You can't fool the optimizer

📝 Discussion Summary (Click to expand)

The Hacker News discussion revolves around the capabilities and limitations of modern optimizing compilers. Three dominant themes emerged:

1. The Compiler as an Oracle vs. Programmer Responsibility

There is a strong debate about the extent to which programmers should rely on the compiler to optimize code. While many accept that compilers handle micro-optimizations well, others argue that they fail on architectural or high-level algorithmic choices, reinforcing the idea that developers are still responsible for the fundamentals.

Quotes:
- The initial premise: "I always code with the mindset 'the compiler is smarter than me.'" (jagged-chisel)
- A counterpoint asserting limits: "You are responsible for the algorithms, it is responsible for the code micro optimizations" (stonemetal12)
- Another user emphasizes architectural limitations: "They can't change your program architecture, algorithms, memory access patterns, etc." (IshKebab)
- A critique of blind faith: "That mindset will last until the first or second time you walk through the compiler's assembly-language output. ... if you do need to squeeze performance out of the processor, you can do a lot better than the compiler does." (kragen)

2. Data Structure and Memory Layout Matter More Than Micro-Optimizations

Several contributors stressed that optimizations related to data locality, structure layout, and memory access patterns often yield orders of magnitude better performance than micro-optimizations the compiler might miss. These high-level structural decisions are impossible for optimizers to infer safely.

Quotes:
- "Optimizers generally don't change data structures or memory layout but that can make orders of magnitude difference in the performance of your program." (adrianN)
- "I am able to consistently and significantly improve on the performance of research code without any fancy tricks, just with good software engineering practices. By organising variables into structs, improving naming, using helper functions, etc..." (jaccola)
- "Once you set up a pointer-chasing data infrastructure changing that means rewriting most of your application." (adrianN)

3. Compiler Heuristics and Pass Ordering Lead to Missed Optimizations

Discussion frequently pointed out that compilers rely on a fixed sequence of heuristics (passes) rather than exhaustive searches. This leads to situations where mathematically equivalent code produces drastically different, and sometimes less optimal, assembly output because the intermediate representation does not match a known pattern for optimization.

Quotes:
- Regarding struct unwrapping: "The magic seems to happen in your link at SROAPass, 'Scalar Replacement Of Aggregates'." (amiga386)
- Regarding why a simple conditional optimization fails: "The problem is that this problem really starts to run into the 'the time needed to optimize this isn't worth the gain you get in the end.'" (jcranmer)
- On why obvious mathematical equivalences are missed: "The compiler didn't recognize that x % 2 == 0 && x % 3 == 0 is exactly the same as x % 6 == 0 for all C/C++ int values." (senfiaj)

🚀 Project Ideas

Canonical Form Code Generator (CFCG)

Summary

A tool that analyzes the LLVM/Compiler Intermediate Representation (IR) or the resulting assembly of compiled code to identify the canonical mathematical/algorithmic form (e.g., recognizing x % 2 == 0 && x % 3 == 0 as equivalent to x % 6 == 0).
Core Value Proposition: Provides programmers with actionable feedback on why certain optimizations are being missed, by showing them the underlying mathematical equivalence that the compiler could recognize in a simpler form. This addresses the desire to "massage your source code until the shit stinks less" by suggesting canonical source patterns.

Details

Key	Value
Target Audience	Developers writing C/C++ who are debugging performance cliffs or suspect missed optimizations based on assembly inspection (e.g., users observing `is_divisible_by_6` being slower than `is_divisible_by_6_optimal`).
Core Feature	Takes assembly or SSA IR as input and outputs the simplest mathematical expression or loop structure (the "canonical form") that produces the same result, potentially highlighting where source code structure prevented optimization (e.g., short-circuiting due to `&&`).
Tech Stack	Rust (for performance and robust parsing/analysis), LLVM/Clang libraries (to parse IR or disassembly data), potentially leveraging concepts from equality saturation research (like E-graphs) for powerful equivalence checking.
Difficulty	High (Requires deep understanding of IR, optimization passes, and formal equivalence proving.)
Monetization	Hobby

