Performance optimization is hard because it's fundamentally a brute-force task

Micro vs macro/architectural optimization

• Several comments distinguish “micro” (instruction-level, cache, pipeline) from “macro”/architectural optimization (choosing better algorithms, dataflows, query patterns).
• Architectural changes are often “cheap” if you have domain expertise: pick the right algorithm, restructure APIs to match client usage, remove redundant work.
• At the “tip of the spear” (codecs, HPC, AI kernels), the low-hanging fruit is gone and optimization becomes much more intricate and incremental.

Profiling, intuition, and theory

• Debate around the “intuition doesn’t work, profile your code” mantra:
  • One side: profiling and measurement are indispensable; intuition alone leads to focusing on the wrong spots.
  • Others: profiling is not a substitute for reasoning; you still need models, big‑O thinking, and understanding call stacks and architecture.
• Some describe a healthy loop: build a mental model → optimize according to it → profile to validate and correct it.
• Misuse of profiling is common: chasing leaf functions, ignoring redundant higher‑level loops, or measuring with unrealistic workloads.

Tooling, compilers, and DSLs

• Existing tools: perf, VTune, PAPI, compiler reports like -fopt-info. They help, but many find them awkward or incomplete, especially for full call trees or microarchitectural behavior.
• Desire for richer tools: cycle‑by‑cycle visibility into pipeline stalls, port usage, memory vs compute balance, local behavior rather than just global counters.
• Discussion of language/tool support:
  • DSLs like Halide separate “algorithm” from “schedule” so you can change performance strategies without duplicating logic.
  • GCC function multiversioning, micro‑kernels in libraries, and Zig/D/C++ compile‑time execution are cited as partial solutions.
  • Interest in e‑graph–based compilers (e.g., Cranelift) that keep multiple equivalent forms and choose an optimal lowering later, versus traditional greedy passes.

Hardware-level details and micro-optimization

• Comments highlight data dependencies, pipeline bubbles, and register pressure; sometimes algorithms are restructured purely to create independent instruction streams.
• Memory access patterns (linear vs random), cache behavior, and branches often dominate; intuition about CPUs is hard to align with high-level algorithm analysis.
• Examples of store/load and memory-mirroring tricks on some microarchitectures; disagreement over what is ISA vs microarchitecture responsibility.

Algorithmic choices, complexity, and “simple code”

• Many argue most gains come from “do less work”: remove redundant calls, avoid N+1 queries, pick better data structures (e.g., hash maps instead of quadratic scans) and algorithms.
• Others caution that big‑O is not everything: for small N, simpler O(n²) code may be faster and clearer; but it encodes hidden future faults if N grows.
• Some frame program optimization as effectively impossible to solve globally (NP hardness, Rice’s theorem); practical work is local search via divide-and-conquer and auto‑tuning of variants.

Caching, architecture, and pitfalls

• Standard advice for app developers: profile, then:
  • Move invariant computations out of hot loops.
  • Cache appropriately; memoize where safe.
  • Reduce work or loosen requirements where users won’t notice.
  • Shift work off the critical path (background, async, concurrency).
• Several warn that caching can obscure real costs, distort profiling, increase memory pressure, and break assumptions (stale values, inconsistent snapshots).

Is optimization fundamentally brute-force?

• Some agree with the article’s thesis for micro/SOTA optimization: once you’ve applied known theory, you still must explore many variants and combinations; the search space explodes and feels brute‑force.
• Others push back: good mental models, documentation of performance characteristics, and experience can narrow the search enough that it’s more “skilled engineering” than brute force, especially at application level.

Human factors and “flow”

• Multiple commenters say optimization is particularly satisfying work: tight feedback loop, clear metrics (“+25% speedup”), and a “hunt the bottleneck” feel.
• Comparing it to debugging, dieting, or hunting: success requires persistence, careful measurement, and willingness to iterate, but the rewards are tangible and often highly valued by teams and organizations.