Hardware-Aware Design (Mechanical Sympathy)

When a design is bound by the machine, architecture must respect it.

The memory hierarchy is the dominant cost

Rough latencies: L1 ~1ns, L2 ~4ns, L3 ~15ns, DRAM ~100ns, NVMe ~50–100µs, network/disk far worse. A cache miss to DRAM is ~100× an L1 hit → layout and access patterns often matter more than algorithmic constants on hot paths.

• Cache lines (64B): pack hot fields together; avoid pointer-chasing (linked lists/trees thrash the cache) — prefer contiguous arrays.
• False sharing: two cores writing different vars on the same line ping-pong the line → pad/align per-core data to a cache line.
• Data-oriented design / SoA vs AoS: struct-of-arrays enables streaming + SIMD and better cache use than array-of-structs for bulk processing.
• Prefetch & predictability: sequential, predictable access lets HW prefetch and branch predictors win; random access defeats them.

Parallelism levers

• SIMD/vectorization (SSE/AVX/NEON, SVE): data-parallel hot loops; needs aligned, contiguous, branch-light data.
• NUMA: pin memory near the core that uses it; cross-socket access is far slower — partition data per node, avoid a global shared structure.
• Concurrency: lock-free/wait-free (CAS, RCU) avoids contention but is hard and ABA-prone; often a well-placed lock or sharded/per-core state beats clever lock-free. Cache-coherence traffic (MESI) is the hidden cost of shared writes.

When to invoke this

• µs-scale latency budgets, high-throughput data processing, DB/engine internals, trading, media/codec, simulation. Not for typical CRUD/web services — there, I/O and architecture-level concerns dominate, not cache lines.

Process

Identify the bottleneck resource (CPU/mem-bandwidth/latency) first, then choose the matching technique. Measure; don't micro-optimize on intuition.