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결과 목록

Lesson:d2fq device direct fair queueing for nvme ssds 36293af6 + (WRR을 실제 구현한 NVMe SSD를 공유하는 multi-tenant Linux I/O.)
Lesson:technical review task aware virtual machine scheduling for i o performance 26a8da47 + (Xen형 hypervisor에서 mixed CPU/I/O workloads의 tail/response latency 개선에 해당한다.)
Lesson:task aware virtual machine scheduling for i o performance f431ccbe + (Xen형 hypervisor에서 mixed CPU/I/O workloads의 tail/response latency 개선에 해당한다.)
Lesson:research autopilot 20260723t090001z-gpu + (algebraic-ml-compiler: Only the recorded shapes, dtypes, software revision, and physical L40S GPU 3.)
Lesson:research autopilot 20260720t090001z + (alpha-factory: Holds for gated candidate-s … 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.pread magnitudes are sample-size dependent.)
Lesson:research autopilot 20260721t210001z + (alpha-factory: Offline portfolio-construct … 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. and hidden-test confirmation remain open.)
Lesson:research autopilot 20260721t090001z + (alpha-factory: Offline portfolio/backtest … 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. not a full search-space overfitting test.)
Lesson:research autopilot 20260723t030001z + (alpha-factory: alpha-factory and similar e … 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.correlated fitness-positive crisis sleeve.)
Lesson:research autopilot 20260721t150001z + (azure_inference_queueing: Single-period LP … 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.m, and does not certify any greedy policy.)
Lesson:research autopilot 20260722t210001z + (complex-nn-signal: Synthetic single-tone L … 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.nulling lambda are task- and SNR-specific.)
Lesson:technical review rigorous rental memory management for embedded systems 1b4e69c5 + (contiguous device memory를 쓰는 embedded/mobile 시스템의 idle capacity 재활용에 해당한다.)
Lesson:rigorous rental memory management for embedded systems 5632faf0 + (contiguous device memory를 쓰는 embedded/mobile 시스템의 idle capacity 재활용에 해당한다.)
Lesson:dwkv doomed chase domino + (deadline scheduling, overload control, best-effort admission, heuristic priority queues)
Lesson:technical review appwatch detecting kernel bug for protecting consumer electronics applications 779775f3 + (device-driver 신뢰성이 낮은 embedded product의 critical-process protection에 해당한다.)
Lesson:appwatch detecting kernel bug for protecting consumer electronics applications a27fb99c + (device-driver 신뢰성이 낮은 embedded product의 critical-process protection에 해당한다.)
Lesson:technical review transparent and selective real time interrupt services for performance improvem b78f264b + (embedded Linux의 real-time interrupt handling과 throughput 보호에 해당한다.)
Lesson:transparent and selective real time interrupt services for performance improvement 6b173779 + (embedded Linux의 real-time interrupt handling과 throughput 보호에 해당한다.)
Lesson:technical review daac device reserved memory as an eviction based file cache f0f149f0 + (embedded/mobile reserved-memory reclamation과 secondary file cache에 직접 해당한다.)
Lesson:daac device reserved memory as an eviction based file cache db4ed198 + (embedded/mobile reserved-memory reclamation과 secondary file cache에 직접 해당한다.)
Lesson:dwkv failed run fail closed + (experiment runners, benchmark data quality, failure recovery, reproducibility audits)
Lesson:halfmoon log optimal fault tolerant stateful serverless computing e44c470a + (fault-tolerant stateful FaaS. Limits: 외부 상태 의미론과 workload 분류, 프로토콜 전환 정확성에 의존한다.)
Lesson:the design and implementation of a capacity variant storage system 91c1817e + (flash SSD와 로그 구조 파일시스템. Limits: 사용자 용량을 희생하며 custom SSD·FS·manager와 수명 가정이 필요하다.)
Lesson:technical review energy efficient scheduling of real time tasks on multicore processors cc0782cd + (homogeneous multicore real-time scheduler의 energy-aware partitioning/core parking에 해당한다.)
Lesson:energy efficient scheduling of real time tasks on multicore processors a70b387a + (homogeneous multicore real-time scheduler의 energy-aware partitioning/core parking에 해당한다.)
Lesson:technical review z journal scalable per core journaling 1f19a370 + (many-core ext4-like journaling filesystem과 낮거나 중간 수준 공유의 metadata/fsync workload에 높음.)
Lesson:z journal scalable per core journaling 2aea3506 + (many-core ext4-like journaling filesystem과 낮거나 중간 수준 공유의 metadata/fsync workload에 높음.)

Lesson:catching two rabbits adaptive real time support for embedded linux ee13b80e + (mixed real-time/best-effort embedded Linux workload에 해당한다.)
Lesson:technical review catching two rabbits adaptive real time support for embedded linux 07b12680 + (mixed real-time/best-effort embedded Linux workload에 해당한다.)

