Dr. Luke Logan is an Assistant Research Professor at the Gnosis Research Center. He completed his PhD at Illinois Institute of Technology (defended 2025, degree conferred December 2025). His research focuses on making distributed storage and operating systems more programmable, portable, and performant. He is the developer of LabStor (SC'22), MegaMmap (SC'24), and pMEMCPY, with contributions to DAOS evaluation and heterogeneous storage optimization. Luke first joined IIT as an undergraduate researcher in 2017 before beginning his PhD in 2020.
Joined: Aug 2017Graduated: Dec 2025
Now: Assistant Research Professor at Gnosis Research Center, Illinois Institute of Technology
23Publications
8Projects
Research Interests
- Heterogeneous Storage
- I/O Optimization
- Persistent Memory
- Distributed Systems
Projects
- ChronoLog (2021–present)
- Coeus (2022–2026)
- DTIO (2023–2026)
- Hermes (2019–2025)
- IOWarp (2024–present)
- IRIS (2019–2023)
- LABIOS (2019–present)
- Portus (2026–present)
News Mentions
Awards & Honors
Publications
2026
GPUH5: A GPU-first I/O Library for Scientific Data
2026
GNNs Beyond HBM: Compressed, Bit-Exact Feature Streaming for Out-of-Core Training
2026
Acropolis: A Portable, AI-Driven Semantic Search System for HPC
2026
Agent Error Simulator: Fault Injection for Testing Recovery in Agentic Workflows
2026
Benchmarking the Performance of Semantic Search Techniques for Agent Discovery
2025
Insights into GPUDirect Data Transfer through NIXL Benchmarking
2025
DTIO: Data Stack for AI-driven Workflows
2025
A Unified Data Stack for AI Workflows
2025
Bridging LLMs and HPC: A Modular MCP-IOWarp Framework for Scalable Task Execution
2025
Scaling Data Tokenization for AI Systems
2024
Jarvis: Towards a Shared, User-Friendly, and Reproducible, I/O Infrastructure.
2024
MegaMmap: Blurring the Boundary Between Memory and Storage for Data-Intensive Workloads
2024
An Evaluation of DAOS for Simulation and Deep Learning HPC Workloads
2024
Hades: A Context-Aware Active Storage Framework for Accelerating Large-Scale Data Analysis
2023
An Evaluation of DAOS for Simulation and Deep Learning HPC Workloads
2022
LuxIO: Intelligent Resource Provisioning and Auto-Configuration for Storage Services
2022
LabStor: A Modular and Extensible Platform for Developing High-Performance, Customized I/O Stacks in Userspace
2021
Utilizing Persistent Memory in Parallel I/O Libraries
2021
HFlow: A Dynamic and Elastic Multi-Layered Data Forwarder
2021
pMEMCPY: a simple, lightweight, and portable I/O library for storing data in persistent memory
2021
Apollo: An ML-assisted Real-Time Storage Resource Observer
2020
Quantifying the Overheads of the Modern Linux I/O Stack
2020
