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결과 목록

• Lesson:infinigen efficient generative inference of large language models with dynamic kv cache manageme a1ca3228  + (InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management)
• Lesson:intel accelerator ecosystem an soc oriented perspective bc8f0d20  + (Intel Accelerator Ecosystem: An SoC-Oriented Perspective)
• Lesson:kal kernel assisted non invasive memory leak tolerance with a general purpose memory allocator 372cd042  + (KAL: Kernel-assisted Non-invasive Memory Leak Tolerance with a General-purpose Memory Allocator)
• Lesson:dwkv kv saturation unproven  + (KV footprint 실험에서 실제 KV-capacity bottleneck을 입증하지 못했다)
• Lesson:kvaccel a novel write accelerator for lsm tree based kv stores with host ssd collaboration 968f5966  + (KVACCEL: A Novel Write Accelerator for LSM-Tree-Based KV Stores with Host-SSD Collaboration)
• Lesson:kvcache cache in the wild characterizing and optimizing kvcache cache at a large cloud provider 0b39b53c  + (KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider)
• Lesson:kangaroo caching billions of tiny objects on flash 90226bd8  + (Kangaroo: Caching Billions of Tiny Objects on Flash)
• Lesson:klotski efficient mixture of expert inference via expert aware multi batch pipeline bae4ea7b  + (Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch Pipeline)
• Lesson:kosmo efficient online miss ratio curve generation for eviction policy evaluation 96a524ff  + (Kosmo: Efficient Online Miss Ratio Curve Generation for Eviction Policy Evaluation)
• Lesson:research autopilot 20260718t030144z-gpu  + (L40S GPU 검증 결과: 실패한 두 가설과 조건부로 유효한 네 실행 구간)
• Lesson:lsm tree a7410f58  + (LSM-Tree 기반 키-값 저장소에서 대용량 데이터 로딩 기법 성능 분석)
• Lesson:lsm tree 060db792  + (LSM-Tree의 읽기 성능 개선을 위한 병렬적 필터/인덱스 접근 기법)
• Lesson:dwkv score code contract  + (Legacy scheduler code는 heuristic scoring과 ablation 경계를 테스트로 고정해야 한다)
• Lesson:legoos a disseminated distributed os for hardware resource disaggregation d71e3e68  + (LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation)
• Lesson:light dedup a light weight inline deduplication framework for non volatile memory file systems 000ae22e  + (Light-Dedup: A Light-weight Inline Deduplication Framework for Non-Volatile Memory File Systems)
• Lesson:lithos an operating system for efficient machine learning on gpus 76d95512  + (LithOS: An Operating System for Efficient Machine Learning on GPUs)
• Lesson:loom efficient capture and querying of high frequency telemetry bbcbd611  + (Loom: Efficient Capture and Querying of High-Frequency Telemetry)
• Lesson:m5 mastering page migration and memory management for cxl based tiered memory systems 4e752168  + (M5: Mastering Page Migration and Memory Management for CXL-based Tiered Memory Systems)
• Lesson:midas minimizing write amplification in log structured systems through adaptive group number and 1cac80b9  + (MIDAS: Minimizing Write Amplification in Log-Structured Systems through Adaptive Group Number and Size Configuration)
• Lesson:moat securely mitigating rowhammer with per row activation counters 6e5ed0e1  + (MOAT: Securely Mitigating Rowhammer with Per-Row Activation Counters)
• Lesson:mp bcoz ea8563ba  + (MP-BCOZ: 멀티프로세스 응용 지원 인과관계 프로파일러)
• Lesson:machine validation receipts should be structurally unable to grant human acceptance 470a7c00  + (Machine validation receipts should be structurally unable to grant human acceptance)
• Lesson:managing gpu buffers for caching more apps in mobile systems afba2ee0  + (Managing GPU Buffers for Caching More Apps in Mobile Systems)
• Lesson:managing memory tiers with cxl in virtualized environments 357c7cc7  + (Managing Memory Tiers with CXL in Virtualized Environments)
• Lesson:shape adaptive attention negative 20260722  + (Measure dispatch headroom and graph-mode effects before building adaptive GPU kernel policies)