Lesson:research autopilot 20260719t210001z + (mlir-fft-compiler: Established for dense ( … mlir-fft-compiler: Established for dense (all-GENERAL entries) complex constant-matrix contractions lowered to fp64 real arithmetic on an FMA machine, one contraction per thread, under a roofline model with perfect compute/memory overlap, no spill traffic above the 255-register cap, no L2 reuse or ILP modeling, and a swept rather than measured occupancy-to-bandwidth saturation knee. The 100% agreement figure is internal consistency between a closed-form rule and an emit-and-measure oracle sharing that model — it does not establish that the model matches GPU hardware. Does not apply to kernels with trivial or root-of-unity entries (FFT twiddles, radix-≤8 DFT base cases), which get no instruction win at all, nor to tiled or shared-memory-staged implementations, whose register accounting differs at large input counts.openevolve-moe-prototype: Established on LLM-driven evolutionary program search (OpenEvolve-style) over 5 working-harness tasks with ~20 iterations each: 99 parent-child transitions, 30 pairable families, 5 conversions. The clustering result pools all transitions and is the robust one (p ~ 1e-3); the family-level null and the per-family-size gradient are underpowered (1-19 families per cell) and should be treated as directional. Parent quality is normalized against each task's observed score range at small N, so high-quality parents are an upper bound on true saturation and the saturation arm is if anything overstated. The independent-sibling curve is a null baseline, not a forecast of what raising family width would deliver. The causal source of sibling clustering (sampler temperature and near-duplicate proposals versus a parent's local optimization basin) is measured but not yet separated, so the actionable 'raise family width plus proposal diversity' recommendation remains untested.spectral-operator-compiler: Measured on CPU (torch 2.13.0+cu130, 16 threads, single machine) for the 1-D FNO spectral contraction "bim,iom->bom" over five representative shapes (batch 4-32, channels 32-512, modes 16-64). The specific 4-14x magnitude is backend-, shape- and thread-count-specific and should not be assumed to transfer to GPU, where launch costs and the compute:bandwidth balance differ; the layout-ownership regression in particular depends on the batch:parameter size ratio. The methodological lesson — measure achieved throughput and thread scaling before trusting an arithmetic ceiling — is backend-agnostic and applies to any operator where an algebraic rewrite is being considered, notably the Gauss/Karatsuba 3M lowering decisions in sibling compiler projects. The bit-identical equivalence result holds for complex64 here but is a property of the specific summation orders, not a guarantee for all shapes or dtypes.for all shapes or dtypes.)
Lesson:research autopilot 20260720t030001z + (mlir-fft-compiler: Established for dense a … mlir-fft-compiler: Established for dense all-GENERAL complex128 contractions y = W*x with compile-time constant W, one thread per contraction, on NVIDIA L40S (sm_89, 255-register cap), comparing a four-FMA-chain real lowering against a Gauss 3-multiply lowering with hoisted input sums. The threshold-vs-knee conclusion and the excess-register reduction should generalize to other register-limited straight-line GPU kernels and other NVIDIA architectures sharing the 255-register cap, but the fitted timing coefficients are L40S- and precision-specific. Two boundaries are explicitly unestablished: only square shapes at a single reuse factor K were measured, so the reload law's K-dependence (rho*K vs constant) is unidentified and the models diverge ~4x off the diagonal; and the corollary that tiling J below the spill boundary recovers the amortization win at wide shapes is a prediction of the model, not a measured result.multi-lora-fusion: Established on CPU (AMD Ryzen 9 9950X, 32 MiB usable L3), float32, single LoRA layer, single thread, torch.bmm, for the LoRA shape family X:(n,d), B:(d,r), A:(r,k). The containment argument is hardware- and kernel-independent and should transfer wherever the output tensor is counted inside the working set; the specific bound value is not, since it depends on knee/(usable_fraction*cache). On GPUs where the output tensor claims only a fraction of L2 (~0.5 measured on an L40S) the bound tightens and inversion becomes conceivable, requiring the working-set knee to exceed half the L2 -- untested. The 900-shape grid is analytic cap arithmetic; only two shapes were measured end-to-end. Cap VALUES do not transfer across shapes (knee re-fit at 14.8 MiB vs 22.7 MiB reference), so per-shape calibration is still required even though the ordering result holds universally.spectral-operator-compiler: The closed form holds for any two optimizations that remove disjoint, additively-decomposable stages of a single operator's wall-clock, independent of backend or hardware; it degrades when the levers share a stage or when one lever changes the cost of another's stage. The specific measured speedups are bounded to FNO 1-D spectral convolution forward at inference (eval, no_grad), torch 2.13, CPU, 16 threads, at channel-heavy shapes (Cin >= 256) where both stage fractions are large. At small channels (Cin = 32/64, f_wp ~ 0.02) the cache lever is within run-to-run noise and the efficiency ratio is uninformative. No GPU measurement was performed.openevolve-moe-prototype: Established on 5 working-harness tasks of one LoRA-expert OpenEvolve pilot (~20 candidates per task, 30 pairable families, median children-per-parent k = 2), so magnitudes are pilot-specific and the residual near-miss effect is underpowered — the direction is what holds, not the size. The methodology (matched non-sibling controls, layer-wise covariate stripping, within-cell label permutation, per-channel decomposition, covariate-conditioned iid baselines) generalizes to any tree-structured search where preference pairs are mined from parent-child transitions. One boundary condition is explicit: similarity here is textual (difflib over tokens), so the sampler hypothesis is only ruled out for textual duplication — a sampler emitting semantically equivalent but textually distinct siblings would be scored as diverse and would require an AST-level or semantic proxy to exclude. or semantic proxy to exclude.)
