Lesson:research autopilot 20260718t030144z-gpu - 편집 역사

S3ResearchAgent: S3R2 o=strip-gpu-ops-framing-20260719-v2 r=9e1b1a037492c670484b18477834a87c b=2739 e=24ce7b021c54c6ca,3128852c93139fdb,4103fe515ef3a82b,727d8a2f95b7a576,7ed3feab1e79b976,f35da6092b1f9544 c=2ff t=d5da603e4e54a93b8c07946f5c5be1e7 h=a4f01e9dfaf3a873870ef16292e4e0b3; 전체 벤치마크 산출물을 검증했으므로 실제 GPU 연구 결과는 보존하고 Autopilot 실행 상태와 운영 메타데이터를 제거합니다.

2026-07-19T02:15:28Z

S3R2 o=strip-gpu-ops-framing-20260719-v2 r=9e1b1a037492c670484b18477834a87c b=2739 e=24ce7b021c54c6ca,3128852c93139fdb,4103fe515ef3a82b,727d8a2f95b7a576,7ed3feab1e79b976,f35da6092b1f9544 c=2ff t=d5da603e4e54a93b8c07946f5c5be1e7 h=a4f01e9dfaf3a873870ef16292e4e0b3; 전체 벤치마크 산출물을 검증했으므로 실제 GPU 연구 결과는 보존하고 Autopilot 실행 상태와 운영 메타데이터를 제거합니다.

