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결과 목록

Lesson:technical review z journal scalable per core journaling 1f19a370 + (Saved official abstract inspected in full; it supports the paper identity/headline only, not every detailed metric, limitation, and derived O/I/R statement.)
Lesson:z journal scalable per core journaling 2aea3506 + (Saved official abstract inspected in full; it supports the paper identity/headline only, not every detailed metric, limitation, and derived O/I/R statement.)
Lesson:a secure fast and resource efficient serverless platform with function rewind 6c6658bb + (Saved official abstract inspected in full; it supports the paper identity/headline only, not every detailed metric, limitation, and derived O/I/R statement.)
Lesson:asap fast mobile application switch via adaptive prepaging 3f9602cd + (Saved official abstract inspected in full; it supports the paper identity/headline only, not every detailed metric, limitation, and derived O/I/R statement.)
Lesson:exploiting gpus in virtual machine for biocloud b3cdd293 + (Sections 3.1-3.3 and 4.1-4.3; Figures 5-8; Tables 2-3.)
Lesson:technical review exploiting gpus in virtual machine for biocloud 9e8875c0 + (Sections 3.1-3.3 and 4.1-4.3; Figures 5-8; Tables 2-3.)
Lesson:task aware virtual machine scheduling for i o performance f431ccbe + (Sections 3.1-3.3, 4, 5.1-5.3, and 7; PBratio definition in Section 3.2.)
Lesson:technical review task aware virtual machine scheduling for i o performance 26a8da47 + (Sections 3.1-3.3, 4, 5.1-5.3, and 7; PBratio definition in Section 3.2.)
Lesson:nap natural app processing for predictive user contexts in mobile smartphones a3c20a54 + (Sections 3.4, 4.3-4.5, and 5; Tables 1-2; Figures 6-7.)
Lesson:technical review nap natural app processing for predictive user contexts in mobile smartphones df34aff8 + (Sections 3.4, 4.3-4.5, and 5; Tables 1-2; Figures 6-7.)
Lesson:scheduler support for video oriented multimedia on client side virtualization c8366be4 + (Sections 5.1-5.4 and 6.1-6.4; Figures 3-6; Table 2. Figure 3 reports 0.79%, 3.05%, and 0.55% estimation errors.)
Lesson:technical review scheduler support for video oriented multimedia on client side virtualization 69d0c186 + (Sections 5.1-5.4 and 6.1-6.4; Figures 3-6; Table 2. Figure 3 reports 0.79%, 3.05%, and 0.55% estimation errors.)
Lesson:research autopilot 20260718t030144z-gpu + (State JSON and six result files support the cycle summary, but no single existing partial-source evidence item covers all six repositories and O/I/R.)
Lesson:pivot-b200-20260717-tmux-argument-boundary-a01 + (The cited tmux pane transcript is not present in the source manifest or supplemental set.)
Lesson:research autopilot 20260718t030144z-gpu + (benchmarks/results/l40s_gpu3_3m_crossover.json)
Lesson:characterizing mobile soc for accelerating heterogeneous llm inference 6b900bb7 + (content-debt-review CD-001)
Lesson:lithos an operating system for efficient machine learning on gpus 76d95512 + (content-debt-review CD-002)
Lesson:constructing and analyzing the lsm compaction design space 1aa4ace5 + (content-debt-review CD-008)
Lesson:pact a criticality first design for tiered memory 20db3445 + (content-debt-review CD-009)
Lesson:rearchitecting buffered i o in the era of high bandwidth ssd 70b23de3 + (content-debt-review CD-010)
Lesson:research autopilot 20260718t030144z-gpu + (experiments/l40s_gpu3_rebase_results.json)
Lesson:research autopilot 20260718t030144z-gpu + (experiments/results/calibration_l40s_gpu3.json)
Lesson:research autopilot 20260718t030144z-gpu + (results/l40s_cuda_graph_validation.json)
Lesson:research autopilot 20260718t030144z-gpu + (results/l40s_gpu3_bf16_throughput.json)
Lesson:research autopilot 20260718t030144z-gpu + (results/l40s_gpu3_bsr_dispatch.json)

Lesson:separate semantic replication hashes from provenance bound artifact hashes e7ab7a4e + (semantic_replication object in preserved archive JSON.)

Lesson:technical review f0ce02c2 + (source acquisition manifest and current Lesson placeholder audit)
Lesson:technical review fd6afd43 + (source acquisition manifest and current Lesson placeholder audit)
Lesson:technical review gpu e130421c + (source acquisition manifest and current Lesson placeholder audit)
Lesson:technical review how storage i o affects user perceived latency in mobile apps ac1dc0d2 + (source acquisition manifest and current Lesson placeholder audit)
Lesson:technical review memory deduplication in mobile systems 9c6aec68 + (source acquisition manifest and current Lesson placeholder audit)
Lesson:technical review nvme d8da4781 + (source acquisition manifest and current Lesson placeholder audit)
Lesson:technical review nvme prp zero copy cb4e87d1 + (source acquisition manifest and current Lesson placeholder audit)
Lesson:a hybrid web browser architecture for mobile devices 0a9118ea + (보존 파일 manifest.json)
Lesson:deltazip efficient serving of multiple full model tuned llms 8420449f + (보존 파일 manifest.json)
Lesson:managing gpu buffers for caching more apps in mobile systems afba2ee0 + (보존 파일 manifest.json)
Lesson:a neural network accelerator for mobile application processors 72ce8850 + (보존 파일 manifest.json)
Lesson:demand based coordinated scheduling for smp vms a57bd76a + (보존 파일 manifest.json)
Lesson:machine validation receipts should be structurally unable to grant human acceptance 470a7c00 + (보존 파일 manifest.json)
Lesson:a performance stable numa management scheme for linux based hpc systems 6533e8f2 + (보존 파일 manifest.json)
Lesson:deltazip efficient serving of multiple full model tuned llms 8420449f + (보존 파일 manifest.json)
Lesson:machine validation receipts should be structurally unable to grant human acceptance 470a7c00 + (보존 파일 manifest.json)
Lesson:a neural network accelerator for mobile application processors 72ce8850 + (보존 파일 manifest.json)
Lesson:demand based coordinated scheduling for smp vms a57bd76a + (보존 파일 manifest.json)
Lesson:machine validation receipts should be structurally unable to grant human acceptance 470a7c00 + (보존 파일 manifest.json)
Lesson:an empirical study on low gpu utilization of deep learning jobs c5389b31 + (보존 파일 manifest.json)
Lesson:development of behavior profilers for multimedia consumer electronics 6a76ba00 + (보존 파일 manifest.json)
Lesson:moe lightning high throughput moe inference on memory constrained gpus e51b6b29 + (보존 파일 manifest.json)
Lesson:analysis of virtual machine live migration as a method for power capping 71088a92 + (보존 파일 manifest.json)
Lesson:development of behavior profilers for multimedia consumer electronics 6a76ba00 + (보존 파일 manifest.json)
Lesson:an empirical study on low gpu utilization of deep learning jobs c5389b31 + (보존 파일 manifest.json)