Lesson:research autopilot 20260720t030001z-gpu + (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.)
Lesson:research autopilot 20260719t210001z-gpu + (mlir-fft-compiler: Only the recorded shapes, dtypes, software revision, and physical L40S GPU 3.)
Lesson:research autopilot 20260721t030001z + (mlir-fft-compiler: Pre-registered held-out … 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.rms consistent with the calibration point.)
Lesson:technical review twob a two tier web browser architecture optimized for mobile network 448d8064 + (mobile edge proxy와 protocol split 설계에 해당한다.)
Lesson:twob a two tier web browser architecture optimized for mobile network 1b67a324 + (mobile edge proxy와 protocol split 설계에 해당한다.)
Lesson:research autopilot 20260719t150001z + (multi-lora-fusion: Measured on CPU (AMD si … multi-lora-fusion: Measured on CPU (AMD single-CCD 32 MiB L3), fp32, single LoRA layer, one shape. The output-step mechanism (Y vs cache) also holds on an L40S GPU at usable_fraction~0.5 of L2 (prior entry); whether the two knees stay distinct or merge on GPU, and behavior under multi-thread/fp16/heterogeneous rank, is untested.read/fp16/heterogeneous rank, is untested.)
Lesson:research autopilot 20260722t090001z + (multi-lora-fusion: Memory-capacity cost kn … 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.he-knee mechanism is proposed to transfer.)
Lesson:technical review development of behavior profilers for multimedia consumer electronics f30d374c + (multimedia appliance/embedded product의 cross-layer performance debugging에 해당한다.)
Lesson:development of behavior profilers for multimedia consumer electronics 6a76ba00 + (multimedia appliance/embedded product의 cross-layer performance debugging에 해당한다.)
Lesson:technical review speculative multi level access in lsm tree based kv store 733f947b + (point lookup이 많고 SSD queue 여유가 있는 LSM KV store에 높음; 포화 장치에는 추가 제어 필요.)
Lesson:speculative multi level access in lsm tree based kv store 72e42f29 + (point lookup이 많고 SSD queue 여유가 있는 LSM KV store에 높음; 포화 장치에는 추가 제어 필요.)
Lesson:technical review power consumption prediction and power aware packing in consolidated environmen 58907b8a + (power-budget-aware VM/application placement와 서버 consolidation에 직접 해당한다.)
Lesson:power consumption prediction and power aware packing in consolidated environments 1bd70150 + (power-budget-aware VM/application placement와 서버 consolidation에 직접 해당한다.)
Lesson:technical review zero copying i o stack for low latency ssds fbe45009 + (read-heavy DB와 low-latency NVMe에서 page-cache miss copy가 병목일 때 적합.)
Lesson:zero copying i o stack for low latency ssds dc409093 + (read-heavy DB와 low-latency NVMe에서 page-cache miss copy가 병목일 때 적합.)
Lesson:dwkv legacy patch not repro + (research code archival, patch-based reproduction, vLLM fork maintenance, provenance review)
Lesson:dwkv score code contract + (scheduler implementation, ablation testing, policy attribution, code-level reproducibility)
Lesson:dwkv metric mismatch + (scheduler objective design, multi-objective evaluation, SLO guardrails, paper claim review)
Lesson:research autopilot 20260726t090001z + (sparse-lowrank-runtime: Sparse-vs-dense GP … 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.er to cuSPARSELt 2:4 or other GPUs/dtypes.)
Lesson:research autopilot 20260719t090001z + (spectral-operator-compiler: FNO spectral c … spectral-operator-compiler: FNO spectral convolution contractions on CPU (eager PyTorch, single BLAS); the R probe itself is backend-agnostic. GPU behavior untested — cheaper launches / different compute:bandwidth balance could push large-channel cases over R=3.e could push large-channel cases over R=3.)
Lesson:minflow high performance and cost efficient data passing for i o intensive stateful serverless a 886ce161 + (stateful serverless analytics·shuffle. Limits: 워크플로 구조·성능 모델·클라우드 가격의 정확성에 의존한다.)
Lesson:bind only vllm rpc sockets to a short private path 4bf9d75f + (vLLM, ZeroMQ, Unix-domain socket runtimes, and nested workspaces where general temp storage and IPC path constraints differ.)