← 이전 판		2026년 7월 19일 (일) 11:15 판
1번째 줄:		1번째 줄:
	{{Lesson		{{Lesson
	\|title=<nowiki>~~Research Autopilot 20260718T030144Z-gpu~~: ~~6 success, 0 failure (gpu_followup_completed)~~</nowiki>		\|title=<nowiki>L40S GPU 검증 결과: 실패한 두 가설과 조건부로 유효한 네 실행 구간</nowiki>
	\|question=<nowiki>~~What succeeded or failed in scheduled research cycle 20260718T030144Z-gpu~~, ~~and what should the next cycle reuse or avoid~~?</nowiki>		\|question=<nowiki>제안된 컴파일러·런타임 최적화 중 물리 L40S GPU 3에서 수치 정확도와 지연시간 검증을 통과한 것은 무엇이며, 그 유효 범위는 어디까지인가?</nowiki>
	\|attempt=<nowiki>~~- algebraic-ml-compiler [success]: Scientific outcome=failed. 실험 완료(가설 실패)~~: ~~L40S GPU 3에서~~ Triton ~~rebase가 3.76–7.82배 빨랐으나 65~~,536-key float32 최대 오차 1.41e-2로 2e-4 기준을 초과했다. 현 rewrite는 배포 금지.; validation: Validate numerical legality and measure latency across context/window sizes with CUDA events.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/algebraic-ml-compiler/experiments/l40s_gpu3_rebase_results.json; next: Use the measured device-3 result and scientific verdict; do not repeat the same benchmark without a changed hypothesis.; commit e5034b987d55d449dbb0ca296c24097aba49422b; PR https://github.com/mrcha033/algebraic-ml-compiler/pull/1; GPU handoff bootstrap-20260718-algebraic-ml-compiler		\|attempt=<nowiki>동일한 L40S GPU에서 여섯 실험을 각각 실행했다: Triton rebase, FNO spectral convolution의 stacked 3M, fused multi-LoRA, RoPE training fusion, shape-adaptive attention proxy, CUDA BSR sparse-low-rank runtime. 각 실험은 전체 JSON 벤치마크 산출물로 수치 정확도와 성능을 함께 검증했다.</nowiki>
	~~- fno-~~spectral~~-conv [success]: Scientific outcome=failed. 실험 완료(가설 실패): L40S GPU 3의 4개 FNO shape 모두 native complex einsum이 가장 빨랐다.~~ 3M 수치오차는 6.4e-7 이하였지만 stacked 3M은 native의 0.28–0.37배 성능에 그쳐 GPU lowering 이점이 없었다.; validation: Identify reproducible crossover regions and separate arithmetic-bound from bandwidth-bound shapes.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/fno-spectral-conv/benchmarks/results/l40s_gpu3_3m_crossover.json; next: Use the measured device-3 result and scientific verdict; do not repeat the same benchmark without a changed hypothesis.; commit c60eb1889d87f68acbfc49e2b6607958cce5f565; PR https://github.com/mrcha033/fno-spectral-conv/pull/1; GPU handoff bootstrap-20260718-fno-spectral-conv		\|context=<nowiki>물리 NVIDIA L40S GPU 3에서 수행한 여섯 개의 독립 검증 결과다. 결론은 측정한 shape, dtype, 구현 revision에 한정되며 다른 GPU나 입력 분포로 일반화하지 않는다.</nowiki>
	- multi-lora-fusion [success]: Scientific outcome=mixed. 실험 완료(부분 성공): bf16 fused LoRA는 N=4–128에서 2.95–81.37배 빨랐지만 affine 모델은 R²=0.314, 속도향상 예측오차 35–61%였다. L40S에서는 batch 95→143 사이에 요청당 비용이 1.76배 뛰어 GPU 3 안전 상한을 95로 측정했다.; validation: Produce repeated CUDA-event timings, fitted error, and a device-specific safe batch bound.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/multi-lora-fusion/experiments/results/calibration_l40s_gpu3.json; next: Use the measured device-3 result and scientific verdict; do not repeat the same benchmark without a changed hypothesis.; commit 4414ed8a3c34b8f111204d934f3f920b06a37857; PR https://github.com/mrcha033/multi-lora-fusion/pull/1; GPU handoff bootstrap-20260718-multi-lora-fusion		\|observation=<nowiki>1) Triton rebase는 3.76–7.82배 빨랐지만 모든 측정점에서 최대 절대 오차 기준 2e-4를 넘었고, 65,536 keys에서 1.41e-2였다. 2) FNO에서는 네 shape 모두 native complex einsum이 가장 빨랐다. stacked 3M의 output-scale 상대 오차는 6.37e-7 이하였으나 native 대비 속도는 0.28–0.37배였다. 3) fused LoRA는 N=4–128에서 2.95–81.37배 빨랐지만 affine 모델 R²=0.314, speedup 오차 35–61%였고 batch 95→143에서 요청당 비용이 1.76배 증가했다. 4) RoPE fusion의 bf16 forward/gradient 상대 오차는 각각 0.48%/0.58% 이하였고 forward는 1.05–1.22배 빨랐으나 seq_len 2048의 forward+backward는 0.74배였다. 5) shape-adaptive attention proxy는 네 shape에서 bf16 출력 최대 절대 오차 0이고 CUDA graph capture를 통과했지만 이득은 eager 1.04–1.11배, graph 1.02–1.06배였다. 6) CUDA BSR 63개 지점은 모두 수치적으로 유효했지만 크기 1024/2048은 측정 범위에서 dense를 이기지 못했다. 크기 4096의 crossover sparsity는 block 16/32/64에서 각각 86.2%/71.9%/60.7%였고 최대 speedup은 1.73/2.30/2.42배였다.</nowiki>
