles, instruction counts)
• Localize performance debt to specific functions or code blocks

‍Capturing assembly output with debug information

To generate assembly listings with source line mappings, compile your code with debug symbols and listing generation enabled. The exact flags depend on your toolchain, but the principle remains consistent: you need both the assembly instructions and the metadata linking them to source code.

The resulting assembly listing shows each instruction alongside its corresponding source line. This interleaved format reveals exactly how the compiler translated your high-level code into machine instructions, making it possible to see where performance costs originate.

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Static analysis: quantifying performance without execution

Static analysis examines your code's assembly representation without running it. This approach provides the deterministic baseline needed for reliable performance comparisons.

Instruction characterization: Building your ISA cost database

Every processor architecture defines an Instruction Set Architecture (ISA) that specifies which instructions exist and their associated costs. The instruction set provides essential information about the performance associated with a code.

These costs vary significantly between different types of instructions. The methodology requires characterizing each instruction for your specific target hardware, as performance debt must be considered in relation to the hardware platform on which the code will be executed.

Critical insight: These costs are architecture-specific. Performance debt must always be measured relative to your target hardware. Some performance enhancement ideas are only valid on certain hardware platforms – an optimization that improves performance on an ARM Cortex-M4 might hurt performance on a RISC-V processor with different instruction costs and cache behavior.

Beyond cycle counts, you can characterize instructions by other metrics depending on your chosen performance parameter. Performance debt can target execution time, CPU load, memory, or even energy consumption. Each assembler instruction can be individually characterized by CPU cost, power cost, or other metrics depending on what you need to measure.

Calculating static performance cost

Once you've characterized your target ISA, calculating static performance cost follows a straightforward process:

1. Parse the assembly listing to extract all instructions
2. Look up each instruction's cost in your ISA database
3. Sum the costs for functions or code regions of interest

With these two pieces of information (the assembly code and the ISA costs) you can evaluate the implementation of a piece of code locally, and thus compare different implementations of the same program by comparing the weighted assembler code.

Static analysis provides objective measurements. If you're measuring CPU cycles and Implementation A generates 150 cycles while Implementation B generates 120 cycles for the same functionality, you've quantified a 30-cycle performance debt.

Comparing implementations to detect debt

The power of static analysis emerges when comparing different implementations of the same functionality. The whole point of this methodology is to determine that a different implementation of a piece of code could produce a performance gain.

Consider two implementations of the same algorithm. One might be straightforward but inefficient, while another uses optimization techniques to reduce instruction count or use less expensive instructions.

Static analysis of both versions reveals the cycle count difference. If the chosen metric is the number of CPU cycles, you'll be able to measure the difference between one implementation and another in terms of the number of cycles.

This cycle difference is your performance debt. The original implementation leaves this performance on the table. By quantifying it precisely, you can make informed decisions about whether the optimization is worth pursuing.

Identifying statically dead code

Static analysis also reveals code that generates no assembly instructions. Studying the generated assembly code allows you to highlight areas of the code that would not generate corresponding assembly code. This is called "statically dead code."

This occurs when:

• The compiler's optimizer eliminates unreachable code
• Constants or expressions evaluate at compile time
• Conditional compilation removes code blocks

This detection allows you to obtain a metric of code coverage in the static sense of the term identifying which source lines actually contribute to the executable.

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Dynamic analysis: weighting performance by real-world usage

Static analysis answers "how expensive is this code?" Dynamic analysis answers "how often does this code run in production?"

If you want to quantify a performance debt on the scale of an entire program in order to focus on the most critical parts, you need to place yourself in a case study as close as possible to the actual operation of the software when it goes into production.

A function consuming significant cycles isn't a problem if it executes once during initialization. The same function becomes critical if it executes thousands of times per second in your main processing loop. Dynamic analysis provides the execution frequency data needed to prioritize optimization efforts effectively.

Pure C/C++ instrumentation: platform-independent profiling

The dynamic analysis phase uses a powerful technique: instrumenting the C/C++ source code directly by injecting counter-recovery code to retrieve execution information.

This approach provides three significant advantages:

1. Not tied to hardware requirements 

This method is not tied to a hardware requirement such as debugging probes. Because the instrumentation is pure C/C++, you can run profiling on any platform: your target hardware, an emulator, or even a PC.

