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DTSTART;TZID=America/Chicago:20251119T133000
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UID:submissions.supercomputing.org_SC25_sess283@linklings.com
SUMMARY:Data Analytics, Visualization, and Storage
DESCRIPTION:MANS: Efficient and Portable ANS Encoding for Multi-Byte Integ
 er Data on CPUs and GPUs\n\nLossless compression is a classic technique fo
 r reducing data storage and transmission requirements. Asymmetric numeral 
 systems (ANS) is a high-throughput, high-ratio lossless compression algori
 thm, but it lacks effective support for multi-byte data and cross-platform
  compatibility.\n\nTo address this...\n\n\nWenjing Huang and Jinwu Yang (I
 nstitute of Computing Technology, Chinese Academy of Sciences; University 
 of Chinese Academy of Sciences, Beijing); Shengquan Yin (Institute of Comp
 uting Technology, Chinese Academy of Sciences; University of Science and T
 echnology of China); Haoxu Li and Yida Gu (Institute of Computing Technolo
 gy, Chinese Academy of Sciences; University of Chinese Academy of Sciences
 , Beijing); Zedong Liu (Institute of Computing Technology, Chinese Academy
  of Sciences; University of Electronic Science and Technology of China); X
 ing Jing and Zheng Wei (Institute of Computing Technology, Chinese Academy
  of Sciences); Shiyuan Fu and Hao Hu (Institute of High Energy Physics, Ch
 inese Academy of Sciences); and Guangming Tan and Dingwen Tao (Institute o
 f Computing Technology, Chinese Academy of Sciences)\n--------------------
 -\nPhoenix: A Refactored I/O Stack for GPU Direct Storage Without Phony Bu
 ffers\n\nGPU Direct Storage (GDS) plays a vital role in GPU storage system
 s, utilizing P2P-DMA technology to establish a direct data transfer path b
 etween the GPU and storage devices. This direct path reduces storage acces
 s latency and CPU overhead, thus improving data transfer efficiency. Curre
 ntly, however...\n\n\nJianqin Yan, Shi Qiu, Yina Lv, Yifan Hu, Hao Chen, a
 nd Zhirong Shen (Xiamen University); Xin Yao and Renhai Chen (Huawei Theor
 y Lab); Jiwu Shu (Xiamen University); Gong Zhang (Huawei Theory Lab); and 
 Yiming Zhang (Shanghai Jiao Tong University, Xiamen University)\n---------
 ------------\nSTELLAR: Storage Tuning Engine Leveraging LLM Autonomous Rea
 soning for High-Performance Parallel File Systems\n\nI/O performance is cr
 ucial to efficiency in data-intensive scientific computing, but tuning lar
 ge-scale storage systems is complex, costly, and notoriously manpower-inte
 nsive, making it inaccessible for most domain scientists. In this study, w
 e propose STELLAR, an autonomous tuner for high-performan...\n\n\nChris Eg
 ersdoerfer (University of Delaware); Philip Carns, Shane Snyder, and Rober
 t Ross (Argonne National Laboratory (ANL)); and Dong Dai (University of De
 laware)\n---------------------\ngParaKV: A GPGPU-Accelerated Key-Value Sep
 aration-Based KV Store with Optimized Compaction and Garbage Collection\n\
 nLSM tree-based key-value stores are widely deployed in modern cloud stora
 ge systems thanks to high data storage efficiency and retrieval capabiliti
 es. The compaction in the LSM tree, however, results in severe performance
  bottlenecks, especially in large-sized value cases. While key-value separ
 ation...\n\n\nHui Sun (Anhui University); Xiangxiang Jiang (Ahhui Universi
 ty); Xiao Qin (Auburn University); Song Jiang (University of Texas, Arling
 ton); and Enhui Wang (Anhui University)\n\nTag: Data Analytics, Visualizat
 ion & Storage\n\nRecording: Livestreamed, Recorded\n\nRegistration Categor
 y: Technical Program Reg Pass\n\nSession Chair: Ana Kupresanian (Lawrence 
 Berkeley National Laboratory (LBNL))
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