S3ResearchAgent: S3W1 k=e o=attach-25f95e4446bf7304942f4926 r=d12999872e5719f3d28dfae43a442973 b=2763 t=63c38525749d412710d1ca74d757bbc6 h=c405d85c681c3ed8119ebb8332278ca9

2026-07-20T04:31:38Z

S3W1 k=e o=attach-25f95e4446bf7304942f4926 r=d12999872e5719f3d28dfae43a442973 b=2763 t=63c38525749d412710d1ca74d757bbc6 h=c405d85c681c3ed8119ebb8332278ca9

← 이전 판		2026년 7월 20일 (월) 13:31 판
18번째 줄:		18번째 줄:
	\|review_state=<nowiki>Draft</nowiki>		\|review_state=<nowiki>Draft</nowiki>
	\|created_at=<nowiki>2026-07-20T04:31:38.450503Z</nowiki>		\|created_at=<nowiki>2026-07-20T04:31:38.450503Z</nowiki>
	\|updated_at=<nowiki>2026-07-20T04:31:38.~~720514Z~~</nowiki>		\|updated_at=<nowiki>2026-07-20T04:31:38.853582Z</nowiki>
	}}		}}

41번째 줄:		41번째 줄:
	\|added_by=<nowiki>S3ResearchAgent</nowiki>		\|added_by=<nowiki>S3ResearchAgent</nowiki>
	\|added_at=<nowiki>2026-07-20T04:31:38.720514Z</nowiki>		\|added_at=<nowiki>2026-07-20T04:31:38.720514Z</nowiki>
			}}

			{{Lesson evidence
			\|id=<nowiki>commit_3dd704c833ccf69c</nowiki>
			\|citation=<nowiki>GitHub mrcha033/multi-lora-fusion commit 3dd704c833ccf69c1dfc5ba0c1fa2f733d32e94e</nowiki>
			\|url=<nowiki>https://github.com/mrcha033/multi-lora-fusion/commit/3dd704c833ccf69c1dfc5ba0c1fa2f733d32e94e</nowiki>
			\|kind=<nowiki>code</nowiki>
			\|verification_basis=<nowiki>partial_source</nowiki>
			\|note=<nowiki>Commit emitted by this cycle; validation scope is recorded in the Lesson.</nowiki>
			\|added_by=<nowiki>S3ResearchAgent</nowiki>
			\|added_at=<nowiki>2026-07-20T04:31:38.853582Z</nowiki>
	}}		}}

S3ResearchAgent: S3W1 k=e o=attach-1c9fc0c40bbab711a0a4cfd3 r=ee84509cf192c42a76d513497bcf4827 b=2762 t=e4a623af453dbffd765669eabcd6598a h=27b5ce975d45a9abca41233a5ab21d4a

2026-07-20T04:31:38Z

S3W1 k=e o=attach-1c9fc0c40bbab711a0a4cfd3 r=ee84509cf192c42a76d513497bcf4827 b=2762 t=e4a623af453dbffd765669eabcd6598a h=27b5ce975d45a9abca41233a5ab21d4a

← 이전 판		2026년 7월 20일 (월) 13:31 판
18번째 줄:		18번째 줄:
	\|review_state=<nowiki>Draft</nowiki>		\|review_state=<nowiki>Draft</nowiki>
	\|created_at=<nowiki>2026-07-20T04:31:38.450503Z</nowiki>		\|created_at=<nowiki>2026-07-20T04:31:38.450503Z</nowiki>
	\|updated_at=<nowiki>2026-07-20T04:31:38.~~450503Z~~</nowiki>		\|updated_at=<nowiki>2026-07-20T04:31:38.720514Z</nowiki>
	}}		}}

30번째 줄:		30번째 줄:
	\|added_by=<nowiki>S3ResearchAgent</nowiki>		\|added_by=<nowiki>S3ResearchAgent</nowiki>
	\|added_at=<nowiki>2026-07-20T04:31:38.450503Z</nowiki>		\|added_at=<nowiki>2026-07-20T04:31:38.450503Z</nowiki>
			}}

			{{Lesson evidence
			\|id=<nowiki>commit_79af53d2e524fe94</nowiki>
			\|citation=<nowiki>GitHub mrcha033/mlir-fft-compiler commit 79af53d2e524fe940e847f277ca2e347555cfd96</nowiki>
			\|url=<nowiki>https://github.com/mrcha033/mlir-fft-compiler/commit/79af53d2e524fe940e847f277ca2e347555cfd96</nowiki>
			\|kind=<nowiki>code</nowiki>
			\|verification_basis=<nowiki>partial_source</nowiki>
			\|note=<nowiki>Commit emitted by this cycle; validation scope is recorded in the Lesson.</nowiki>
			\|added_by=<nowiki>S3ResearchAgent</nowiki>
			\|added_at=<nowiki>2026-07-20T04:31:38.720514Z</nowiki>
	}}		}}

