Rohan Kadekodi
Postdoctoral Scholar
University of Washington
About
I am a postdoc in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, working with Prof. Baris Kasikci. My research focuses on building efficient systems infrastructure for modern storage and memory tecnologies. I work on building systems that are able to achieve the best performance out of modern and heterogeneous memory technologies such as CXL-attached memory, GPU memory, as well as byte addressable storage for improving the efficiency of datacenter applications as well as machine learning systems.
Prior to joining UW, I completed my PhD at the University of Texas at Austin under the guidance of Prof. Vijay Chidambaram, where I developed innovative solutions for persistent memory file systems and key-value stores. My doctoral research focused on transparently achieving high performance for datacenter applications on modern byte-addressable storage technologies.
Research Interests
Publications
Peer-reviewed Publications
Y. Gu, I. Neal, J. Xu, SC Lee, A. Said, M. Haydar, J. Van Geffen, Rohan Kadekodi, B. Kasikci. Scalable and Accurate Application-Level Crash-Consistency Testing via Representative Testing.
Proceedings of the ACM on Programming Languages 9 (OOPSLA1), 477-506, 2025
Molly Jane Nicholas, Nicolai Marquardt, Michel Pahud, Nathalie Riche, Hugo Romat, Christopher Collins, David Ledo, Rohan Kadekodi, Badrish Chandramouli and Ken Hinckley. Escapement: A Tool for Interactive Prototyping with Video via Sensor-Mediated Abstraction of Time.
In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI 2023)
Rohan Kadekodi, Saurabh Kadekodi, Soujanya Ponnapalli, Harshad Shirwadkar, Gregory R. Ganger, Aasheesh Kolli and Vijay Chidambaram. WineFS: a hugepage-aware file system for persistent memory that ages gracefully.
In Proceedings of the 28th ACM Symposium on Operating Systems Principles, 2021. (SOSP 2021)
Rohan Kadekodi, Se Kwon Lee, Sanidhya Kashyap, Taesoo Kim, Aasheesh Kolli and Vijay Chidambaram. SplitFS: Reducing Software Overhead in File Systems for Persistent Memory.
In Proceedings of the 27th ACM Symposium on Operating Systems Principles, pp. 494-508. ACM, 2019. (SOSP 2019)
Suhas Jayaram Subramanya, Devvrit, Harsha Simhadri, Ravishankar Krishanswamy, Rohan Kadekodi. RandNSG: Billion Point Nearest Neighbor Search on a Single Node.
Proceedings of the 33rd Annual Conference on Neural Information Processing Systems. (NeurIPS 2019)
Pandian Raju, Rohan Kadekodi, Vijay Chidambaram, Ittai Abraham. PebblesDB: Building Key-Value Stores using Fragmented Log-Structured Merge Trees.
Proceedings of the 15th Symposium of Operating Systems Principles, pp. 497-514. ACM, 2017. (SOSP 2017)
Jayashree Mohan, Rohan Kadekodi, Vijay Chidambaram. Analyzing IO Amplification in Linux File Systems (Poster).
Proceedings of the 8th ACM The Eighth SIGOPS AsiaPacific Workshop on Systems, Sep 2017. Best Poster Award. (ApSys 2017)
ArXiv Preprints
Rohan Kadekodi*, Z. Jin*, K. Kamahori, Y. Gu, S. Khatiri, N. Bayindirli, S. Gorbunov, B. Kasikci. DualTune: Decoupled Fine-Tuning for On-Device Agentic Systems.
arXiv preprint arXiv:2510.00229, 2025
Y. Gu*, Rohan Kadekodi*, H. Nguyen, K. Kamahori, Y. Liu, B. Kasikci. ConsumerBench: Benchmarking Generative AI Applications on End-User Devices.
arXiv preprint arXiv:2506.17538, 2025
K. Zhu, T. Tang, Q. Xu, Y. Gu, Z. Zeng, Rohan Kadekodi, L. Zhao, A. Li, et al. Tactic: Adaptive sparse attention with clustering and distribution fitting for long-context llms.
arXiv preprint arXiv:2502.12216, 2025
CY Lin, K Kamahori, Y Liu, X Shi, M Kashyap, Y Gu, R Shao, Z Ye, K Zhu, Rohan Kadekodi, et al. TeleRAG: Efficient retrieval-augmented generation inference with lookahead retrieval.
arXiv preprint arXiv:2502.20969, 2025
Research Projects
Fine-tuning local LLMs for agentic workflows
University of Washington (Ongoing)
Exploring fine-tuning techniques for local LLMs to improve their tool calling capabilities in agentic systems.
