속성:Evidence overview
관련 자료를 짧게 정리합니다. 자세한 인용은 자료 항목에 따로 적습니다.
p
Failed decision SHA-256 e1c0e98e2de2fd78fa21acd57aec899ea13f94ffc5707128f9ceb1d87c7ac7eb; child log SHA-256 6c3bc4bd550b39a5ef4657a339f45712448d9756cbf2e949a1210e52bdb75d94; no GPU compute process. +
a02 decision SHA-256 b8a1ebaed3c48842cd10768235d66ff1985c2cc8d9c20bdfd046ae4db72996f4; Qwen log SHA-256 85237dd99be24c2c20386d96ab756d463b64540eb282bfc45629716b698992fc; no GPU compute process. +
PMR: Fast Application Response via Parallel Memory Reclaim on Mobile Devices. USENIX ATC 2025. +
POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference. ASPLOS 2025. +
Polymorphic Error Correction. MICRO 2024. +
Publication record 1: Jeonghwan Choi, Sriram Govindan, Jinkyu Jeong, Bhuvan Urgaonkar, and Anand Sivasubramaniam, "Power Consumption Prediction and Power-Aware Packing in Consolidated Environments," IEEE Transactions on Computer, vol. 59, no. 12, pp. 1640-1654, Dec. 2010 +
Predicting Future-System Reliability with a Component-Level DRAM Fault Model. MICRO 2023. +
Prism: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices. ASPLOS 2023. +
PruneOff: Offloading LSM Read-Path Pruning in Disaggregated Key-Value Stores. +
PVM: Efficient Shadow Paging for Deploying Secure Containers in Cloud-native Environment. SOSP 2023. +
r
Yekang Zhan; Tianze Wang; Zheng Peng; Haichuan Hu; Jiahao Wu; Xiangrui Yang; Qiang Cao; Hong Jiang; Jie Yao. Rearchitecting Buffered I/O in the Era of High-Bandwidth SSDs. FAST, 2026.
Source: https://www.usenix.org/conference/fast26/presentation/zhan
Verification basis: official_abstract.
Canonical evidence ID: canonical-paper-v2-70b23de3 +
Publication record 1: Hyungil Jo, Sung-hun Kim, Sangwook Kim, Jinkyu Jeong, and Joonwon Lee, "Request-aware Cooperative I/O Scheduling for Scale-out Database Applications," in Proceedings of the 9th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage'17), Santa Clara, CA, USA, July 10 - 11, 2017. +
Publication record 1: Sangwook Kim, Hwanju Kim, Sang-Hoon Kim, Joonwon Lee, and Jinkyu Jeong, "Request-Oriented Durable Write Caching for Application Performance," in Proceedings of The 2015 USENIX Annual Technical Conference (USENIX ATC 15), Santa Clara, CA, USA, July 8-10, 2015. +
SHA-256으로 식별한 여섯 개의 전체 JSON 벤치마크 산출물이 수치 오차, latency/speedup, crossover 또는 회귀를 뒷받침한다. 각 산출물은 연결된 저장소 커밋을 명시한다. 운영 사이클 상태나 스케줄러 로그는 이 결론의 근거가 아니다. +
torch 2.13.0 CPU, best-of-9×50: FNO R = 1.2/1.3/1.5/1.7/2.6 across (B,Cin,Cout,M) from (32,32,32,16) to (4,512,512,32); k_bound 0.41→0.88, all <1; square GEMM control: R = 2.4/3.2/3.7/4.0 at n=256/512/1024/2048, reaching multiply-bound; benchmarks/results/multiply_boundedness_cpu.json; src/multiply_boundedness.py; 15 tests; full suite 279 passing; channel-heavy R stabilized at ~2.6 across 4 repeat runs (earlier 3.96 was timing noise); spectral-operator-compiler commit 0bf1148b9cc8731097d190863f60685a2befd46c; https://github.com/mrcha033/spectral-operator-compiler/pull/1 +
Reference shape seq_len=16,d=1024,r=16,k=1024, fp32, single-thread, torch 2.13, CPU L3=32 MiB; models fit on N<=256, scored on far tail N=320..768; Second knee empirically at N 504->512 (Y/L3 0.984->1.000), jump ~1.49x, bracketed within one scan step, reproducible to the sample; analytic max_batch_before_output_spill predicts N=511 (in bracket); Far-tail mean rel error: step 25-27%, resident_fraction 16-19%, resident_fraction_sqrt 16% (all under-predict ~40% at N=512); ~11% inter-form spread << ~44% shared max error at the second knee; Two-boundary model (resident_fraction working-set knee x measured output step past cap): 0.8% mean / 2.0% max far-tail error; experiments/resolve_transition_form.py + committed transition_form_cpu.json + tests/test_transition_form.py; 526 tests pass; multi-lora-fusion commit b048fc0e78958e5c4a7b0b1a93a6cb9994b4eead; https://github.com/mrcha033/multi-lora-fusion/pull/1 +
