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scale and flexibility in distribution of hot content a83ac58f +
How can a private cloud distribute large, hot objects at very high fanout while supporting diverse per-use-case policies?  +
scalecache a scalable page cache for multiple solid state drives 60b5c227 +
How can the Linux page cache scale with many fast SSDs and many CPU cores?  +
scaleswap a scalable os swap system for all flash swap arrays 4041bd54 +
How can OS swap scale across many CPU cores and NVMe SSDs without shared metadata bottlenecks?  +
scalio scaling up dpu based jbof key value store with nvme of target offload 8db39764 +
How can NVMe-oF storage targets scale across dense SSD enclosures without overloading host CPUs?  +
scheduler support for video oriented multimedia on client side virtualization c8366be4 +
guest 수정 없이 hypervisor가 video/3D workload를 식별하고 CPU contention 중에도 목표 frame rate를 유지할 수 있는가?  +
scoz a systemwide causal profiler for multicore systems 7b5e72e2 +
thread-level/user-space 전용 COZ causal profiling을 multiprocess 애플리케이션과 Linux kernel code까지 확장할 수 있는가?  +
selective memory deduplication for cost efficiency in mobile smart devices 9f3139a0 +
모바일 애플리케이션 특성을 이용해 deduplication 비용을 줄이되 메모리 절감과 앱 실행 개선을 유지할 수 있는가?  +
separate semantic replication hashes from provenance bound artifact hashes e7ab7a4e +
How can two generation artifacts collected from different clean source commits be compared for model-semantic replication without erasing provenance?  +
serverless in the wild characterizing and optimizing the serverless workload at a large cloud pr f2fa1121 +
실제 FaaS 워크로드는 어떤 분포를 보이며 콜드 스타트를 자원 효율적으로 줄일 수 있는가?  +
serverlessllm low latency serverless inference for large language models 69b8cd6d +
How can serverless LLM inference start large models quickly despite GPU-memory scarcity and bursty arrivals?  +
shape adaptive attention negative 20260722 +
When is a shape-adaptive GPU kernel portfolio worth implementing, and why did the tested attention policy fail to justify further investment?  +
shiftlock mitigate one sided rdma lock contention via handover a5d67e63 +
Can one-sided RDMA locks avoid retry storms and server-NIC IOPS collapse under contention?  +
sieve is simpler than lru an efficient turn key eviction algorithm for web caches 86f86293 +
웹 캐시에 쉽게 이식하면서 LRU보다 단순하고 효율적인 축출 정책을 만들 수 있는가?  +
silk preventing latency spikes in log structured merge key value stores 8576916f +
How can an LSM store prevent compaction from causing severe foreground tail-latency spikes?  +
siloz leveraging dram isolation domains to prevent inter vm rowhammer 9d15762c +
DRAM 성능을 거의 희생하지 않고 VM 간 Rowhammer를 막을 수 있는가?  +
smartlmk a memory reclamation scheme for improving user perceived app launch time d60522c6 +
Android LMK가 사용자에게 가치 있는 cached app을 보존하도록 victim selection을 최적화할 수 있는가?  +
specinfer accelerating generative large language model serving with tree based speculative infer e609aa25 +
Can speculative decoding exploit multiple draft continuations while exactly preserving the target LLM's output distribution?  +
speculative multi level access in lsm tree based kv store 72e42f29 +
빠른 SSD의 내부 병렬성을 이용해 LSM-tree point lookup이 level을 순차 탐색하며 CPU lookup과 I/O를 직렬화하는 지연을 줄일 수 있는가?  +
speed is all you need on device acceleration of large diffusion models via gpu aware optimizatio c53959fd +
대형 확산 모델을 양자화 없이 스마트폰 GPU에서 실용 속도로 실행할 수 있는가?  +
ssdstreamer specializing i o stack for large scale machine learning 6b597ff1 +
반복적 ML 작업의 working set이 DRAM보다 클 때 저비용 SSD 기반 캐시가 in-memory caching에 근접한 성능을 낼 수 있는가?  +
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