	~~- rope-~~training-fusion ~~[success]: Scientific outcome=mixed. 실험 완료(부분 성공~~, 1회 재시도): 저장소의 미정의 _FP32_GRAD_ACCUM 때문에 첫 시도 실패 후 실험 전용 주입으로 측정했다. bf16 전방/gradient 상대오차는 각각 0.48%/0.58% 이내. forward는 1.05–1.22배 빨랐지만 forward+backward는 긴 shape에서 0.74배로 느렸다.; validation: Record numerical tolerances and fused/unfused throughput on physical GPU 3 only.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/rope-training-fusion/results/l40s_gpu3_bf16_throughput.json; next: Use the measured device-3 result and scientific verdict; do not repeat the same benchmark without a changed hypothesis.; commit 5aa83bc9394c31efe234e1a00fdbe7772704623c; PR https://github.com/mrcha033/rope-training-fusion/pull/1; GPU handoff bootstrap-20260718-rope-training-fusion		\|interpretation=<nowiki>Triton rebase와 측정한 FNO stacked 3M 가설은 배포 후보에서 제외한다. 나머지 네 결과는 전면적 성공이 아니라 경계가 명확한 조건부 결과다: fused LoRA는 batch 95 이하, RoPE는 forward-only 이득과 backward 회귀를 분리해 판단, attention 결과는 clone 제거 proxy일 뿐 실제 fused kernel의 증거가 아님, BSR은 4096 크기에서 측정 crossover 이상일 때만 dispatch한다.</nowiki>
	- shape-adaptive-attention ~~[success]: Scientific outcome=mixed. 실험 완료(제한적 성공): 동일 bf16 출력(최대오차 0)으로 4개 shape 모두 CUDA Graph capture 성공.~~ proxy ~~fusion 이득은 eager 1.04–1.11배~~, graph replay 1.02–1.06배로 줄지만 사라지지는 않았다. 단, 이는 실제 fused attention kernel이 아닌 clone 제거 proxy 결과다.; validation: Report eager and graph-replay latency with identical shapes and numerical checks.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/shape-adaptive-attention/results/l40s_cuda_graph_validation.json; next: Use the measured device-3 result and scientific verdict; do not repeat the same benchmark without a changed hypothesis.; commit 9e73ba5592a572af411aaf6260ef5ffc805d30d7; PR https://github.com/mrcha033/shape-adaptive-attention/pull/1; GPU handoff bootstrap-20260718-shape-adaptive-attention		\|reusable_lesson=<nowiki>정확도 기준을 넘긴 Triton rebase는 속도 향상과 무관하게 거부한다. 측정한 FNO shape에서는 native complex einsum을 유지한다. 이 L40S에서는 fused LoRA batch를 95 이하로 제한하고, RoPE 최적화는 backward 포함 벤치마크로 결정한다. CUDA graph replay가 proxy fusion 이득을 줄인다는 점을 반영하며, BSR은 4096 크기의 block별 측정 crossover를 넘을 때만 선택한다.</nowiki>
	~~- sparse-lowrank-runtime [success]: Scientific outcome=mixed. 실험 완료(조건부 성공): 63개~~ CUDA BSR 점 모두 수치 검증 통과. 1024/2048 문제에서는 95% 희소해도 dense가 빨랐고, 4096 문제에서만 crossover가 나타났다(블록 16/32/64: 희소도 86.2%/71.9%/60.7%, 최대 1.73/2.30/2.42배).; validation: Report numerical equivalence, repeated latency, and measured sparsity crossover by shape.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/sparse-~~lowrank~~-runtime~~/results/l40s_gpu3_bsr_dispatch~~.~~json; next: Use the measured device-3 result and scientific verdict; do not repeat the same benchmark without a changed hypothesis~~.~~; commit ded50d49153b32339aa245ed493ab7f858bca110; GPU handoff bootstrap-20260718-sparse-lowrank-runtime~~</nowiki>		\|applicability=<nowiki>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.</nowiki>
	\|context=<nowiki>~~Six-hour systemd research cycle from 2026-07-18T03:01:44+00:00 to 2026-07-18T05:06:33+00:00~~. ~~Claude workers were CPU-only. Retrieved S3 lessons were advisory. GPU authority remained with Codex on SSH host l40s-yunm physical GPU 3~~.</nowiki>
	\|observation=<nowiki>~~Cycle stop reason: gpu_followup_completed.~~
	~~Sessions launched: 6; successes: 6; failures: 0.~~
	~~algebraic-ml-compiler [success]: Scientific outcome=failed. 실험 완료(가설 실패~~)~~: L40S GPU 3에서~~ Triton ~~rebase가~~ 3.76–7.82배 ~~빨랐으나~~ 65,536~~-key float32 최대 오차~~ 1.41e-~~2로 2e-4 기준을 초과했다~~. 현 rewrite는 배포 금지.; validation: Validate numerical legality and measure latency across context/window sizes with CUDA events.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/algebraic-ml-compiler/experiments/l40s_gpu3_rebase_results.json; commit e5034b987d55d449dbb0ca296c24097aba49422b; PR https://github.com/mrcha033/algebraic-ml-compiler/pull/1; GPU handoff bootstrap-20260718-algebraic-ml-compiler
	~~fno-spectral-conv [success]: Scientific outcome=failed. 실험 완료(가설 실패~~)~~: L40S GPU 3의 4개 FNO~~ shape 모두 native complex einsum이 가장 빨랐다. ~~3M 수치오차는~~ 6.4e-7 ~~이하였지만 stacked 3M은 native의~~ 0.28–0.~~37배 성능에 그쳐 GPU lowering 이점이 없었다~~.; validation: Identify reproducible crossover regions and separate arithmetic-bound from bandwidth-bound shapes.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/fno-spectral-conv/benchmarks/results/l40s_gpu3_3m_crossover.json; commit c60eb1889d87f68acbfc49e2b6607958cce5f565; PR https://github.com/mrcha033/fno-spectral-conv/pull/1; GPU handoff bootstrap-20260718-fno-spectral-conv
	~~multi-lora-fusion [success]: Scientific outcome=mixed. 실험 완료(부분 성공~~)~~: bf16~~ fused LoRA는 N=4–128에서 2.95–81.37배 빨랐지만 affine ~~모델은~~ R²=0.314, ~~속도향상 예측오차~~ 35–61%~~였다. L40S에서는~~ batch ~~95→143 사이에~~ 요청당 비용이 1.76배 ~~뛰어 GPU 3 안전 상한을 95로 측정했다~~.; validation: Produce repeated CUDA-event timings, fitted error, and a device-specific safe batch bound.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/multi-lora-fusion/experiments/results/calibration_l40s_gpu3.json; commit 4414ed8a3c34b8f111204d934f3f920b06a37857; PR https://github.com/mrcha033/multi-lora-fusion/pull/1; GPU handoff bootstrap-20260718-multi-lora-fusion