src/lowering_policy.py: closed forms for instructions (4KJ vs 3KJ+J), FLOPs (8KJ−2K vs 6KJ−K+J), and MAXLIVE (2J+2 vs 3J+2), each checked against real scheduled instruction streams at all 100 grid shapes; experiments/results/lowering_policy.json: closed_forms_exact=true; 900 points, policy_agreement_pct=100.00, max_abs_advantage_error_pct=0.0, policy_max_regret_pct=0.00, naive_mean_regret_pct=7.82, naive_max_regret_pct=55.56, naive_harmful_pct=17.7; Decision census over the grid: occupancy-tied 609, occupancy-loss 159, no-reuse 90, compute-bound-win 42 — the amortization is the correct choice at only 4.7% of shapes, all with K ≥ 32; tests/test_lowering_policy.py: 368 tests including K-flatness of measured register pressure, the +J penalty and its K=1 boundary, field-by-field agreement of the closed-form cost with the measured evaluation, and oracle agreement across targets and knees; suite 1100 tests passing; Validation surfaced a genuine defect in the first closed form: an off-by-one at J=1 for both strategies, where a single input leaves no accumulation chain; mlir-fft-compiler commit 43e787be17ee558a95a44185fc17b758f0abacbb; https://github.com/mrcha033/mlir-fft-compiler/pull/1; src/conversion_quality_curve.py: normalized parent-quality coordinate q=(f-lo)/(hi-lo) over executable candidates, Wilson-interval binning, within-task stratified permutation tests, partial Spearman, iid-sibling conversion model, and a family-size-preserving label-reshuffle test; reports/phase_a1f_conversion_quality_curve_report.md and .json: full result tables over 5 working-harness tasks, 99 transitions (92 executed), 30 pairable families, 5 converting; Confirmatory trends: positive rate vs parent quality rho = -0.57 (p = 5e-05); no-op rate vs parent quality rho = +0.57 (p = 5e-05); both by within-task permutation so between-task confounds cannot produce them; Quality-vs-time separation: partial rho -0.51 (quality given iteration) versus +0.01 (iteration given quality); Null result: converting versus all-improving families differ in mean parent quality by -0.014, permutation p = 0.94; Sibling clustering: iid model expects 9.2 converting families vs 5 observed; within-task family-size-preserving reshuffle expects 11.8 vs 5 observed, one-sided p = 0.001 over 20,000 permutations; Family-size gradient: observed conversion 10.5%/16.7%/25.0% at k = 2/3/4 against iid predictions 21.3%/40.1%/53.9%; tests/test_conversion_quality_curve.py: 53 tests (392 total) including exact family-by-family reconciliation with the prior phase, idle-ledger sum invariants, and known-answer statistics checks (planted effect, null, and a between-stratum confound the stratified test must reject); openevolve-moe-prototype commit dec037ba93c203f580c290cb50d291515398acef; https://github.com/mrcha033/openevolve-moe-prototype/pull/1; benchmarks/results/contraction_lowering_cpu.json — contraction sweep, weight-layout-ownership control, end-to-end forward comparison, and thread-scaling records across 5 FNO shapes; benchmarks/results/spectral_stage_profile_cpu.json — per-stage forward decomposition giving f_c = 