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How much of your current computation is being repeated even though the inputs affecting it never changed? HKD Kernel is a native C library designed to update only affected parts of a persistent computation after sparse changes, while matching the result of full recomputation. Its author reports a mean speedup of roughly 18,000× across the project’s documented benchmark suite—but that is a project-reported, workload-specific result, not a general performance guarantee.
What HKD Kernel is designed to do
HKD Kernel uses dependency structure to identify which regions of a computation may need updating after inputs change. If most of the state and its dependencies remain unchanged, an incremental path may reuse prior work rather than recompute everything.
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The correctness requirement is exact agreement with full recomputation. That matters: a faster result is not useful if it silently differs from the answer the complete computation would produce.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →This approach is aimed at persistent workloads—systems that retain reusable state across updates—not programs that run once with wholly new inputs. Its potential advantage depends on how much of the computation a change actually affects.
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What the 18,000× benchmark does—and does not—show
Michael Yang reports a roughly 18,000× measured mean speedup in 2026 across the repository’s currently documented benchmark suite, comparing its full-recomputation path with HKD’s incremental path. The result belongs to the project’s benchmark workload population, particularly cases with sparse changes and reusable state. It is not an independently established result or a prediction for other applications.
“This does not mean HKD makes arbitrary programs 18,000x faster.”
The key question is not whether a workload is written in C, or whether it runs on a particular processor. It is whether the work left unaffected by a change is large enough to reuse—and whether the incremental implementation can establish the required exact result.
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Where incremental computation may fit
The project identifies several candidate workload classes. These are areas to evaluate, not evidence that HKD has been validated in deployed applications or measured across every example.
- Dependency graphs, graph closure, and dependency propagation
- Incremental build systems
- Large simulations with sparse updates
- Repeated sparse numerical computation and cached numerical pipelines
- Mathematical optimization, exact cover, scheduling, and assignment
- Financial or risk recomputation, logistics, and other persistent decision systems
A good fit is more plausible when state persists, updates touch a small region, and the application can express the dependencies needed to determine what is affected. If most state changes on every update, the incremental route may have little work to avoid. The project materials do not establish a universal threshold for how small the dirty set must be.
How to evaluate the benchmark for your workload
A useful comparison gives both methods the same input, output, and correctness requirements. Compare a cold or reference full execution with an HKD update after establishing reusable state; a timing ratio alone can be misleading if one side does less work or returns a weaker result.
- Inspect the benchmark implementation. The public repository lists
benchmark/,include/, andsrc/, alongside source and build instructions. Review the benchmark code to understand what each case computes and how its reference path is defined. - Record the relevant measurements. For each case, capture cold/reference execution time, HKD update execution time, dirty-set size, and total-state size.
- Check exact correctness. Record whether the incremental output exactly equals the full-recomputation result under the required correctness standard.
- For optimization tasks, report model details. Include model class, variable and constraint counts, sparsity, objective value, feasibility, reference-solver result, and elapsed time.
- Keep conditions visible. Include hardware, compiler, build flags, and repetition methodology when the benchmark code or your own run establishes them. The project materials described here do not establish those configuration details, all per-case results, or an independently reproduced result.
For an optimization comparison, equivalent work means more than using the same input file: document the model and required solution quality, and report objective and feasibility alongside runtime. For any workload, a speedup claim is meaningful only if both paths meet the same correctness standard.
HKD is an additional engine, not a general solver replacement
The repository presents HKD as an additional computation or optimization engine, not a feature-for-feature substitute for broad general-purpose solvers. Mature solvers support more model families and features. HKD is more plausibly useful where the model is supported and persistent, sparse structure lets it avoid repeated work, and the application’s correctness requirements can be met.
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It is also a user-space library. It does not replace macOS XNU, modify CPU microcode, disable System Integrity Protection (SIP), or change processor arithmetic hardware. Its performance claim concerns how software organizes computation, not a change to the operating system kernel or CPU.
What to challenge before relying on the result
Readers can inspect the source and benchmark code in the public repository, then ask whether its cases resemble their own workload. In particular, scrutinize the workload population behind the mean, the proportion of state marked dirty, and whether the reference and incremental paths perform equivalent work.
Yang invites developers to challenge the benchmark assumptions, propose adversarial cases, share real sparse-update workloads, and identify situations where incremental recomputation is the wrong architecture. Those are useful tests of the approach: a workload that changes most of its state, cannot preserve reusable state, or lacks exploitable dependency structure may favor straightforward recomputation instead.
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