속성:Applicability
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mlir-fft-compiler: Only the recorded shapes, dtypes, software revision, and physical L40S GPU 3.
multi-lora-fusion: Only the recorded shapes, dtypes, software revision, and physical L40S GPU 3. +
alpha-factory: Holds for gated candidate-selection pipelines that filter per-candidate on standalone merit before assembling a portfolio or ensemble, where some candidate roles are expected to underperform standalone benchmarks. Demonstrated on one ETF macro pool, one asset universe, and a single 587-observation validation window with lambda=0.5 tested rather than calibrated; the dose-response shape, generalization beyond two lineages, and out-of-sample confirmation on the sealed hidden test are all unestablished. The joint-necessity structure is expected to transfer more readily than the specific effect sizes.
complex-nn-signal: Synthetic single-tone Doppler-bin classification, K=4 classes, sequence length L=16, complex AWGN at −5/0 dB, 5 seeds, CPU, small models with shared hyperparameters; detectors compared are a uniform-grid correlator scan and a diagonal unit-modulus linear complex recurrence with a linear magnitude read-out. The negative result is bounded by the learned bank having no cell structure, so it cannot implement a per-cell max even in principle — the fair retest is a max-pooled learned bank, ideally under a non-uniform within-cell prior where the uniform grid is provably mismatched. Conclusions about budget-matched evaluation and the train-accuracy underfitting diagnostic generalize beyond this task; the specific accuracy margins do not.
azure_inference_queueing: Holds for single-resource (memory-constrained) admission control where each item has one reduced-quality variant with a proportional weight multiplier m and a fractional value loss a satisfying m + a < 1, compared against the fractional LP relaxation rather than a full-horizon MDP optimum. The inertness result (m <= 1/2) is a general property of the increment weights and is workload-independent; the quantitative decoupling penalties are specific to workloads where accuracy is priced linearly and cheaply relative to revenue (validated on synthetic Azure NDm A100 v4 parameters and real BurstGPT windows, 2-3 service classes). Untested for three or more variants, convex or SLA-cliff accuracy penalties, and — most importantly — multi-resource settings: the model captures only quantization's memory effect, not its compute effect (INT8 = 2x TFLOPS), which could plausibly change the decoupling conclusion under a compute-binding regime.
gaussian-3mul-compiler: IEEE-754 binary64 complex multiplication on CPU, measured against exact rational truth over uniform/normal/lognormal/mixed-scale/large-magnitude input distributions. The contraction result assumes the compiler fuses one product per output part (the asymmetric contraction numpy emits); the mirrored fusion choice has a distinct distribution-dependent profile and is not covered by the conclusion. Imaginary part only, since 3-mul and 4-mul real parts are bit-identical. The median-vs-mean estimator lesson generalises to any heavy-tailed floating-point error comparison; the specific spread magnitudes are sample-size dependent.
