Fengguang Song
Computer Science · Indiana University
Publications
90
Citations
696
Est. group size
~2
Recurring co-author estimate
Active years
23
Publishing since 2004
Fengguang Song works in high-performance and parallel computing, with a strong focus on techniques for compressing large scientific datasets so they can be stored, moved, and processed more efficiently, often using GPUs and other specialized hardware. Recent projects also include workflow systems that coordinate computation across supercomputers and cloud resources, learned data structures for memory, and applying machine learning to large optimization problems. The work spans both foundational systems tools and applications such as water-science modeling and quantum circuit simulation.
Publication activity was higher in the late 2010s (around 8-11 per year), dipped in 2020-2022, and has since recovered to roughly 4-7 papers per year, indicating renewed but variable output.
Generated by claude-opus-4-8 from public bibliographic data · Jul 9, 2026
Current awards run through September 2027 — about 1 year of funding on record from today. Awards are often renewed, so this is what is currently public, not a forecast.
Collaborative Research: SHF: Small: Reimagining Communication Bottlenecks in GNN Acceleration through Collaborative Locality Enhancement and Compression Co-Design
Collaborative Research: Frameworks: FZ: A fine-tunable cyberinfrastructure framework to streamline specialized lossy compression development
Matched to public NIH RePORTER and NSF records by name and institution. Awards from other agencies are not shown, and a match is not always found — this list may be incomplete.
Typically publishes in teams of ~3 · 56% small-team papers (≤3 authors) · across 23 venues
- KVCC_SC26_artifact
Zenodo (CERN European Organization for Nuclear Research) · 2026
- KVCC_SC26_artifact
Zenodo (CERN European Organization for Nuclear Research) · 2026
- GPZ: GPU-Accelerated Lossy Compressor for Particle Data
2026
- Accelerating AI Compression through Lightweight Lossless Encoding and Pipelined Workflows
2026
- Pushing the Limits of GPU Lossy Compression: A Hierarchical Delta Approach
2025
- STZ: A High Quality and High Speed Streaming Lossy Compression Framework for Scientific Data
2025
- FZModules: A Heterogeneous Computing Framework for Customizable Scientific Data Compression Pipelines
2025
- BMQSim: Overcoming Memory Constraints in Quantum Circuit Simulation with a High-Fidelity Compression Framework
2025
- STZ: A High Quality and High Speed Streaming Lossy Compression Framework for Scientific Data
2025
- Automated Statistical Testing and Certification of a Reliable Model-Coupling Server for Scientific Computing
arXiv (Cornell University) · 2025
- Automated Statistical Testing and Certification of a Reliable Model-Coupling Server for Scientific Computing
Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2025
- Asynchronous modeling workflows in CyberWater with on-demand HPC/Cloud access
Future Generation Computer Systems · 2024
- WIPE: A Write-Optimized Learned Index for Persistent Memory
ACM Transactions on Architecture and Code Optimization · 2023
- A Distributed-GPU Deep Reinforcement Learning System for Solving Large Graph Optimization Problems
ACM Transactions on Parallel Computing · 2023
- Efficient in-situ workflow planning for geographically distributed heterogeneous environments
Future Generation Computer Systems · 2023
- Author eBooks×8
- Lecture notes in computer science×5
- arXiv (Cornell University)×4
- Future Generation Computer Systems×2
- Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering×2
- Suren Byna
Computer Science · The Ohio State University
- Jiannan Tian
Computer Science · Indiana University
- Dingwen Tao
Computer Science · Indiana University
- Hammad Ahmad
Computer Science · University of Michigan
- Tom Peterka
Computer Science · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Sep 1, 2026.
Claim or correct this profile