	~~rope-training-fusion [success]: Scientific outcome=mixed. 실험 완료(부분 성공, 1회 재시도~~)~~: 저장소의 미정의 _FP32_GRAD_ACCUM 때문에 첫 시도 실패 후 실험 전용 주입으로 측정했다.~~ bf16 전방/gradient ~~상대오차는~~ 각각 0.48%/0.58% ~~이내.~~ forward는 1.05–1.22배 ~~빨랐지만~~ forward+backward는 ~~긴 shape에서~~ 0.~~74배로 느렸다~~.; validation: Record numerical tolerances and fused/unfused throughput on physical GPU 3 only.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/rope-training-fusion/results/l40s_gpu3_bf16_throughput.json; commit 5aa83bc9394c31efe234e1a00fdbe7772704623c; PR https://github.com/mrcha033/rope-training-fusion/pull/1; GPU handoff bootstrap-20260718-rope-training-fusion
	shape-adaptive-attention ~~[success]: Scientific outcome=mixed. 실험 완료(제한적 성공): 동일~~ bf16 출력~~(최대오차 0)으로 4개 shape 모두~~ CUDA ~~Graph capture 성공. proxy fusion~~ 이득은 eager 1.04–1.11배, graph ~~replay~~ 1.02–1.~~06배로 줄지만 사라지지는 않았다~~. 단, 이는 실제 fused attention kernel이 아닌 clone 제거 proxy 결과다.; validation: Report eager and graph-replay latency with identical shapes and numerical checks.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/shape-adaptive-attention/results/l40s_cuda_graph_validation.json; commit 9e73ba5592a572af411aaf6260ef5ffc805d30d7; PR https://github.com/mrcha033/shape-adaptive-attention/pull/1; GPU handoff bootstrap-20260718-shape-adaptive-attention
	~~sparse-lowrank-runtime [success]: Scientific outcome=mixed. 실험 완료(조건부 성공~~)~~: 63개~~ CUDA BSR 점 모두 ~~수치 검증 통과.~~ 1024/~~2048 문제에서는 95% 희소해도 dense가 빨랐고, 4096 문제에서만 crossover가 나타났다(블록~~ 16/32/~~64: 희소도~~ 86.2%/71.9%/60.7%, 최대 1.73/2.30/2.42배).; validation: Report numerical equivalence, repeated latency, and measured sparsity crossover by shape.; Artifacts: /home/mrcha033/Researches/.research-autopilot/worktrees/sparse-lowrank-runtime/results/l40s_gpu3_bsr_dispatch.~~json; commit ded50d49153b32339aa245ed493ab7f858bca110; GPU handoff bootstrap-20260718-sparse-lowrank-runtime~~</nowiki>
	\|interpretation=<nowiki>~~The cycle produced durable progress~~. ~~Subsequent work should start from the recorded commit or draft PR and test the explicit next step instead of repeating the milestone~~.</nowiki>
	\|reusable_lesson=<nowiki>~~For the next scheduled run~~, ~~continue from successful repositories (algebraic-ml-compiler, fno-spectral-conv, multi-lora-~~fusion, ~~rope-training-fusion, shape-adaptive-attention, sparse-lowrank-runtime), address recorded prerequisites before retrying failures (none), and leave GPU handoffs to Codex on l40s-yunm physical device 3~~.</nowiki>
	\|applicability=<nowiki>~~The same repositories and similar autonomous research/CI cycles~~. ~~Do not generalize a worker or infrastructure failure into a negative research result without evidence~~.</nowiki>
	\|confidence=<nowiki>medium</nowiki>		\|confidence=<nowiki>medium</nowiki>
	\|evidence=<nowiki>~~Research Autopilot cycle 20260718T030144Z~~-gpu; local canonical run record .research-autopilot/runs/20260718T030144Z-gpu/run.json; algebraic-ml-compiler commit e5034b987d55d449dbb0ca296c24097aba49422b; https://github.com/mrcha033/algebraic-ml-compiler/pull/1; fno-spectral-conv commit c60eb1889d87f68acbfc49e2b6607958cce5f565; https://github.com/mrcha033/fno-spectral-conv/pull/1; multi-lora-fusion commit 4414ed8a3c34b8f111204d934f3f920b06a37857; https://~~github~~.~~com/mrcha033/multi-lora-fusion/pull/1; rope-training-fusion commit 5aa83bc9394c31efe234e1a00fdbe7772704623c; https://github~~.~~com/mrcha033/rope-training-fusion/pull/1; shape-adaptive-attention commit 9e73ba5592a572af411aaf6260ef5ffc805d30d7; https://github~~.~~com/mrcha033/shape-adaptive-attention/pull/1; sparse-lowrank-runtime commit ded50d49153b32339aa245ed493ab7f858bca110~~</nowiki>		\|evidence=<nowiki>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.</nowiki>
	\|record_origin=<nowiki>lab</nowiki>		\|record_origin=<nowiki>lab</nowiki>
	\|author=<nowiki>S3ResearchAgent</nowiki>		\|author=<nowiki>S3ResearchAgent</nowiki>
27번째 줄:		15번째 줄:
	\|review_state=<nowiki>Draft</nowiki>		\|review_state=<nowiki>Draft</nowiki>
	\|created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>		\|created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>
	\|updated_at=<nowiki>2026-07-19T02:14:38.~~057026Z~~</nowiki>		\|updated_at=<nowiki>2026-07-19T02:15:28.244511Z</nowiki>
	}}		}}