• Lesson:memsos os guided selective memory mirroring ac9aab48  + (MemSOS: OS-Guided Selective Memory Mirroring)

• Lesson:memory deduplication in mobile systems 063e8b2e  + (Memory Deduplication in Mobile Systems)
• Lesson:minflow high performance and cost efficient data passing for i o intensive stateful serverless a 886ce161  + (MinFlow: High-performance and Cost-efficient Data Passing for I/O-intensive Stateful Serverless Analytics)
• Lesson:mitigating application resource overload with targeted task cancellation 90270bf8  + (Mitigating Application Resource Overload with Targeted Task Cancellation)
• Lesson:mitigating resource usage dependency in sorting based kv stores on hybrid storage devices via op 31cbb8ad  + (Mitigating Resource Usage Dependency in Sorting-based KV Stores on Hybrid Storage Devices via Operation Decoupling)
• Lesson:moe lightning high throughput moe inference on memory constrained gpus e51b6b29  + (MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs)
• Lesson:nap natural app processing for predictive user contexts in mobile smartphones a3c20a54  + (NAP: Natural App Processing for Predictive User Contexts in Mobile Smartphones)
• Lesson:nomad non exclusive memory tiering via transactional page migration 3990fa86  + (NOMAD: Non-Exclusive Memory Tiering via Transactional Page Migration)
• Lesson:nvme prp zero copy 76cd53aa  + (NVMe PRP 오프셋을 이용한 Zero-copy 저장장치 읽기 기법)
• Lesson:nvme bb91a49b  + (NVMe 다중 큐를 이용한 공정 분배 입출력 스케줄링 연구)
• Lesson:nvme driven lazy cache coherence for immutable data with nvme over fabrics c19688e2  + (NVMe-Driven Lazy Cache Coherence for Immutable Data with NVMe over Fabrics)
• Lesson:nanoflow towards optimal large language model serving throughput f0633332  + (NanoFlow: Towards Optimal Large Language Model Serving Throughput)
• Lesson:dwkv doomed chase domino  + (Negative laxity를 최고 urgency로 clamp하면 doomed-request domino가 생긴다)
• Lesson:not a dpu in name only unleashing rdma capable dpus in multi tenant serverless clouds with nadin 7031318c  + (Not A DPU in Name Only! Unleashing RDMA-capable DPUs in Multi-Tenant Serverless Clouds with NADINO)
• Lesson:ozz identifying kernel out of order concurrency bugs with in vivo memory access reordering 0b85f639  + (OZZ: Identifying Kernel Out-of-Order Concurrency Bugs with In-Vivo Memory Access Reordering)
• Lesson:dwkv metric mismatch  + (Objective와 논문의 primary metric이 다르면 DW-KV의 승패 해석이 뒤집힌다)
• Lesson:omnicache collaborative caching for near storage accelerators 303fe963  + (OmniCache: Collaborative Caching for Near-storage Accelerators)
• Lesson:on performance stability in lsm based storage systems e473bf0c  + (On Performance Stability in LSM-based Storage Systems)
• Lesson:optimizing file systems on heterogeneous memory by integrating dram cache with virtual memory ma aaa7aa17  + (Optimizing File Systems on Heterogeneous Memory by Integrating DRAM Cache with Virtual Memory Management)
• Lesson:optimizing the startup time of embedded systems a case study of digital tv 7f9199bd  + (Optimizing the Startup Time of Embedded Systems: A Case Study of Digital TV)
• Lesson:orca a distributed serving system for transformer based generative models 6bd2fa60  + (Orca: A Distributed Serving System for Transformer-Based Generative Models)
• Lesson:overcoming the memory wall with cxl enabled ssds ef2155d6  + (Overcoming the Memory Wall with CXL-Enabled SSDs)
• Lesson:pact a criticality first design for tiered memory 20db3445  + (PACT: A Criticality-First Design for Tiered Memory)
• Lesson:papi exploiting dynamic parallelism in large language model decoding with a processing in memory 5f9f53cc  + (PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System)
• Lesson:pet proactive demotion for efficient tiered memory management e2185da3  + (PET: Proactive Demotion for Efficient Tiered Memory Management)
• Lesson:pit optimization of dynamic sparse deep learning models via permutation invariant transformation 48509ad0  + (PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation)