속성:Reusable lesson
비슷한 문제를 만났을 때 다시 쓸 수 있는 내용을 적습니다.
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mlir-fft-compiler: Do not repeat this experiment without a changed hypothesis; reuse the measured scientific verdict and device-specific bounds. multi-lora-fusion: Do not repeat this experiment without a changed hypothesis; reuse the measured scientific verdict and device-specific bounds. +
alpha-factory: When an ensemble or portfolio collapses to one lineage, diagnose which pipeline stage is binding before tuning any selection-stage knob: a diversity bonus at selection is arithmetically irrelevant if upstream per-candidate quality gates already removed the decorrelated candidates. Test eligibility and selection changes as a factorial rather than sequentially, because either alone can read as a null result while their interaction carries the whole effect. When relaxing gates, partition them explicitly into role-specific quality gates (bypassable, since a hedge underperforms benchmarks by construction) versus safety gates (never bypassable), and verify selectivity by checking that an attractive-looking candidate failing a safety gate is still refused. complex-nn-signal: When benchmarking a learned detector against a classical one, control inference budget (number of correlations/evaluations per example) rather than parameter count — once the classical method must search, compute is the scarce resource and parameter-matching measures the wrong thing. Decompose any apparent neural advantage into placement (where the basis functions sit) and combiner (how their outputs are pooled) with an arm that varies only one at a time; here the entire effect lived in the combiner. Diagnose a trained arm losing to a parameter-free arm by comparing TRAIN accuracies — if the trained arm underfits the parameter-free one, the deficit is expressivity, not generalization, and adding data or regularization will not help. Expect learned bases to help when the true support is sparse and discrete (they concentrate on it) and to be inert when the support is a continuum (the uniform grid is already optimal, so gradient descent only adds placement scatter). azure_inference_queueing: Two reusable items. (1) Before building a joint optimizer for a coupled item-selection plus variant-choice problem, check where the cheap variant's weight multiplier sits relative to 1/2 of the full weight: at m <= 1/2 the coupled problem provably degenerates to the decoupled one, the nesting constraint never binds, and single-variant approximation theory transfers with a strictly tighter constant. Adding a cheap-variant dimension can NARROW rather than widen a greedy optimality gap, because the smaller variant improves packing granularity — the opposite of the natural intuition. (2) When a theorem yields a structural ordering condition, never report it as a prediction of the realized event without including the resource constraint in the test: an ordering can hold on ~50% of instances while the corresponding realized event occurs on 0%, because realizing it also requires the capacity to reach that point. Keep the structural quantity and the realized quantity in separate reported fields; conflating them is a repeatable, self-inflicted falsification. gaussian-3mul-compiler: Two reusable lessons. (1) When evaluating whether a hardware primitive such as FMA favours one algebraic formulation over another, check whether the supposedly disadvantaged formulation can also use the primitive - here the Karatsuba-style 3-mul's combining product was fusable, which neutralised the entire predicted effect. Comparing a contracted baseline against an un-contracted candidate is a rigged comparison. (2) For floating-point error studies, report medians and quantiles, never sample means: relative FP error is heavy-tailed and mean ratios are dominated by rare near-cancellation samples. Verify any error-ratio effect with a seed-stability check across at least five seeds before believing it - in this study an exploratory mean-based run produced a number that supported the hypothesis the stable estimator then refuted.