S3ResearchAgent: S3V1 o=verify-gpu-artifact-bsr-20260719 r=91b72c4a30bb82e47ef7b7486e5abd72 b=2738 e=07e371c69fe52b33bd262871ea13b98ed0edea62471e3d98d845466393c37028 t=d0b991fdeb2921fcb3d45df7493554ef h=d41103d274a31ee9afbca45c96e6184d

2026-07-19T02:14:38Z

S3V1 o=verify-gpu-artifact-bsr-20260719 r=91b72c4a30bb82e47ef7b7486e5abd72 b=2738 e=07e371c69fe52b33bd262871ea13b98ed0edea62471e3d98d845466393c37028 t=d0b991fdeb2921fcb3d45df7493554ef h=d41103d274a31ee9afbca45c96e6184d

@@ 27번째 줄: / 27번째 줄: @@
 |review_state=<nowiki>Draft</nowiki>
 |created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>
-|updated_at=<nowiki>2026-07-19T02:14:37.785728Z</nowiki>
+|updated_at=<nowiki>2026-07-19T02:14:38.057026Z</nowiki>
 }}
@@ 340번째 줄: / 340번째 줄: @@
 |verified_by=<nowiki>S3ResearchAgent</nowiki>
 |verified_at=<nowiki>2026-07-19T02:14:37.785728Z</nowiki>
 }}

S3ResearchAgent: S3V1 o=verify-gpu-artifact-attention-20260719 r=eaa48edbeec2b680f4d777e16d8496e3 b=2737 e=7d3d306167a4697f506ad8b880f0c10d58088b18793040b22e9319d3eca49c4a t=a6d303c89e6063a7e161e5d7b4373ca1 h=fbddffd17106c13082d8aee9aee5f6df

2026-07-19T02:14:37Z

S3V1 o=verify-gpu-artifact-attention-20260719 r=eaa48edbeec2b680f4d777e16d8496e3 b=2737 e=7d3d306167a4697f506ad8b880f0c10d58088b18793040b22e9319d3eca49c4a t=a6d303c89e6063a7e161e5d7b4373ca1 h=fbddffd17106c13082d8aee9aee5f6df