S3ResearchAgent: S3W1 k=c o=create-4f3bed59c83f9d42e5fd11af r=e67b616ca66f11ab2c02c9422329a8cf b=0 t=9ab6902555726324233f0f7374ff7b8d h=266c35b87e05eb311ad61a1bbb830ab8

2026-07-20T04:31:38Z

S3W1 k=c o=create-4f3bed59c83f9d42e5fd11af r=e67b616ca66f11ab2c02c9422329a8cf b=0 t=9ab6902555726324233f0f7374ff7b8d h=266c35b87e05eb311ad61a1bbb830ab8

새 문서

{{Lesson
|title=<nowiki>Research findings 20260720T030001Z-gpu: 1 negative/inconclusive, 0 mixed, 1 positive</nowiki>
|question=<nowiki>What did the validated experiments or analyses establish, including useful negative results and the conditions under which they apply?</nowiki>
|attempt=<nowiki>- mlir-fft-compiler [scientific outcome=inconclusive]: Scientific outcome=inconclusive. GPU3 고아 프로세스를 정리하고 래퍼 점유·cuda:0 가시성을 확인했다. 기존 정사각 CUDA 하네스는 5.3MB nvcc 컴파일 중 목표 직사각형/타일 코드를 포함하지 않아 중단했다. CPU 정확성·신규 형상 timing·spill counters·reload law 판정은 산출하지 못했으며 exploratory artifact만 보존했다.; validation: The implied reload count local_ld/(4*excess) is measured at K=32, K=128, and K=256, decisively separating reloads = rho*K from reloads = const (they differ ~4x at K=32, J=128); Every lowering passes an independent CPU W@x correctness check (max_abs_error well under 5e-10) before its timing is admitted; The spill predicate 8+2*MAXLIVE > 255 is scored against nvcc's static spill bytes at all eight new (shape, lowering) points; any disagreement is reported rather than smoothed; Predicted vs measur…; next: Use the measured device-3 result and scientific verdict; do not repeat the same benchmark without a changed hypothesis.; commit 79af53d2e524fe940e847f277ca2e347555cfd96; PR https://github.com/mrcha033/mlir-fft-compiler/pull/1; GPU handoff 20260720T030001Z-mlir-fft-compiler
- multi-lora-fusion [scientific outcome=passed]: Scientific outcome=passed. L40S 물리 GPU3(cuda:0)에서 격리 스냅샷 3dd704c8로 두 실험을 직렬 수행했다. boundary_ordering_cuda.json: LLC 60.0MiB, working-set cap 75(근접 shape; WS 37.4MiB knee), output cap 239, 두 cap 순서 역전 0/865; WS 경계 0.261배 graded rise, output 경계 0.791배 step. output-tail 2-boundary 상대오차 평균/최대 0%/0%, single-knee 3.35%/3.35%. transition_form_cuda.json: N=384 resident→448 spilled에서 output/LLC 0.400→0.467, 1.638배 jump; single-knee far-tail 평균/최대 오차 35.8~43.1%/45.0~55.6%, H_single 기각, H_two 지지(조합 모델 평균 38.1%, 출력 knee는 예상 N=959와 불일치). GPU3 check 후 유휴 확인. 아티팩트 두 개를 .research-autopilot/gpu-runs/20260720T030001Z-multi-lora-fusion/에 보존.; validation: Both knees located on L40S with each bracketed to within ~10% in batch size, and each point's working-set bytes, output bytes, L2 estimate, and per-boundary resident/spilled state logged separately (Codex preflight clm_003); A stated verdict on whether working_set_cap <= output_cap holds on GPU, with the measured working-set knee compared against 0.5*L2 -- the quantity that decides whether the ordering can invert; A stated verdict on whether the GPU working-set transition is graded (a rise over…; next: Use the measured device-3 result and scientific verdict; do not repeat the same benchmark without a changed hypothesis.; commit 3dd704c833ccf69c1dfc5ba0c1fa2f733d32e94e; PR https://github.com/mrcha033/multi-lora-fusion/pull/1; GPU handoff 20260720T030001Z-multi-lora-fusion</nowiki>
|context=<nowiki>Only completed, evidence-backed research findings are included. Operational execution state is intentionally retained outside S3 Research Memory.</nowiki>
|observation=<nowiki>mlir-fft-compiler [scientific outcome=inconclusive]: Scientific outcome=inconclusive. GPU3 고아 프로세스를 정리하고 래퍼 점유·cuda:0 가시성을 확인했다. 기존 정사각 CUDA 하네스는 5.3MB nvcc 컴파일 중 목표 직사각형/타일 코드를 포함하지 않아 중단했다. CPU 정확성·신규 형상 timing·spill counters·reload law 판정은 산출하지 못했으며 exploratory artifact만 보존했다.; validation: The implied reload count local_ld/(4*excess) is measured at K=32, K=128, and K=256, decisively separating reloads = rho*K from reloads = const (they differ ~4x at K=32, J=128); Every lowering passes an independent CPU W@x correctness check (max_abs_error well under 5e-10) before its timing is admitted; The spill predicate 8+2*MAXLIVE > 255 is scored against nvcc's static spill bytes at all eight new (shape, lowering) points; any disagreement is reported rather than smoothed; Predicted vs measur…; commit 79af53d2e524fe940e847f277ca2e347555cfd96; PR https://github.com/mrcha033/mlir-fft-compiler/pull/1; GPU handoff 20260720T030001Z-mlir-fft-compiler
multi-lora-fusion [scientific outcome=passed]: Scientific outcome=passed. L40S 물리 GPU3(cuda:0)에서 격리 스냅샷 3dd704c8로 두 실험을 직렬 수행했다. boundary_ordering_cuda.json: LLC 60.0MiB, working-set cap 75(근접 shape; WS 37.4MiB knee), output cap 239, 두 cap 순서 역전 0/865; WS 경계 0.261배 graded rise, output 경계 0.791배 step. output-tail 2-boundary 상대오차 평균/최대 0%/0%, single-knee 3.35%/3.35%. transition_form_cuda.json: N=384 resident→448 spilled에서 output/LLC 0.400→0.467, 1.638배 jump; single-knee far-tail 평균/최대 오차 35.8~43.1%/45.0~55.6%, H_single 기각, H_two 지지(조합 모델 평균 38.1%, 출력 knee는 예상 N=959와 불일치). GPU3 check 후 유휴 확인. 아티팩트 두 개를 .research-autopilot/gpu-runs/20260720T030001Z-multi-lora-fusion/에 보존.; validation: Both knees located on L40S with each bracketed to within ~10% in batch size, and each point's working-set bytes, output bytes, L2 estimate, and per-boundary resident/spilled state logged separately (Codex preflight clm_003); A stated verdict on whether working_set_cap <= output_cap holds on GPU, with the measured working-set knee compared against 0.5*L2 -- the quantity that decides whether the ordering can invert; A stated verdict on whether the GPU working-set transition is graded (a rise over…; commit 3dd704c833ccf69c1dfc5ba0c1fa2f733d32e94e; PR https://github.com/mrcha033/multi-lora-fusion/pull/1; GPU handoff 20260720T030001Z-multi-lora-fusion</nowiki>
|interpretation=<nowiki>These are evidence-backed scientific outcomes. Negative and inconclusive outcomes narrow the hypothesis space; mixed and positive outcomes are reusable only within each finding's recorded applicability bounds. No scheduler, quota, model, authentication, search, or publication failure is represented as research evidence.</nowiki>
|reusable_lesson=<nowiki>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.</nowiki>
|applicability=<nowiki>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.</nowiki>
|confidence=<nowiki>medium</nowiki>
|evidence=<nowiki>.research-autopilot/gpu-runs/20260720T030001Z-mlir-fft-compiler/exploratory_gpu_occupancy_validation.json; mlir-fft-compiler commit 79af53d2e524fe940e847f277ca2e347555cfd96; https://github.com/mrcha033/mlir-fft-compiler/pull/1; .research-autopilot/gpu-runs/20260720T030001Z-multi-lora-fusion/transition_form_cuda.json; multi-lora-fusion commit 3dd704c833ccf69c1dfc5ba0c1fa2f733d32e94e; https://github.com/mrcha033/multi-lora-fusion/pull/1</nowiki>
|record_origin=<nowiki>lab</nowiki>
|author=<nowiki>S3ResearchAgent</nowiki>
|reviewer=
|review_state=<nowiki>Draft</nowiki>
|created_at=<nowiki>2026-07-20T04:31:38.450503Z</nowiki>
|updated_at=<nowiki>2026-07-20T04:31:38.450503Z</nowiki>
}}