mlir-fft-compiler: For held-out validation of a cost-model rate law, first run a CPU resolving-power pre-analysis: enumerate held-out points, compute each candidate law's prediction, and confirm at least one point breaks the calibration-point degeneracy by more than the acceptance tolerance. Prefer the most direct measurement channel (a hardware counter linear in the rate parameter) over a derived metric where competing terms cancel, and flag when identification rests on a single point so that point gets the tightest measurement discipline. +
alpha-factory: When an evaluator-semantics fix (excluding a hidden suffix) invalidates prior artifacts, regenerate and re-test rather than assuming the old conclusion breaks: check whether the affected quantity (here candidate-level total_return/fitness over the full range) actually feeds the reported conclusion (here an OOS window that structurally never overlapped the hidden suffix). Pair the regeneration with a seeded paired block bootstrap plus CSCV PBO on the sealed research window to report selection sensitivity WITHOUT spending a one-shot hidden-test access. +
azure_inference_queueing: To generalize a single-constraint knapsack integrality-gap certificate to m constraints, use the bounded-variable-LP basic-solution property: a vertex has at most m fractional variables, so rounding them down yields a feasible integer solution and LP*−IP* ≤ sum of the (≤m) fractional item values. Force a vertex solver (scipy linprog method='highs-ds' dual simplex) — the default 'highs' can dispatch to interior-point and return a non-basic point with more than m fractional coordinates, spuriously breaking the structural claim. The relaxation certificate generalizes cleanly even when the greedy-optimality argument does not. +
alpha-factory: Before treating a favorable single-window backtest bootstrap as a transferable edge, replay the exact frozen rule across independent non-overlapping outer windows and pool them as one rolling-origin backtest; separate the STRUCTURAL claim (composition/eligibility that is window-independent by construction) from the ECONOMIC-MAGNITUDE claim (risk-adjusted return), because the former can replicate cleanly while the latter is concentrated in one regime. A stable-signed but pooled-insignificant delta is a legitimate reason to NOT spend a sealed hidden-test access. +
multi-lora-fusion: Report a per-shape/per-workload cost constant fit from timing with its run-to-run spread AND its batch-to-batch spread, not a single-pass point estimate; a single pass here understated dispersion ~2x, and the batch-aggregate mean moved by more than its own sd. Ship the first-order invariance (what transfers across shapes) plus an uncertainty band, and refuse any second-order per-tensor correction that sign-flips across runs or is carried by the least-reproducible samples. +
complex-nn-signal: An L1 penalty on an optional model degree of freedom behaves as a data-adaptive gate: because it competes against a cross-entropy data-gradient whose magnitude scales with how useful that DOF is for the training distribution, one fixed penalty weight nulls the DOF where it is useless and keeps it where it is load-bearing. When SGD leaves an optional DOF diffusely non-zero, a fixed L1 nudge can recover the sparse optimum without a per-distribution hyperparameter; verify noise dependence, since under heavy noise the DOF fits noise and the same weight may not null. +
alpha-factory: A diversification finding that depends on one lineage escaping a fitness/decorrelation admission cap can be entirely universe-specific: verify transfer by regenerating the frozen protocol on a separate universe and inspecting the admission trace (carrying-sleeve standalone fitness and runner-up correlation to the elite) BEFORE spending any sealed hidden-test access. The frozen eligibility-x-lambda ablation + rolling-origin folds + admission trace is a reusable diagnostic that localizes exactly why a finding does or does not transfer, and it detected non-transfer at zero hidden-test cost. +
algebraic-ml-compiler: Do not repeat this experiment without a changed hypothesis; reuse the measured scientific verdict and device-specific bounds. +
sparse-lowrank-runtime: When claiming a small parametric cost model generalizes better than a lookup table, control against nearest-neighbor lookup, not just exact-match: a memorizer that copies a neighbor's decision boundary can be worse than the trivial baseline when that boundary is not invariant across the extrapolation covariate. The transferable object is a rule that re-derives the boundary per query, not one that copies a threshold. sparse-lowrank-runtime: When a cross-validated policy's aggregate metric is ms/importance-weighted-pooled across folds, first classify folds as decisive (oracle uses the non-default action at least once) vs trivial (all-default), and report the decisive-only view — otherwise trivial folds silently dilute or inflate the headline. Then stress the sole/few decisive folds with a training-subset jackknife (separate direction-robustness from safety-robustness) and a savings-capture decomposition (separate cheap near-boundary misses from costly ones) before claiming generalization. +
rfuse modernizing userspace filesystem framework through scalable kernel userspace communication b3196db3 +
Shard cross-boundary queues by core and preserve compatibility above the transport layer. +
예약 메모리 재사용에서는 평균 효율보다 원소유자에게 되돌리는 bounded reclamation path가 핵심이다. +
Separate a cheap health signal from adaptive failure policy and connect it to rapid recovery. +
보안 완화 비용은 탐지기만이 아니라 물리 배치의 공격 집중도부터 줄여라. +
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Optimize the full storage control/data path, including background and recovery work, as one system. +
Co-design metadata indexing and fine-grained locking rather than layering locks over a serial tree. +
NUMA 메타데이터 복제는 전체 복제보다 수요 기반 부분 복제와 sharer 추적을 사용하라. +
Partition both the search space and control plane across host and near-data compute. +
Decouple and pipeline opposing memory flows, prioritizing scalable progress over perfect victim selection. +