Qing Liu

Qing Liu. 2022. “Locality-based transfer learning on compression autoencoder for efficient scientific data lossy compression.” Journal of Network and Computer Applications.

Qing Liu. 2021. “MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction.” IEEE Transactions on Computers.

Qing Liu. 2021. “The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science.” The International Journal of High Performance Computing Applications.

Qing Liu. 2021. “High-Ratio Lossy Compression: Exploring the Autoencoder to Compress Scientific Data.” IEEE Transactions on Big Data.

Qing Liu. 2020. “ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management.” SoftwareX.

Qing Liu. 2020. “Enhancing Proportional IO Sharing on Containerized Big Data File Systems.” IEEE Transactions on Computers.

Qing Liu. 2020. “Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks.” IEEE Letters of the Computer Society.

Qing Liu. 2020. “Fine-grained resource provisioning and task scheduling for heterogeneous applications in distributed green clouds.” IEEE/CAA Journal of Automatica Sinica.

Qing Liu. 2019. “An Embedded Feature Selection Method for Imbalanced Data Classification.” IEEE/CAA Journal of Automatica Sinica.

Qing Liu. 2019. “Bi-objective Task Scheduling for Distributed Green Data Centers.” IEEE Transactions on Automation Science and Engineering.

Qing Liu. 2019. “Can I/O Variability be Reduced on QoS-less HPC Storage Systems?.” IEEE Transactions on Computers.

Qing Liu. 2019. “Compression Ratio Modeling and Estimation Across Error Bounds for Lossy Compression.” IEEE Transactions on Parallel and Distributed Systems.

Qing Liu. 2019. “Time-Dependent Cloud Workload Prediction via Multi-Task Learning.” IEEE Robotics and Automation Letters.

Qing Liu. 2018. “DuoModel: Leveraging Reduced Model for Data Reduction and Re-computation on HPC Storage.” IEEE Letters of Computer Society.

Qing Liu. 2018. “Write Energy Reduction for PCM via Pumping Efficiency Improvement.” ACM Transactions on Storage.

“Error-controlled, Progressive, and Adaptable Retrieval of Scientific Data with Multilevel Decomposition”
32nd ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, November 2021.

“Reducing the Training Overhead of the HPC Compression Autoencoder via Dataset Proportioning”
15th IEEE International Conference on Networking, Architecture, and Storage , October (4th Quarter/Autumn) 2021.

“Accelerating Multigrid-based Hierarchical ScientificData Refactoring on GPUs”
35th IEEE International Parallel and Distributed Processing Symposium, May 2021.

“zMesh: Exploring Application Characteristics to Improve Lossy Compression Ratio for Adaptive Mesh Refinement”
35th IEEE International Parallel and Distributed Processing Symposium, May 2021.

“A Comprehensive Study of In-Memory Computing on Large HPC Systems”
40th IEEE International Conference on Distributed Computing Systems (ICDCS), December 2020.

“Taming I/O Variation on QoS-less HPC Storage: What Can Applications do?”
31st ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, November 2020.

“Exploring Transfer Learning to Reduce Training Overhead of HPC Data in Machine Learning”
IEEE International Conference on Networking, Architecture, and Storage, 2019.

“Identifying Latent Reduced Models to Precondition Lossy Compression”
33rd IEEE International Parallel and Distributed Processing Symposium, 2019.

“Load-aware Elastic Data Reduction and Re-computation for Adaptive Mesh Refinement”
IEEE International Conference on Networking, Architecture, and Storage , 2019.

“A View from ORNL: Scientific Data Research Opportunities in the Big Data Age”
International Conference on Distributed Computing Systems, 2018.

“Understanding and Modeling Lossy Compression Schemes on HPC Scientific Data”
IEEE International Parallel & Distributed Processing Symposium, 2018.

“Canopus: A Paradigm Shift Towards Elastic Extreme-Scale Data Analytics on HPC Storage”
IEEE International Conference on Cluster Computing (CLUSTER), September 2017.

“Canopus: Enabling Extreme-Scale Data Analytics on Big HPC Storage via Progressive Refactoring”
USENIX Hotstorage, July (3rd Quarter/Summer) 2017.

“Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales”
EuroPar'17, August 2017.

“DFS-Container: Achieving Containerized Block I/O for Distributed File Systems”
ACM SOCC'17, September 2017.

“Exacution: Enhancing Scientific Data Management for Exascale”
IEEE ICDCS, June 2017.

“SELF: A High Performance and Bandwidth Efficient Approach to Exploiting Die-stacked DRAM as Part of Memory”
IEEE MASCOT, September 2017.

“StoreRush: An Application-Level Approach to Harvesting Idle Storage in a Best Effort Environment”
International Conference on Computational Science, June 2017.

“TGE: Machine Learning Based Task Graph Embedding for Large-scale Topology Mapping”
IEEE International Conference on Cluster Computing (CLUSTER),, September 2017.