속성:Applicability
이 메모가 맞는 조건과 예외를 적습니다.
p
Applies to Python virtual environments, safe-path subprocesses, tmux launchers, and experiment runners that normalize executable paths. +
Applies to vLLM, ZeroMQ, multiprocessing, and other Unix-socket runtimes launched from deeply nested workspaces or containers. +
Memory-constrained mobile systems with flash-backed swapping/writeback.
Limits: Reported evaluation is mobile/Android-specific; energy and flash-wear effects were not in the accessible abstract. +
GPU LLM serving that mixes ongoing decode with new prompt prefill.
Limits: Requires custom attention kernels and careful resource partitioning; gains depend on prefill/decode mix. +
Secure DDR5 memory systems combining ECC and integrity checks.
Limits: Assumes a MAC+ECC secure-memory setting; performance, iteration latency, and all fault assumptions were not verified from full text. +
power-budget-aware VM/application placement와 서버 consolidation에 직접 해당한다. +
DRAM architecture, ECC, repair, and future-system reliability planning.
Limits: Predictions extrapolate public production logs through assumed component mappings and future-device parameters. +
PM+SSD 계층형 KV 저장소.
Limits: 현재 단종된 Optane 계열 하드웨어 의존성이 크고 초록에 세부 baseline이 없다. +
서지 추적과 후속 1차 출처 확보 작업에만 적용합니다.
Limits: 공개 primary-source paper, publisher record, DOI, author manuscript 또는 official program entry를 확인하지 못했습니다. +
클라우드 네이티브 secure container.
Limits: x86/KVM 및 게스트 수정, shadow coherence 구현에 의존하고 공개 정량치가 제한적이다. +
r
Linux filesystems on high-bandwidth SSDs, especially partial/unaligned writes.
Limits: Headline results focus on evaluated filesystems/devices; adds a new buffering/flushing architecture. +
Fan-out 또는 sharded read 요청을 수행하는 scale-out database에 해당한다. +
작은 persistent 또는 non-volatile write cache를 database logging 및 journaling과 함께 사용하는 시스템에 높다. +
NVIDIA L40S GPU 3, 기록된 shape·dtype·커밋·벤치마크 구현에만 적용한다. 다른 GPU 세대, batch 분포, sparsity 구조, 실제 fused attention kernel에는 재측정 없이 적용하지 않는다. +
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. +
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. +
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.
mlir-fft-compiler: Only the recorded shapes, dtypes, software revision, and physical L40S GPU 3. +
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.
Only the recorded repositories/commits, fp32 multi-LoRA GPU3 run, and exploratory MLIR compile environment. Timings are device- and shape-specific. +