0.50-0.89, f_fft = 0.01-0.13, f_wp up to 0.49, and the free/4-3 Amdahl ceilings; src/contraction_lowering.py — three equivalent lowerings, layout converters, in-harness equivalence assertion, GFLOP/s-reporting comparison harness; src/spectral_conv.py — SpectralConv1d weight_layout=(channel_leading\\|mode_leading), default unchanged; Contraction speedup over einsum, bmm_mode_leading: 4.36x (B32 C32 M16), 4.46x (C64), 10.86x (C128), 14.49x (C256), 5.75x (C512); End-to-end SpectralConv1d forward speedup: 1.86x, 2.55x, 5.67x, 1.87x, 1.71x with max_abs_err = 0.0 at every shape; Thread scaling at B=16 Cin=Cout=128 M=64 (GFLOP/s): einsum 62.5/73.3/83.2/82.2/70.5 vs bmm 102.2/201.4/398.4/599.1/615.7 at 1/2/4/8/16 threads; Weight-layout-ownership control, bmm_transpose_both: 2.84x, 3.19x, 7.42x, 0.64x, 0.38x — regressing below baseline at large channels; tests/test_contraction_lowering.py and tests/test_spectral_stage_profile.py — 51 tests covering equivalence at degenerate and truncated shapes, gradient preservation, throughput accounting, and Amdahl algebra; full suite 330 passed; spectral-operator-compiler commit 7175ff452fb060e71c6ba6cefcc7d3e5a5c140f8; https://github.com/mrcha033/spectral-operator-compiler/pull/1
/home/mrcha033/Researches/.research-autopilot/worktrees/mlir-fft-compiler/experiments/results/gpu_occupancy_validation.json; mlir-fft-compiler commit 43e787be17ee558a95a44185fc17b758f0abacbb; https://github.com/mrcha033/mlir-fft-compiler/pull/1 +
Achieved occupancy (sm__warps_active) across all four measured configurations: 14.34/19.87/16.21/15.57%, spread 5.53 pp, correctly ordering the faster lowering at 0/2 shapes; Parameter-free spill predicate 8+2*MAXLIVE > 255 matches nvcc's static spill bytes at 4/4 points (J=32: raw 140/204, 0 spill bytes observed; J=128: raw 524/780, 95,936/183,300 bytes observed); Implied reload counts local_ld/(4*excess): 87.29 (excess 525) vs 89.16 (excess 269), agreeing to 2.15% across two different schedules; predicted spill bytes within 1.7% of measured (184,128 vs 183,300; 94,344 vs 95,936); Advantage at K=J=32 (no-spill): measured +22.65%, spill model +24.22%, occupancy model -55.56%; at K=J=128 (both-spill): measured -22.08%, spill model -21.51%, occupancy model +24.85%; Measured dynamic instructions per problem at K=J=32 (3,180 and 4,172) within 2.5% of closed forms 3KJ+J=3,104 and 4KJ=4,096, confirming the instruction model transfers to hardware in the no-spill regime; Artifacts: src/spill_model.py, experiments/spill_calibration.py, experiments/results/spill_calibration.json, tests/test_spill_model.py (105 tests); full suite 1205 tests passing; mlir-fft-compiler commit 79af53d2e524fe940e847f277ca2e347555cfd96; https://github.com/mrcha033/mlir-fft-compiler/pull/1; 900-shape analytic grid (seq_len 8-256, hidden_dim 16-1024, rank 2-64, out_dim 512-16384): 0 ordering inversions; max continuous cap ratio 0.704 <= analytic bound 0.710 = knee/LLC (22.7 MiB knee, 32 MiB single-CCD L3); Direct measurement at the closest approach the machine admits (n=64,d=64,r=8,k=1024; caps 75 and 127, only 1.69x apart vs 5.68x for the reference shape), N=4..256, 25 repeats, median, single thread: mean us/request 27.56 below both caps, 29.42 between them (1.07x graded rise), 58.39 above both (1.98x sharp step); A 1.2x adjacent-point cliff detector returns None below the output cap despite that window crossing the working-set cap, while the sharpest jump in the full sweep brackets N=124->128 at Y/LLC = 0.97->1.00 -- only