cpp-hpc-optimization

name

cpp-hpc-optimization

description

Evidence-driven C++ high-performance computing design, profiling, and optimization across algorithmic time/space complexity, data layout, cache and memory behavior, numerics, SIMD, multicore scheduling, and CPU/accelerator pipelines. Use before designing or changing computation-intensive kernels, high-throughput data structures, performance-critical loops, SIMD/OpenMP/TBB/CUDA code, mixed-precision or sparse representations, or when investigating throughput, latency, scaling, cache, bandwidth, compiler-vectorization, or profiling problems.

C++ HPC Optimization

Optimize the measured data path, not the code that merely looks low-level. Preserve a simple reference implementation and make every optimization earn its complexity with correctness evidence and a representative benchmark.

Non-negotiable rules

1. Define the contract first. Record inputs, sizes, distributions, target hardware, accuracy, determinism, memory, latency, and throughput objectives.
2. Pass the complexity gate. Compare the best practical algorithmic families for the actual size range before tuning constants inside one family.
3. Profile before kernel redesign. Identify the hot path with end-to-end evidence.
4. Keep an oracle. Retain a scalar or otherwise obviously-correct reference implementation and compare every optimized path against it.
5. Model before tuning. Estimate useful operations, bytes transferred, working set, dependency depth, synchronization, and launch overhead.
6. Change one axis at a time. Measure after each transformation and revert changes that do not win on the target workload.
7. Never encode a benchmark accident as a law. Cache sizes, SIMD width, thread count, tile size, padding, and crossover thresholds are target- and workload-dependent.
8. Keep a portable fallback. ISA-specific kernels require feature dispatch, tail handling, and equivalence tests.

Workflow

1. Frame the performance contract

Write down:

• the semantic result and accepted numerical error;
• representative and adversarial input shapes and distributions;
• steady-state throughput, single-item latency, tail latency, or deadline;
• memory-footprint and allocation constraints;
• target CPUs, accelerators, compilers, build flags, and deployment topology;
• whether batching, reordering, nondeterministic reductions, or preprocessing are permitted.

Do not optimize an unspecified target. Throughput and latency often demand opposite choices: batching and deep queues improve throughput but add waiting.

2. Pass the algorithmic time/space complexity gate

Before applying cache, SIMD, threading, or instruction-level techniques:

• identify lower bounds and the best mature practical algorithm families for the declared sizes, distributions, sparsity, dimensionality, accuracy, update/query ratio, and number of timesteps or solves;
• compare total time, peak/live space, preprocessing, communication, numerical behavior, worst/expected cases, and implementation maturity;
• benchmark crossover regions and retain a size/shape-dependent portfolio when different algorithms win in different regimes.

An asymptotically better method may lose at small n; an asymptotically worse regular kernel may exploit the target exceptionally well. Conversely, no amount of constant-factor kernel tuning rescues an avoidable complexity class at scale. Choose the industrially useful family for the declared range first, then use the rest of this skill to reduce its realized cost. Preserve the declared semantic or numerical contract; optimization need not be bit-exact when the contract permits an approximate, reordered, or mixed-precision algorithm.

3. Establish correctness and measurement baselines

• Build a readable reference path before intrinsics, lossy precision, relaxed math, or concurrency.
• Create correctness tests covering empty, tiny, tail, misaligned, extreme, NaN/Inf, sparse/dense, and aliasing cases that the contract permits.
• Benchmark the actual hot operation with realistic data. Warm up, prevent dead-code elimination, report distribution rather than one lucky sample, and retain end-to-end measurements beside microbenchmarks.
• Inspect generated code and compiler optimization remarks before assuming the compiler failed.

Read references/profiling-and-cost-model.md before profiling, benchmarking, or declaring a bottleneck.

4. Classify the limiting resource

Calculate at least an approximate per-item model:

• useful operations and transcendental/divide cost;
• compulsory input, output, metadata, and intermediate bytes;
• working-set size at each reuse distance;
• independent operations versus loop-carried dependency chains;
• tasks, locks, atomics, barriers, launches, copies, and queue transitions;
• scaling versus threads, vector width, batch size, and problem size.

Classify the current regime as memory-bandwidth, cache-capacity/latency, compute/issue, dependency-latency, synchronization, task-granularity, launch, or transfer bound. A kernel can move between regimes after each optimization.