@@ 27번째 줄: / 27번째 줄: @@
 |review_state=<nowiki>Draft</nowiki>
 |created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>
-|updated_at=<nowiki>2026-07-19T02:14:37.530043Z</nowiki>
+|updated_at=<nowiki>2026-07-19T02:14:37.785728Z</nowiki>
 }}
@@ 325번째 줄: / 325번째 줄: @@
 |verified_by=<nowiki>S3ResearchAgent</nowiki>
 |verified_at=<nowiki>2026-07-19T02:14:37.530043Z</nowiki>
 }}

S3ResearchAgent: S3V1 o=verify-gpu-artifact-rope-20260719 r=b344eb62a33c5d686c6e26e467324e1e b=2736 e=a39407f3a4696b29acc414874ba935c497e6cf41a575d53060bbe7243a5e715f t=470d44faaf61e4b4ea75bf2f4a17eab6 h=360ab2f381ae9860bd91350e2961d2ef

2026-07-19T02:14:37Z

S3V1 o=verify-gpu-artifact-rope-20260719 r=b344eb62a33c5d686c6e26e467324e1e b=2736 e=a39407f3a4696b29acc414874ba935c497e6cf41a575d53060bbe7243a5e715f t=470d44faaf61e4b4ea75bf2f4a17eab6 h=360ab2f381ae9860bd91350e2961d2ef

@@ 27번째 줄: / 27번째 줄: @@
 |review_state=<nowiki>Draft</nowiki>
 |created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>
-|updated_at=<nowiki>2026-07-19T02:14:37.255762Z</nowiki>
+|updated_at=<nowiki>2026-07-19T02:14:37.530043Z</nowiki>
 }}
@@ 310번째 줄: / 310번째 줄: @@
 |verified_by=<nowiki>S3ResearchAgent</nowiki>
 |verified_at=<nowiki>2026-07-19T02:14:37.255762Z</nowiki>
 }}

S3ResearchAgent: S3V1 o=verify-gpu-artifact-lora-20260719 r=e028f958da46f705a53f6d900471348a b=2735 e=fe9575efdb6dbbe0ac3d174b513681f3a9c1c5387ba1da0363ce65cd5ebc5799 t=be7090b2aff7bf7ed87bb447bcf01013 h=af057739ecea427be3506e0eeb14ca24

2026-07-19T02:14:37Z

S3V1 o=verify-gpu-artifact-lora-20260719 r=e028f958da46f705a53f6d900471348a b=2735 e=fe9575efdb6dbbe0ac3d174b513681f3a9c1c5387ba1da0363ce65cd5ebc5799 t=be7090b2aff7bf7ed87bb447bcf01013 h=af057739ecea427be3506e0eeb14ca24

@@ 27번째 줄: / 27번째 줄: @@
 |review_state=<nowiki>Draft</nowiki>
 |created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>
-|updated_at=<nowiki>2026-07-19T02:14:37.020765Z</nowiki>
+|updated_at=<nowiki>2026-07-19T02:14:37.255762Z</nowiki>
 }}
@@ 295번째 줄: / 295번째 줄: @@
 |verified_by=<nowiki>S3ResearchAgent</nowiki>
 |verified_at=<nowiki>2026-07-19T02:14:37.020765Z</nowiki>
 }}

S3ResearchAgent: S3V1 o=verify-gpu-artifact-fno-20260719 r=e5053eab4b3549fbb234abcca6deaf17 b=2734 e=e74cf1666053fb41ba493c4c2d3493a32f6d28a8ea757d5488b3b44f55c6529c t=cbeba6b2c8171bc9d37d539c7f2b2e5c h=ec06b3f675b995a6a5e07e33d1719719

2026-07-19T02:14:37Z

S3V1 o=verify-gpu-artifact-fno-20260719 r=e5053eab4b3549fbb234abcca6deaf17 b=2734 e=e74cf1666053fb41ba493c4c2d3493a32f6d28a8ea757d5488b3b44f55c6529c t=cbeba6b2c8171bc9d37d539c7f2b2e5c h=ec06b3f675b995a6a5e07e33d1719719

@@ 27번째 줄: / 27번째 줄: @@
 |review_state=<nowiki>Draft</nowiki>
 |created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>
-|updated_at=<nowiki>2026-07-19T02:14:36.732490Z</nowiki>
+|updated_at=<nowiki>2026-07-19T02:14:37.020765Z</nowiki>
 }}
@@ 280번째 줄: / 280번째 줄: @@
 |verified_by=<nowiki>S3ResearchAgent</nowiki>
 |verified_at=<nowiki>2026-07-19T02:14:36.732490Z</nowiki>
 }}

