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결과 목록

• Lesson:exploiting asymmetric cpu performance for fast startup of subsystem in mobile smart devices c00616ec  + (Exploiting Asymmetric CPU Performance for Fast Startup of Subsystem in Mobile Smart Devices)
• Lesson:exploiting gpus in virtual machine for biocloud b3cdd293  + (Exploiting GPUs in Virtual Machine for BioCloud)
• Lesson:exploring the asynchrony of slow memory filesystem with easyio 0952deee  + (Exploring the Asynchrony of Slow Memory Filesystem with EasyIO)
• Lesson:extmem enabling application aware virtual memory management for data intensive applications 8a6dc0e4  + (ExtMem: Enabling Application-Aware Virtual Memory Management for Data-Intensive Applications)
• Lesson:extending applications safely and efficiently 7a5f9f65  + (Extending Applications Safely and Efficiently)
• Lesson:fifo queues are all you need for cache eviction ebc30306  + (FIFO queues are all you need for cache eviction)
• Lesson:faasmem improving memory efficiency of serverless computing with memory pool architecture b6ab9eec  + (FaaSMem: Improving Memory Efficiency of Serverless Computing with Memory Pool Architecture)
• Lesson:fast trapless kernel probes everywhere 781e739b  + (Fast (Trapless) Kernel Probes Everywhere)
• Lesson:fast core scheduling with userspace process abstraction d1ea9f62  + (Fast Core Scheduling with Userspace Process Abstraction)
• Lesson:fast on device llm inference with npus deecd917  + (Fast On-device LLM Inference with NPUs)
• Lesson:fast flexible and practical kernel extensions 9eb8897f  + (Fast, Flexible, and Practical Kernel Extensions)
• Lesson:fetchbpf customizable prefetching policies in linux with ebpf 9756cb90  + (FetchBPF: Customizable Prefetching Policies in Linux with eBPF)
• Lesson:fleetio managing multi tenant cloud storage with multi agent reinforcement learning 714ca9fb  + (FleetIO: Managing Multi-Tenant Cloud Storage with Multi-Agent Reinforcement Learning)
• Lesson:flexmem adaptive page profiling and migration for tiered memory 43991e2a  + (FlexMem: Adaptive Page Profiling and Migration for Tiered Memory)
• Lesson:following the data not the function rethinking function orchestration in serverless computing 5edeca75  + (Following the Data, Not the Function: Rethinking Function Orchestration in Serverless Computing)
• Lesson:fully harnessing the performance potential of dram less mobile flash storage ff995292  + (Fully Harnessing the Performance Potential of DRAM-less Mobile Flash Storage)
• Lesson:research autopilot 20260720t030001z gpu  + (GPU follow-up 20260720T030001Z: 1 inconclusive, 1 positive)
• Lesson:gqa training generalized multi query transformer models from multi head checkpoints a6839ee2  + (GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints)
• Lesson:group based memory oversubscription for virtualized clouds a7129dfa  + (Group-based Memory Oversubscription for Virtualized Clouds)
• Lesson:halfmoon log optimal fault tolerant stateful serverless computing e44c470a  + (Halfmoon: Log-Optimal Fault-Tolerant Stateful Serverless Computing)
• Lesson:harvesting memory bound cpu stall cycles in software with msh 5866da08  + (Harvesting Memory-bound CPU Stall Cycles in Software with MSH)
• Lesson:hotrap hot record retention and promotion for lsm trees with tiered storage 849aa350  + (HotRAP: Hot Record Retention and Promotion for LSM-trees with Tiered Storage)
• Lesson:how storage i o affects user perceived latency in mobile apps b59ccb75  + (How Storage I/O Affects User-Perceived Latency in Mobile Apps)
• Lesson:how to copy memory coordinated asynchronous copy as a first class os service 8e43b859  + (How to Copy Memory? Coordinated Asynchronous Copy as a First-Class OS Service)
• Lesson:i o passthru upstreaming a flexible and efficient i o path in linux 7b568465  + (I/O Passthru: Upstreaming a Flexible and Efficient I/O Path in Linux)

• Lesson:ic cache efficient large language model serving via in context caching 4f05d8b7  + (IC-Cache: Efficient Large Language Model Serving via In-context Caching)

• Lesson:ice collaborating memory and process management for user experience on resource limited mobile d 312e453f  + (ICE: Collaborating Memory and Process Management for User Experience on Resource-limited Mobile Devices)
• Lesson:impress an importance informed multi tier prefix kv storage system for large language model infe 51b47015  + (IMPRESS: An Importance-Informed Multi-Tier Prefix KV Storage System for Large Language Model Inference)
• Lesson:ionia high performance replication for modern disk based kv stores 7cafc97e  + (IONIA: High-Performance Replication for Modern Disk-based KV Stores)
• Lesson:identifying on off cpu bottlenecks together with blocked samples 1f04444a  + (Identifying On-/Off-CPU Bottlenecks Together with Blocked Samples)
• Lesson:improving application launch performance in smartphones using recurrent neural network 8cfcaeb5  + (Improving Application Launch Performance in Smartphones Using Recurrent Neural Network)
• 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)