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Luke Logan

Luke Logan is a PhD Candidate and HPC software engineer in the Department of Computer Science at the Illinois Institute of Technology and a member of the Gnosis Research Center. Luke Logan works under the guidance of Dr. Xian-He Sun. His research aims to make distributed storage and operating systems more programmable, portable, and performant. HPC and AI applications perform massive data movements, putting extreme stress on storage systems to satisfy a wide variety of conflicting I/O and computational objectives. However, with rapidly evolving storage hardware and software, the design of these systems need to be reconsidered, especially considering the dynamic nature of AI workloads and agents.

Luke Logan

Research Interests

  • Distributed Storage
  • Parallel and Distributed Systems
  • Parallel I/O
  • Operating Systems
  • GPU Programming

Publications

Authors
Title
Venue
Type
Date
Links
I. Muradli,
J. Ye,
L. Logan,
A. Kougkas,
X.-H. Sun		Insights into GPUDirect Data Transfer through NIXL Benchmarking21st IEEE International Conference on eScience (eScience '25), September 15-18, 2025PosterSeptember, 2025
K. Bateman,
N. Rajesh,
J. Cernuda,
L. Logan,
B. Nicolae,
F. Cappello,
X.-H. Sun,
A. Kougkas		DTIO: Data Stack for AI-driven WorkflowsThe 37th International Conference on Scalable Scientific Data Management (SSDBM 2025)ConferenceJune, 2025
J. Cernuda,
L. Logan,
N. Lewis,
S. Byna,
X.-H. Sun,
A. Kougkas		Jarvis: Towards a Shared, User-Friendly, and Reproducible, I/O Infrastructure.The International Parallel Data Systems Workshop (PDSW'24)WorkshopNovember, 2024
L. Logan,
X.-H. Sun,
A. Kougkas		MegaMmap: Blurring the Boundary Between Memory and Storage for Data-Intensive WorkloadsThe International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'24)ConferenceNovember, 2024
L. Logan,
J. Lofstead,
A. Kougkas,
X.-H. Sun		An Evaluation of DAOS for Simulation and Deep Learning HPC WorkloadsSIGOPS Operating Systems Review (OSR'24)JournalJune, 2024
J. Cernuda,
L. Logan,
A. Gainaru,
J. Lofstead,
A. Kougkas,
X.-H. Sun		Hades: A Context-Aware Active Storage Framework for Accelerating Large-Scale Data AnalysisThe 24th IEEE/ACM international Symposium on Cluster, Cloud and Internet Computing (CCGRID 2024)ConferenceMay, 2024
L. Logan,
J. Lofstead,
A. Kougkas,
X.-H. Sun		An Evaluation of DAOS for Simulation and Deep Learning HPC WorkloadsThe 3rd Workshop on Challenges and Opportunities of Efficient and Performant Storage Systems (CHEOPS'23)WorkshopMay, 2023
K. Bateman,
N. Rajesh,
J. Cernuda,
L. Logan,
J. Ye,
S. Herbein,
A. Kougkas,
X.-H. Sun		LuxIO: Intelligent Resource Provisioning and Auto-Configuration for Storage ServicesThe 29th edition of the IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC'22)ConferenceDecember, 2022
L. Logan,
J. Cernuda,
J. Lofstead,
X.-H. Sun,
A. Kougkas		LabStor: A Modular and Extensible Platform for Developing High-Performance, Customized I/O Stacks in UserspaceThe International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'22)ConferenceNovember, 2022
L. Logan,
J. Lofstead,
S. Levy,
P. Widener,
X.-H. Sun,
A. Kougkas		Utilizing Persistent Memory in Parallel I/O LibrariesThe 2021 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'21), November 14–19, 2021PosterNovember, 2021
J. Cernuda,
H. Devarajan,
L. Logan,
K. Bateman,
N. Rajesh,
J. Ye,
A. Kougkas,
X.-H. Sun		HFlow: A Dynamic and Elastic Multi-Layered Data ForwarderThe 2021 IEEE International Conference on Cluster Computing (CLUSTER'21), September 7-10, 2021ConferenceSeptember, 2021
L. Logan,
J. Lofstead,
S. Levy,
P. Widener,
X.-H. Sun,
A. Kougkas		pMEMCPY: a simple, lightweight, and portable I/O library for storing data in persistent memoryThe 1st Workshop on Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads (REX-IO'21), in conjunction with the 2021 IEEE International Conference on Cluster Computing (CLUSTER'21), September 7-10, 2021WorkshopSeptember, 2021
N. Rajesh,
H. Devarajan,
J. Cernuda,
K. Bateman,
L. Logan,
J. Ye,
A. Kougkas,
X.-H. Sun		Apollo: An ML-assisted Real-Time Storage Resource ObserverThe 30th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC'21), June 21-25, 2021ConferenceJune, 2021
L. Logan,
A. Kougkas,
X.-H. Sun		Quantifying the Overheads of the Modern Linux I/O StackThe International Conference for High Performance Computing, Networking, Storage and Analysis (SC'20)PosterNovember, 2020
H. Devarajan,
A. Kougkas,
L. Logan,
X.-H. Sun		HCompress: Hierarchical Data Compression for Multi-Tiered Storage EnvironmentsIEEE International Parallel and Distributed Processing Symposium (IPDPS'20), May 18-22, 2020ConferenceMay, 2020