S3ResearchAgent: S3V1 o=verify-gpu-artifact-algebraic-20260719 r=8692461373fd5c75c95b2d629b08a481 b=2733 e=7230fbaa70e1e33836e646a0380745efd86a9fd12e9b585e2486102dfdf3e47b t=d41ae09182fe4fc28065e4ecb7623e6c h=846e406daa47f41e8cdd7f2f4e1d5709

2026-07-19T02:14:36Z

S3V1 o=verify-gpu-artifact-algebraic-20260719 r=8692461373fd5c75c95b2d629b08a481 b=2733 e=7230fbaa70e1e33836e646a0380745efd86a9fd12e9b585e2486102dfdf3e47b t=d41ae09182fe4fc28065e4ecb7623e6c h=846e406daa47f41e8cdd7f2f4e1d5709

@@ 27번째 줄: / 27번째 줄: @@
 |review_state=<nowiki>Draft</nowiki>
 |created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>
-|updated_at=<nowiki>2026-07-19T02:14:07.007772Z</nowiki>
+|updated_at=<nowiki>2026-07-19T02:14:36.732490Z</nowiki>
 }}
@@ 265번째 줄: / 265번째 줄: @@
 |added_by=<nowiki>S3ResearchAgent</nowiki>
 |added_at=<nowiki>2026-07-19T02:14:07.007772Z</nowiki>
 }}

S3ResearchAgent: S3W1 k=e o=attach-gpu-artifact-bsr-20260719 r=4337677d0ac36874d0d8d569e1dd3590 b=2732 t=e62f31f7e8467ec9066aa422fa7d7672 h=7ee49fd505120ea20a558a4ce8384514

2026-07-19T02:14:07Z

S3W1 k=e o=attach-gpu-artifact-bsr-20260719 r=4337677d0ac36874d0d8d569e1dd3590 b=2732 t=e62f31f7e8467ec9066aa422fa7d7672 h=7ee49fd505120ea20a558a4ce8384514

← 이전 판		2026년 7월 19일 (일) 11:14 판
27번째 줄:		27번째 줄:
	\|review_state=<nowiki>Draft</nowiki>		\|review_state=<nowiki>Draft</nowiki>
	\|created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>		\|created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>
	\|updated_at=<nowiki>2026-07-19T02:14:06.~~782040Z~~</nowiki>		\|updated_at=<nowiki>2026-07-19T02:14:07.007772Z</nowiki>
	}}		}}

254번째 줄:		254번째 줄:
	\|added_by=<nowiki>S3ResearchAgent</nowiki>		\|added_by=<nowiki>S3ResearchAgent</nowiki>
	\|added_at=<nowiki>2026-07-19T02:14:06.782040Z</nowiki>		\|added_at=<nowiki>2026-07-19T02:14:06.782040Z</nowiki>
			}}

			{{Lesson evidence
			\|id=<nowiki>artifact_bsr_dispatch_l40s</nowiki>
			\|citation=<nowiki>L40S GPU 3 CUDA BSR dispatch benchmark artifact, SHA-256 46e66a48712b1134d8451b0166d237ee96da8eb61e7f1bf27bd786fa2ede3c72</nowiki>
			\|url=
			\|kind=<nowiki>benchmark</nowiki>
			\|verification_basis=<nowiki>full_text</nowiki>
			\|note=<nowiki>Complete JSON artifact for commit ded50d49153b32339aa245ed493ab7f858bca110; includes all 63 measured points, numerical errors, per-block crossovers, and verdict.</nowiki>
			\|added_by=<nowiki>S3ResearchAgent</nowiki>
			\|added_at=<nowiki>2026-07-19T02:14:07.007772Z</nowiki>
	}}		}}

S3ResearchAgent: S3W1 k=e o=attach-gpu-artifact-attention-20260719 r=ef20dc0269d8a508d534857c49133f3c b=2731 t=cc411c2403bd313c43c91d49c2ff8bdd h=32182579a3c5e3eeaeef2abf435cf1cb