mlir-fft-compiler: Pre-registered held-out validation of analytical GPU/compiler cost models where a rate parameter is under-identified by available calibration data; strongest when a hardware performance counter is a linear function of the parameter. Bounds: establishes experiment resolving power and design adequacy, not which law is true; assumes the candidate set is frozen and cannot exclude unmodeled functional forms consistent with the calibration point. +
alpha-factory: Offline portfolio/backtest research where a train/select/validate/hidden split is percentage-based and non-overlapping, and a headline OOS metric is computed on a window that does not touch the sealed suffix. The CSCV PBO gate is only weakly informative when the search collapses to few distinct strategies; use it as a sanity gate, not a full search-space overfitting test. +
azure_inference_queueing: Single-period LP-vs-IP relaxation gap of a multidimensional (m-constraint) 0/1 knapsack with nonnegative values and weights; verified for m=2 (memory×compute) on CPU-synthetic instances. Does not extend to full-horizon/rolling optimality, arrival dynamics, real serving traces, or any Azure production/SLA claim, and does not certify any greedy policy. +
alpha-factory: Offline portfolio-construction / ensemble-selection research where a treatment changes ensemble composition; single ETF-macro universe and one frozen candidate pool re-sliced by evaluation window (not re-evolved per fold), rolling-origin (not walk-forward-refit). Second-universe transfer (etf_macro_transfer) and hidden-test confirmation remain open. +
multi-lora-fusion: Memory-capacity cost knees for batched low-rank/GEMM serving fit from wall-clock timing; demonstrated CPU-only (float32, single-thread, torch.bmm). Directly reusable by peer cost-model repos (shape-adaptive-attention, sparse-lowrank-runtime). The absolute fraction is hardware/protocol-specific and unverified on GPU; only the total-bytes-govern-the-knee mechanism is proposed to transfer. +
complex-nn-signal: Synthetic single-tone LFM chirp classification, CPU, K=4/L=16, one optimizer, descriptive SESOI verdicts, not externally preregistered and no captured data. The full escape holds at SNR=0; the SNR=-5 case only partially closes. The adaptive-gate lesson generalizes to models with an optional/nuisance DOF and a data-scaled loss gradient, but the specific accuracy numbers and the exact nulling lambda are task- and SNR-specific. +
alpha-factory: alpha-factory and similar evolutionary alpha-discovery pipelines with an eligibility/diversifier admission track gated on standalone fitness and correlation-to-elite. Bounded here to two universes from one Yahoo-adjusted ETF vendor snapshot and a seed-only pool (max_generations=0); an evolved pool or cross-vendor data could still admit a decorrelated fitness-positive crisis sleeve. +
algebraic-ml-compiler: Only the recorded shapes, dtypes, software revision, and physical L40S GPU 3. +
sparse-lowrank-runtime: Sparse-vs-dense GPU dispatch where the crossover moves monotonically with a size covariate (FLOP scale); demonstrated on FP32 BSR on one L40S with a single winning held-out shape. Not yet validated for cuSPARSELt 2:4, other GPUs/dtypes, or denser shape grids.
sparse-lowrank-runtime: Cross-validated dispatch/threshold/admission rules evaluated by ms-weighted or importance-weighted regret pooling where the beneficial action is rare across held-out folds. Demonstrated on FP32 torch.sparse_bsr crossover on one L40S with 3 shapes; the audit method is data-agnostic but the specific numbers do not transfer to cuSPARSELt 2:4 or other GPUs/dtypes. +
rfuse modernizing userspace filesystem framework through scalable kernel userspace communication b3196db3 +
Userspace filesystems whose daemon logic can remain unchanged.
Limits: Exact workloads, hardware, and quantitative comparisons were not extracted from full text. +
contiguous device memory를 쓰는 embedded/mobile 시스템의 idle capacity 재활용에 해당한다. +
SSD and RAID liveness monitoring.
Limits: Generalization depends on training, SSD models, RAID layouts, and fault types represented in evaluation. +
Rowhammer 방어 메모리 컨트롤러.
Limits: 암호화 주소 매핑 하드웨어, 시뮬레이션·공격 모델, row-buffer locality 절충에 의존한다. +
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High-performance user-space all-flash arrays.
Limits: The design assumes SSD offload capabilities and its evaluated hardware/software stack. +
Many-core kernels and mmap-intensive applications.
Limits: Kernel-specific implementation and results on one 48-core platform; complex interval semantics remain workload-dependent. +
대형 멀티소켓 Linux 서버.
Limits: x86_64 4·8 socket 평가이며 sharer 메타데이터·Linux 변경 비용이 있다. +
Large vector-search services using computational storage.
Limits: Depends on SmartSSD hardware, learned pruning accuracy, index configuration, and evaluated datasets. +
Page-based remote/far memory on high-core-count servers.
Limits: Trades eviction accuracy for concurrency; results depend on far-memory latency and thread count. +