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Cache lines, SIMD intrinsics, profile-guided optimisation, lock-free data structures, and memory ordering. Every number comes from real measurements on real hardware — assembly output included.
Feed `std::regex` a pathological pattern and a crafted input, and watch it spiral. Input length 16 characters: 4.48 milliseconds. Input length 18: 18 milliseconds. Input length 20: over a second. The
Compiler optimization passes live and die on tree traversal. LLVM's dominator analysis alone queries ancestor relationships thousands of times per function. A real C++ translation unit with heavy temp
Unsigned overflow wraps. That's the contract. C++ won't catch it, the CPU won't trap it. But your size calculation that wraps to a smaller buffer? That's a memory corruption vulnerability, and it's yo
You have a sorted array. Need to search it. `std::lower_bound`: O(log n), predictable, robust. This is the default solution. But my industry experience breeds skepticism. Every few months, a new paper
I've spent enough time staring at `perf stat` output to recognize a pattern: OpenMP's dynamic scheduler measures **55,593 ops/sec** on Zipf-distributed task costs with 512 tasks. That's about 18 micro
Llama.cpp's PR #22423 landed a kernel fusion for RMS_NORM + MUL in the ggml CPU backend a few weeks ago. The speedup: 1.60×. Consistently. Across dimension sizes, thread counts, even hardware variatio
Here's the uncomfortable truth: modern C++ standard library I/O becomes a bottleneck at scale. Traditional POSIX APIs introduce 1–10 microseconds of latency per operation due to syscall overhead and k
If you watched Ben Saks's CppCon 2025 'Back to Basics: Move Semantics' talk, you know what moves are and why the compiler calls them. That talk is solid. C++26 doesn't contradict it. What it does is t
I manually unrolled a byte-counting loop with four independent accumulators — the textbook ILP optimization — and it ran 2.08x *slower* than the plain loop. The plain loop that GCC had quietly autovec
The RP2350 has a feature most embedded developers ignore. Two Cortex-M33 cores at 150 MHz, 520 KB of SRAM, $0.80 in quantity — and buried in the ISA, packed arithmetic instructions that process four 8
That's the whole story. I took a virtual-dispatch interpreter loop — the textbook PGO target — instrumented it, trained it on a representative workload, and recompiled. Both GCC 15.2.1 and Clang 21.1.
A `std::mutex`-protected `std::deque` is 12% faster than moodycamel::ConcurrentQueue when contention is low.
Your threads are doing independent work on independent data, and yet adding a second thread makes everything six times slower. This is false sharing, and it hides in struct layouts and thread-local counters across more production codebases than anyone wants to admit.
