The following Work-in-Progress Reports will be presented during the Work-in-Progress Reports (WiPs) Session on Tuesday, February 24, 4:10 pm–5:10 pm.
Courier: High-Performance Persistent Transactional Memory via Cooperative Concurrency Control
Hao Hu, Xinrui Zheng, Yizou Chen, and Xiangyu Zou, Harbin Institute of Technology, Shenzhen; Erci Xu, Shanghai Jiao Tong University and Alibaba Cloud; Hongpeng Wang and Wen Xia, Harbin Institute of Technology, Shenzhen
OmniStore: Towards a Unified Standard for Cost-Efficient, High-Performance Multi-modal Storage
Long Yang, Yuchen Shao, Chengcheng Wan, and Liang Shi, East China Normal University, Shanghai Innovation Institute
High-performance storage tier management for container-native AI workloads
Lei Pan, Frank Schmuck, Vasily Tarasov, and Veera Deenadhayalan, IBM Research
Chimera-VDB 2.0: Mixed-Precision Vector Database Improving Recall within Vector Spaces Containing Quantized Low-Precision Vectors
Naoshi Yamane, Michael Ryan Zielewski, Kazunori Yamada, Takuo Suganuma, and Takaki Nakamura, Tohoku University
Primer–Payload Collision Caching for Increasing Capacity in Random-Access DNA Storage
Alex Sensintaffar, University of Texas at Dallas; Gemma Mendonsa, Mengdi Bao, Riyan Mendonsa, and Sriram Chari, Seagate; Bingzhe Li, University of Texas at Dallas
The Pitfalls of Underspecified Workloads in Benchmarking
John Lewars, John Divirgilio, Frank Schmuck, and Vasily Tarasov, IBM Research
CacheANN: Cache-Aware Disk-Based Approximate Nearest Neighbor Search with a Multi-Granularity Graph
Dingyi Kang, The University of Texas at Dallas; Juncheng Yang, Harvard University; Bingzhe Li, The University of Texas at Dallas
Beyond ARC - Introducing LLM-driven Adaptive Policy Replacement
Zhenjie Sun, Viraj Thakkar, and Qi Lin, Arizona State University; Avani Wildani, Emory University and Cloudflare; Zhichao Cao, Arizona State University
littlefs: rbyd B-trees: Flexible B-trees when RAM << block size
Christopher Haster, littlefs
Less Index, More Speed: Accelerating Large-Scale KVCache Index for Long-Context LLM Serving
Jun Kong, Chang Guo, Zhenyu Zhang, and Zhichao Cao, Arizona State University
Energy-per-Tile: Characterizing the Energy Footprint of AI Workloads on Heterogeneous, Recycled Infrastructure
Wenkai Guan, University of Minnesota, Morris; Yang Zhao, University of Minnesota, Twin Cities; Zhichao Cao, Arizona State University; Tianlong Chen, The University of North Carolina at Chapel Hill; Zishen Wan, Georgia Tech