5. Evaluate change scope and ROI

Record the currently authorized edit boundary, but do not mistake it for the root-cause boundary. When evidence places the limiting work upstream or in data representation, ownership, or construction, compare:

• the best optimization inside the current boundary;
• the smallest upstream, layout, index, or API change that removes the cause;
• the practical blank-slate design, to expose the ceiling of both candidates.

For each candidate, estimate end-to-end speedup, complexity and crossover, memory, implementation and migration work, write amplification, invariant and test surface, and ongoing maintenance. If wider scope has credible net value, present the evidence and obtain user approval before expanding it. Keep the experiment reversible, measure the representative full workflow, and retain the wider change only when its observed performance and complexity benefits repay its lifecycle cost; otherwise revert it.

6. Apply transformations in economic order

Prefer the first measured transformation that attacks the current limit:

1. Remove unnecessary work, copies, allocation, conversion, and materialization.
2. Reduce footprint and improve data layout or traversal locality.
3. Fuse passes or tile/block computation to reuse data before eviction.
4. Expose compiler optimization by removing false alias/dependency barriers.
5. Vectorize, using intrinsics only when generated code proves they are needed.
6. Parallelize with enough work per task and no shared hot write locations.
7. Batch, pipeline, or overlap copies and computation when latency can be hidden.
8. Offload only when transfer, launch, and synchronization costs fit the model.

Load the relevant references before choosing:

• references/data-layout-and-memory.md — DOD, AoS/SoA/AoSoA, packing, sparse data, locality, tiling, fusion, and Morton order.
• references/allocation-and-memory-resources.md — measured allocation bottlenecks, preallocation, PMR, arenas, pools, TLS scratch, general-purpose allocators, NUMA, and allocator benchmarks.
• references/ragged-topology.md — flattened variable-length arrays, offset encodings, sidecar topology, packed sparse sets, polygon-corner topology, aligned attributes, and scan-built output.
• references/numerics-and-quantization.md — precision, FP16/BF16, shared exponent, quantization, accumulation, and numerical validation.
• references/simd-and-compiler.md — auto-vectorization, dependencies, intrinsics, SIMD tricks, tails, and ISA dispatch.
• references/parallelism-and-pipelines.md — thread degree, TLS reduction, false sharing, work stealing, queues, async launch, and pipelines.
• references/accelerator-throughput.md — CPU/accelerator transfer, CUDA-style streams, occupancy, synchronization, and launch amortization.
• references/hotpath-polymorphism.md — hot/cold boundaries, abstraction cost, and data-oriented polymorphism.

7. Validate and integrate

• Compare every output with the reference under the declared error metric.
• Run sanitizers and boundary tests before trusting benchmark results.
• Measure the same workload, hardware state, compiler, and flags before/after.
• Check performance across small, crossover, and large sizes; optimized kernels often lose below a threshold.
• Preserve the readable fallback and dispatch outside the inner loop.
• Record assumptions, selected thresholds, measured results, and rejected alternatives near the benchmark or design documentation.
• Keep cold-path architecture maintainable. Do not spread kernel-specific data layout or ISA details across the rest of the system.

Relationship to C++ OOP design

Apply $cpp-oop-style to ownership, orchestration, I/O, error handling, and module boundaries. Inside a measured hot kernel, prefer flat data, value views, batch operations, static dispatch, and explicit SIMD when evidence requires it.

The boundary should normally look like:

• abstract behavior and resource ownership on the cold/control side;
• data-only request/config/result types at the seam;
• one dense concrete pool per hot subtype, with homogeneous spans or tiles entering the hot/data side;
• dispatch once per batch, never once per element;
• a reference kernel and one or more selected optimized implementations.

Read references/hotpath-polymorphism.md before removing abstractions or adding type tags. Virtual dispatch is rarely the largest cost by itself; the lost inlining, scattered objects, unpredictable branches, and pointer-chasing around it are often the actual problem.

Source material and provenance

Read references/parallel101-case-studies.md when looking for concrete lesson progressions or deeper examples from archibate's parallel101/course and parallel101/simdtutor repositories. Search the bundled offline corpus under references/parallel101/ before depending on a maintainer's checkout or the network. Use references/parallel101/provenance.tsv to recover the repository, author, source URL, commit, original path, teaching classification, license, and SHA-256 for every excerpt.

Treat the corpus as educational experiments, not production code. The case-study index marks outdated, unsafe, incomplete, or broken examples so their ideas can be learned without copying their defects. The skill and bundled corpus are licensed under CC BY-NC-SA 4.0; retain attribution and compatible terms when redistributing adaptations.