activity
20172026
most citedSplitFS: Reducing Software Overhead in File Systems for Persistent Memory

159 citations · 168 across the 10 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.AI2025

AgentFlux: Decoupled Fine-Tuning & Inference for On-Device Agentic Systems

Rohan Kadekodi, Zhan Jin, Keisuke Kamahori +5

The deployment of Large Language Models (LLMs) as agentic orchestrators has revolutionized task automation, but the need for privacy-preserving, cost-effective solutions demands on…

cs.ET2025

Jenga: Responsive Tiered Memory Management without Thrashing

Rohan Kadekodi, Haoran Peng, Gilbert Bernstein +2

A heterogeneous memory has a single address space with fast access to some addresses (a fast tier of DRAM) and slow access to other addresses (a capacity tier of CXL-attached memor…

cs.DC2025

ConsumerBench: Benchmarking Generative AI Applications on End-User Devices

Yile Gu, Rohan Kadekodi, Hoang Nguyen +3

The recent shift in Generative AI (GenAI) applications from cloud-only environments to end-user devices introduces new challenges in resource management, system efficiency, and use…

cs.OS2025

Scalable and Accurate Application-Level Crash-Consistency Testing via Representative Testing

Yile Gu, Ian Neal, Jiexiao Xu +7

Crash consistency is essential for applications that must persist data. Crash-consistency testing has been commonly applied to find crash-consistency bugs in applications. The cras…

cs.LG2025

Tactic: Adaptive Sparse Attention with Clustering and Distribution Fitting for Long-Context LLMs

Kan Zhu, Tian Tang, Qinyu Xu +7

Long-context models are essential for many applications but face inefficiencies in loading large KV caches during decoding. Prior methods enforce fixed token budgets for sparse att…

cs.DC2025★ 1 cited

TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval

Chien-Yu Lin, Keisuke Kamahori, Yiyu Liu +11

Retrieval-augmented generation (RAG) extends large language models (LLMs) with external data sources to enhance factual correctness and domain coverage. Modern RAG pipelines rely o…