2. No timing interference 

Because the instrumentation does not measure a time value, the measured information is not affected by the instrumentation code. Counter-based instrumentation doesn't measure time, only how many times code executes, so it doesn't affect what it measures.

3. Collect once, apply everywhere 

Because the instrumentation is purely C/C++, the measurements are not affected by the processor running the instrumented code. It is therefore possible to run it on an emulator or even on a PC-type processor and then quantify the gains with the assembler generated for the target.

The dynamic information gathered is "pure" information in C, it is not tied to the hardware on which the code is running. You can collect execution frequencies once and apply that data to assembly analysis for different hardware targets.

Representative workload testing

For dynamic analysis to provide meaningful data, the input data set must be representative of the actual execution of the code in its production environment.

Your test workload should:

• Cover the typical range of input data your system processes
• Reflect actual usage patterns in production
• Exercise significant code paths
• Run with sufficient data to stimulate different parts of the code

One of the elements that can be measured to verify that the values are sufficient to stimulate the different parts of the code is code coverage, this time dynamic. This metric can be used to verify that the input data used is representative of, or at least able to stimulate, the different areas of the code.

If coverage is low, your workload isn't representative and you need additional test cases before dynamic analysis can provide reliable optimization priorities.

Dynamic dead code detection

While static analysis finds code that compiles to nothing, dynamic analysis finds code that compiles to assembly instructions but never executes in practice. This code consumes ROM space without providing value in actual operation.

The principle is clear: code that generates assembly but has zero executions during representative workload testing may be candidates for removal or conditional compilation.

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Combining static and dynamic: weighted performance debt

The methodology's full power emerges when you combine static cost analysis with dynamic execution frequency. Dynamic analysis information allows you to re-weight the assembler code whose initial weightings were obtained by static analysis alone.

The concept is straightforward: multiply static cycle costs by how often the code executes to reveal system-level performance impact.

Practical example: prioritizing optimizations

While static analysis alone might suggest optimizing code with the highest per-call cost, this metric is not intended to provide a universal potential performance gain, but rather to indicate where work and opportunities exist in a piece of code to improve performance.

Consider functions with different characteristics:

A function with high static cost but rare execution may have less total impact than a function with moderate static cost but frequent execution. This new metric needs to be set against the impact of the debt-generating area on the scale of the entire code.

Dynamic execution data reveals which optimizations will actually improve system performance. It is essential to balance the performance loss against the overall impact on the code to have a good estimate of the metric and be able to use it to improve software performance.

Performance debt as a continuous metric

The goal is to apply this methodology as early as possible in the software development cycle and on an ongoing basis, in particular by automating it and integrating it into continuous integration and deployment chains.

By calculating weighted performance debt regularly, you create a continuous metric that tracks software efficiency over time, enabling early detection of performance issues throughout development.

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The performance debt workflow: from detection to optimization

The methodology describes a two-phase approach for measuring performance debt:

Phase 1: Static analysis

Static analysis provides the ability to:

• Compare different implementations of the same piece of code
• Quantify the impact of a code change: the performance debt itself
• Highlight areas of the code that would not generate corresponding assembly code (statically dead code)
• Provide code coverage in the static sense

This phase uses the assembly code and ISA characterization to determine cycle costs without execution.

Phase 2: Dynamic analysis

Dynamic analysis allows you to get as close as possible to how the software will actually perform in production, providing valuable metrics on memory usage, execution time, and energy consumption.

Dynamic analysis collects two key pieces of information:

• Dynamic code coverage
• Number of times each line of assembly code is executed

This information allows you to weight the measurement of performance debt and the associated gain using code execution data.

The complete process

The methodology combines both phases. Static analysis determines whether portions of the code generate performance debt, without comparing the associated gain to runtime data. Dynamic analysis provides execution data to weight these measurements and reveal which debts matter most in production.

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Addressing common implementation challenges

Challenge 1: Execution uncertainty and model limitations

A fundamental challenge exists: there are many hazards to running a program on a processor, and it is very difficult to predict in advance how a software application will run.