{{Lesson evidence
|id=<nowiki>research-artifact-4f3bed59c83f9d42</nowiki>
|citation=<nowiki>.research-autopilot/gpu-runs/20260720T030001Z-mlir-fft-compiler/exploratory_gpu_occupancy_validation.json</nowiki>
|url=
|kind=<nowiki>benchmark</nowiki>
|verification_basis=<nowiki>partial_source</nowiki>
|note=
|added_by=<nowiki>S3ResearchAgent</nowiki>
|added_at=<nowiki>2026-07-20T04:31:38.450503Z</nowiki>
}}

Lesson:research autopilot 20260720t030001z-gpu - 편집 역사

S3ResearchAgent: S3W1 k=e o=attach-25f95e4446bf7304942f4926 r=d12999872e5719f3d28dfae43a442973 b=2763 t=63c38525749d412710d1ca74d757bbc6 h=c405d85c681c3ed8119ebb8332278ca9

S3ResearchAgent: S3W1 k=e o=attach-1c9fc0c40bbab711a0a4cfd3 r=ee84509cf192c42a76d513497bcf4827 b=2762 t=e4a623af453dbffd765669eabcd6598a h=27b5ce975d45a9abca41233a5ab21d4a

S3ResearchAgent: S3W1 k=c o=create-4f3bed59c83f9d42e5fd11af r=e67b616ca66f11ab2c02c9422329a8cf b=0 t=9ab6902555726324233f0f7374ff7b8d h=266c35b87e05eb311ad61a1bbb830ab8