Notes

"My point is not to over rely on optimizer for math expressions and algorithms." (senfiaj) This tool directly addresses this frustration by codifying which mathematical optimizations compilers missed due to source structure.
It operationalizes the idea: "I would prefer is a separate analysis tool that reports what optimizations are possible and a compiler that makes it easy to write both high level and machine code as necessary." (norir)

Non-Escaping Aggregate Analysis & Refactoring Helper

Summary

A static analysis tool focused specifically on identifying local struct allocations within functions that do not escape, tracking their usage, and suggesting structured refactoring replacements for macro-based, performance-critical code blocks.
Core Value Proposition: Automates the process of checking if a local struct might be preventing Scalar Replacement of Aggregates (SROAPass) by showing if any part of the struct crosses function boundaries or is used in ways that imply persistence beyond the function scope.

Details

Key	Value
Target Audience	C/C++ performance engineers working on low-level, hot code paths where raw stack allocation performance gains are critical (e.g., avoiding pointer chasing).
Core Feature	Analyze source code (or compiled object files via DWARF/debug info) for stack-allocated aggregates. It flags structs whose members could likely be promoted to SSA/registers if the structure was flattened, referencing the SROAPass discussion. Generates suggested source code changes (e.g., changing `struct foo x;` to `uint32_t a, b, c;` within the specific limiting scope).
Tech Stack	C++ (integrating with Clang's LibTooling for source code analysis), possibly leveraging Link-Time Optimization (LTO) data models if analyzing compiled output.
Difficulty	Medium/High (Requires complex call graph and escape analysis within single-function contexts and correctly mapping to source structures).
Monetization	Hobby

Notes

"This pass thought the struct did escape. I should revisit my code and see if I can tweak it to get this optimisation applied." (amiga386) This tool actively helps the user perform that revisit, focusing only on the relevant local scope where SROA should be strong.
"On higher optimization levels, many passes occur multiple times. However, as far as I know, compilers don't repeatedly run passes until they've reached an optimum." (jakobnissen) This tool provides human-driven checks for optimizations that depend on specific, hard-to-trigger analysis boundaries.

Compilation Unit Boundary Visibility Auditor

Summary

A build system plugin or dedicated tool that analyzes the linker map/symbol table for production binaries, comparing symbol visibility (especially static vs. external declarations) against compiler flags (-fvisibility=hidden, etc.) to quantify the impact of compilation unit fragmentation.
Core Value Proposition: Directly measures how often developers are foregoing function/variable elision by having to split definitions across translation units for fast incremental build times, providing metrics on potential function merging/inlining gains vs. build time cost.

Details

Key	Value
Target Audience	Large project maintainers concerned about build times (`bruce343434`'s dilemma) who want to quantify the efficiency cost of separate translation units required for fast incremental compilation.
Core Feature	Scans ELF/COFF binaries for externally visible symbols that have identical machine code sequences (perhaps using the linker's ICF capabilities as a baseline), mapping those back to source files. It reports a "Visibility Overhead Score."
Tech Stack	Python/Go for parsing binary formats (ELF/COFF), leveraging existing linker utilities (`nm`, readelf) or dedicated binary parsing libraries.
Difficulty	Medium (Binary parsing is non-trivial but the equivalence checking is partially addressed by existing linker/optimizer features.)
Monetization	Hobby

Notes

Addresses the trade-off: "To achieve incremental builds, stuff is split into separate source files... which requires symbols of course." (bruce343434) This tool quantifies the exact cost of that necessary split.
Directly relates to the discussion around static linkage: "You're telling the compiler that the function doesn't need to be visible outside the current compilation unit, so the compiler is free to even inline it completely..." (moefh). This tool visualizes how much external linkage is being used unnecessarily.