The solution is the methodology's core requirement for determinism. To make reliable comparisons, it was essential to overcome these uncertainties. The goal is to ensure that, with code as input, the performance measure as output is deterministic.

However, this modeling approach has trade-offs. The use of this model can lead to inaccuracies in the gain measurements. The key point is that determinism is more valuable than perfect precision: the model avoids artificially highlighting the vagaries of execution that could lead to unnecessary or even harmful optimizations.

Challenge 2: Compiler optimization effects

The methodology requires analyzing the assembly code generated by the compiler using the compilation method used by the programmer with the options and flags used.

Solution: Always analyze the actual production build configuration. The assembly you analyze must match what will be deployed.

Challenge 3: Multi-parameter optimization trade-offs

A debt on one parameter may have the opposite impact on another. For example, certain ways of optimizing execution time, such as function inlining (replacing a function call with the code of the called function), can in some cases have a negative impact on ROM.

Solution: The different parameters can be considered together, but the choice is made to focus on one of the parameters. Select your primary constraint based on system requirements.

Challenge 4: Representative workload definition

It is important to note that for execution information to be relevant, the input data set must be representative of the actual execution of the code in its production environment.

Solution: Use code coverage as a verification metric to verify that the input data used is representative of, or at least able to stimulate, the different areas of the code.

‍Integration into embedded development workflows

The goal is to apply this methodology as early as possible in the software development cycle and on an ongoing basis, in particular by automating it and integrating it into continuous integration and deployment chains.

This early and continuous integration transforms performance from a late-stage activity to an ongoing quality metric throughout development.

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Real-world impact: The environmental context

Digital technology's environmental impact is substantial. A study by ADEME and Arcep from January 2022 showed that 2.5% of France's carbon footprint is linked to digital technology, more than the waste sector at 2%.

This impact of digital technology may be difficult to perceive when working with remote software, but it is very real and needs to be studied.

Several factors drive the need for this methodology:

• An ever-increasing volume of lines of code (the automotive sector is projected to reach 650 million lines by 2025)
• An increase in the complexity of the software being developed
• The integration of Artificial Intelligence (AI) into software development tools and practices has a strong impact on the quality of developed code
• The growing impact of digital technology on the environment is driving the need to rework software building bricks as and when required

Performance optimization directly addresses environmental concerns by reducing power consumption in systems deployed at scale.

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The Role of C/C++ in assembly-level analysis

The programming language used has an impact on this methodology. A software program written in C/C++ serves as the reference example for this approach.

Key reasons for C/C++'s suitability include:

Hardware Proximity

The proximity to the hardware, as well as the great mastery of various aspects (especially the memory part) brought about using a low-level language such as C or C++ are two obvious reasons for its efficiency and performance (of course, when you know how to code in these languages).

Assembly Code Linkage

The use of these languages allows you to link the source code and the generated assembly code, an essential point in the methodology presented.

The methodology can be applied to both embedded and off-the-shelf software when using C/C++.

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Conclusion: Deterministic Performance Analysis for Embedded Systems

Software performance debt methodology provides embedded developers with what traditional profiling cannot: deterministic, hardware-specific, quantifiable measurements of optimization opportunities.

By analyzing assembly code statically and weighting results with dynamic execution data, you can:

• Compare implementations objectively without execution variability
• Identify exactly where performance debt exists in your codebase
• Quantify potential improvements in concrete terms (cycles, power, memory)
• Prioritize optimization efforts based on real-world impact
• Track performance continuously throughout development

For embedded systems where every cycle, every milliwatt, and every byte matters, this precision transforms performance optimization from art to engineering.

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Key Takeaways

• Assembly-level analysis provides deterministic performance measurements by examining compiler-generated machine code
• Static analysis calculates instruction costs using ISA databases specific to your target hardware
• Dynamic analysis collects execution frequencies using pure C/C++ instrumentation for platform independence
• Weighted performance debt combines static costs with dynamic frequencies to reveal high-impact optimization targets
• Determinism enables reliable comparison of implementations without execution variability
• C/C++ languages are optimal for this methodology due to their hardware proximity and assembly inspection capabilities
• Continuous measurement integrates performance debt analysis into CI/CD pipelines for ongoing quality assurance
• Environmental impact makes performance optimization increasingly important for sustainability goals

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