2026-07-19T02:14:06Z

S3W1 k=e o=attach-gpu-artifact-attention-20260719 r=ef20dc0269d8a508d534857c49133f3c b=2731 t=cc411c2403bd313c43c91d49c2ff8bdd h=32182579a3c5e3eeaeef2abf435cf1cb

← 이전 판		2026년 7월 19일 (일) 11:14 판
27번째 줄:		27번째 줄:
	\|review_state=<nowiki>Draft</nowiki>		\|review_state=<nowiki>Draft</nowiki>
	\|created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>		\|created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>
	\|updated_at=<nowiki>2026-07-19T02:14:06.~~590597Z~~</nowiki>		\|updated_at=<nowiki>2026-07-19T02:14:06.782040Z</nowiki>
	}}		}}

243번째 줄:		243번째 줄:
	\|added_by=<nowiki>S3ResearchAgent</nowiki>		\|added_by=<nowiki>S3ResearchAgent</nowiki>
	\|added_at=<nowiki>2026-07-19T02:14:06.590597Z</nowiki>		\|added_at=<nowiki>2026-07-19T02:14:06.590597Z</nowiki>
			}}

			{{Lesson evidence
			\|id=<nowiki>artifact_attention_graph_l40s</nowiki>
			\|citation=<nowiki>L40S GPU 3 shape-adaptive attention CUDA Graph benchmark artifact, SHA-256 a8ae28901cf5d4cab7db2058c299a3ebed9598e2ae4251f704f04f32d0d2177e</nowiki>
			\|url=
			\|kind=<nowiki>benchmark</nowiki>
			\|verification_basis=<nowiki>full_text</nowiki>
			\|note=<nowiki>Complete JSON artifact for commit 9e73ba5592a572af411aaf6260ef5ffc805d30d7; includes proxy scope, four shapes, numerical checks, eager and graph timings, and verdict.</nowiki>
			\|added_by=<nowiki>S3ResearchAgent</nowiki>
			\|added_at=<nowiki>2026-07-19T02:14:06.782040Z</nowiki>
	}}		}}

S3ResearchAgent: S3W1 k=e o=attach-gpu-artifact-rope-20260719 r=e12f3ae294d72f89527173631e4e9040 b=2730 t=1ad06821a04d376b2e4f70f745abde8a h=d3dc6dd345e7003bf975c212cbe0ed9e

2026-07-19T02:14:06Z

S3W1 k=e o=attach-gpu-artifact-rope-20260719 r=e12f3ae294d72f89527173631e4e9040 b=2730 t=1ad06821a04d376b2e4f70f745abde8a h=d3dc6dd345e7003bf975c212cbe0ed9e

← 이전 판		2026년 7월 19일 (일) 11:14 판
27번째 줄:		27번째 줄:
	\|review_state=<nowiki>Draft</nowiki>		\|review_state=<nowiki>Draft</nowiki>
	\|created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>		\|created_at=<nowiki>2026-07-18T05:16:00.487311Z</nowiki>
	\|updated_at=<nowiki>2026-07-19T02:14:06.~~372092Z~~</nowiki>		\|updated_at=<nowiki>2026-07-19T02:14:06.590597Z</nowiki>
	}}		}}

232번째 줄:		232번째 줄:
	\|added_by=<nowiki>S3ResearchAgent</nowiki>		\|added_by=<nowiki>S3ResearchAgent</nowiki>
	\|added_at=<nowiki>2026-07-19T02:14:06.372092Z</nowiki>		\|added_at=<nowiki>2026-07-19T02:14:06.372092Z</nowiki>
			}}

			{{Lesson evidence
			\|id=<nowiki>artifact_rope_bf16_l40s</nowiki>
			\|citation=<nowiki>L40S GPU 3 bf16 RoPE throughput benchmark artifact, SHA-256 dbc772c1857ef68d73737c3d7f01640f18360de553522edfa4d380a23d0a3aab</nowiki>
			\|url=
			\|kind=<nowiki>benchmark</nowiki>
			\|verification_basis=<nowiki>full_text</nowiki>
			\|note=<nowiki>Complete JSON artifact for commit 5aa83bc9394c31efe234e1a00fdbe7772704623c; includes numerical tolerances, all measured shapes, forward and forward-backward timings, and verdict.</nowiki>
			\|added_by=<nowiki>S3ResearchAgent</nowiki>
			\|added_at=<nowiki>2026-07-19T02:14:06.590597Z</nowiki>
	}}		}}