Key contributions: Fine-tuning, Tool calling, Agentic systems, local LLMs
Benchmarking and Improving the Performance of LLM Applications on End-User Devices via smart GPU sharing, scheduling and memory management
University of Washington (Ongoing)
Building a benchmark suite and an evaluation leaderboard for evaluating the performance of generative AI applications and agentic workflows on end-user devices. Also building a runtime system that accelerates local LLM performance for agentic workflows using smart GPU sharing, scheduling and memory management system for end-user devices.
Key contributions: Benchmark suite, performance evaluation, end-user devices, smart GPU sharing, scheduling and memory management
Tiered Memory Management with Controlled Allocation and Adaptive Migration
University of Washington (Ongoing)
Building a tiered memory system that uses smart allocation policies for achieving fine-grained hot data tracking, along with adaptive migrations for reacting to workload changes.
Key contributions: Smart allocation policies, fine-grained data tracking, adaptive migration strategies
RDFS: Enabling Remote DAX Memory-Mapping for Persistent Memory
University of Washington & UT Austin (Ongoing)
Building a distributed Persistent Memory manager for transparent scaling of single-node memory-mapped applications across a cluster.
Key contributions: Distributed memory management, transparent scaling architecture, cluster-wide memory mapping
Shared-state system for distributed interactive applications
Microsoft Research (CHI 2023)
Building a shared-state system with client-side caching and easy-to-use API for supporting distributed interactive applications in the cloud.
Key contributions: Client-side caching, distributed state management, cloud application support
WineFS: a hugepage-aware file system for persistent memory that ages gracefully
UT Austin (SOSP 2021)
A PM file system aimed at preserving hugepages for improving the performance of emerging PM applications.
Key contributions: Hugepage preservation, performance optimization for PM applications, graceful aging behavior
SplitFS: Reducing Software Overhead in File Systems for Persistent Memory
UT Austin & VMware Research (SOSP 2019)
A user-space file system aimed at improving performance of POSIX applications on persistent memory by converting reads and writes to loads and stores from user space, and passing metadata operations to the kernel.
Key contributions: User-space file system design, POSIX compatibility, metadata operation optimization
PebblesDB: Building Key-Value Stores using Fragmented Log-Structured Merge Trees
UT Austin (SOSP 2017)
A key-value store based on fragmented log-structured merge trees, which reduces IO Amplification while increasing throughput.
Key contributions: Fragmented LSM-tree design, IO amplification reduction, throughput optimization
Teaching Experience
CS360V: Virtualization
Assistant Instructor, UT Austin
Spring 2023
CS380D: Distributed Computing
Teaching Assistant, UT Austin
Spring 2022
CS378: Virtualization
Teaching Assistant, UT Austin
Fall 2019
Technical Talks
Profile Guided Memory Tiering (PRISM SRC Liaison Talk)
May 2024
Building high-performance storage systems for Persistent Memory (BigHPC Webinar)
July 2022
WineFS: A hugepage-aware file system for persistent memory that ages gracefully at Storage Analytics team (Google)
October 2021
WineFS: A hugepage-aware file system for persistent memory that ages gracefully at Symposium on Operating Systems Principles (SOSP 2021)
October 2021
SplitFS: Reducing Software Overhead in File Systems for Persistent Memory at Symposium on Operating Systems Principles (SOSP 2019)
October 2019
Accelerating POSIX applications on Persistent Memory at VMware Research
August 2018
Work Experience
Postdoctoral Scholar
University of Washington
Aug 2023 - Present
Mentor: Prof. Baris Kasikci
Research Intern
Microsoft Research, Redmond
May 2021 - Aug 2021
Mentor: Dr. Badrish Chandramouli
Research Intern
Microsoft Research, Redmond
May 2020 - Aug 2020
Mentor: Dr. Badrish Chandramouli
Research Intern
Microsoft Research, India
May 2019 - Aug 2019
Mentor: Dr. Harsha Vardhan Simhadri
Research Intern
VMware Research
May 2018 - Aug 2018
Mentor: Dr. Aasheesh Kolli
Research Intern
University of Wisconsin, Madison
Jan 2017 - July 2017
Mentors: Prof. Remzi Arpaci-Dusseau and Prof. Vijay Chidambaram