the output boundary is a cliff; the working-set boundary is a slope; Two-boundary composed model scores 0.8% mean / 1.8% max relative error on the output tail of a shape it was not fit on, versus 17.3% / 32.1% for a single-knee resident_fraction fit; Tolerance sweep: the relaxed cap crosses the output cap at 11.4% tolerance (output-dominated shape) and 22.9% (reference shape), confirming the scheduler-side min() is not degenerate; Reproduced across three independent runs (output step 1.90-2.03x); 627 tests pass including record-pinning tests over the committed JSON; multi-lora-fusion commit 3dd704c833ccf69c1dfc5ba0c1fa2f733d32e94e; https://github.com/mrcha033/multi-lora-fusion/pull/1; benchmarks/results/lever_composition_cpu.json: 2x2 factorial over 5 FNO shapes; s_both = 4.06-5.05x at Cin=256 and 7.84-8.29x at Cin=512 vs multiplicative nulls 2.60-2.69x and 2.99-3.61x; composition efficiencies 1.56-1.87 and 2.29-2.63 (two independent runs, torch 2.13 CPU, 16 threads); src/lever_composition.py: ideal_disjoint_efficiency encodes the closed form; amdahl_composition_prediction gives the a-priori stage-model null computed from the baseline profile without using the combined measurement; tests/test_lever_composition.py: 41 tests including strict-super-composition across a fraction grid, agreement with the closed form, and consistency with composing the two individual Amdahl speedups; closed form independently verified symbolically; All four factorial cells bit-identical at every shape (max_abs_err = 0.0) via assert_cells_equivalent, so the speedups are schedule changes rather than approximations; Full suite 371 passed (330 prior + 41 new); spectral-operator-compiler commit a2a50fb1f14b3cafb34c1d2ac37ffbb8f5eb63f0; https://github.com/mrcha033/spectral-operator-compiler/pull/1; src/sibling_concordance.py: matched-control sibling-excess estimator over 931 within-task child pairs (71 sibling, 860 non-sibling controls) from 5 working-harness pilot tasks, with cells refined one covariate at a time and a within-cell label-permutation test (20,000 permutations); reports/phase_a1g_sibling_concordance_report.md/.json: matching layers task_only +0.290 (p = 5e-05), similarity +0.285 (p = 1e-04), parent_quality +0.075 (p = 0.16), similarity_and_quality +0.078 (p = 0.16); Outcome-channel decomposition: both-positive raw excess +0.219 -> +0.023 quality-matched (89.5% absorbed); both-near-miss +0.084 -> +0.070 (16.3% absorbed); Family-level reshuffle with and without a quality stratum: 30 families, 5 observed converting, expected 11.81 (p = 0.0005) unstratified vs 9.35 (p = 0.0070) quality-stratified; tests/test_sibling_concordance.py: 49 tests (441 total, all passing), including a pure-covariate-confound case where the naive estimator reports excess > 0.7 and the matched estimator must report 0.0, its complement preserving a genuine within-stratum effect, and reconciliation of task set, family count, conversion count and sibling-pair count with the prior phase; openevolve-moe-prototype commit 11a77d13a64a2b8e5f7955f676071034a4d326a0; https://github.com/mrcha033/openevolve-moe-prototype/pull/1
/home/mrcha033/Researches/.research-autopilot/gpu-runs/20260720T030001Z-multi-lora-fusion/boundary_ordering_cuda.json; /home/mrcha033/Researches/.research-autopilot/gpu-runs/20260720T030001Z-multi-lora-fusion/transition_form_cuda.json; /home/mrcha033/Researches/.research-autopilot/gpu-runs/20260720T030001Z-mlir-fft-compiler/exploratory_gpu_occupancy_validation.json +