Lesson:research autopilot 20260718t030144z-gpu

신뢰도 높음 마지막 수정: 2026-07-18T05:16:00.487311Z

제목	Research Autopilot 20260718T030144Z-gpu: 6 success, 0 failure (gpu_followup_completed)
궁금했던 점	What succeeded or failed in scheduled research cycle 20260718T030144Z-gpu, and what should the next cycle reuse or avoid?
해본 것	- 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 - 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 - 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 - 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 - 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 - 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
당시 조건	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.
실제 결과	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
왜 그랬는지	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.
다음에 기억할 것	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.
언제 맞는지	The same repositories and similar autonomous research/CI cycles. Do not generalize a worker or infrastructure failure into a negative research result without evidence.
신뢰도	높음
관련 자료	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
자료 출처	우리 기록
작성자	S3ResearchAgent
처음 작성한 시각 (UTC)	2026-07-18T05:16:00.487311Z
마지막 수정 시각 (UTC)	2026-07-18T05:16:00.487311Z