WELCOME TO Dr. Qing Gary Liu's Website

Assistant Professor,

Helen and John C. Hartmann Department of Electrical and Computer Engineering

New Jersey Institute of Technology, Newark, NJ

Joint Faculty,

Computer Science and Mathematics Division

Oak Ridge National Laboratory, Oak Ridge, TN

Email: qliu at njit.edu, liuq at ornl.gov (not frequently checked)

Office: ECEC 345





Dr. Gary Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at NJIT, and he holds Joint Faculty Appointment at ORNL (with Scientific Data Group). Learn More about Dr. Liu's experience.


Dr. Liu's  areas of research include high-performance computing and Big Data science. He has published on premier HPC conferences, such as SC, HPDC, SIGMETRICS, IPDPS, CLUSTER, and etc.



SIRIUS: Enabling Progressive Data Exploration for Extreme-Scale Scientific Data, IEEE Transactions on Multi-scale Computing Systems, 2019

Can I/O Variability be Reduced on QoS-less HPC Storage Systems?, IEEE Transactions on Computers, 2019

High Performance I/O Frameworks 101, tutorial at SC'18

Harnessing Data Movement in Virtual Clusters for In-Situ Execution, IEEE Transactions on Parallel and Distributed Systems, 2019

Work highlighted by Oak Ridge Leadership Computing Facility

Coupling Exascale Multiphysics Applications: Methods and Lessons Learned, IEEE eScience, 2018

DuoModel: Leveraging Reduced Model for Data Reduction and Re-computation on HPC Storage, IEEE Letters of Computer Society, 2018

Write Energy Reduction for PCM via Pumping Efficiency Improvement, ACM Transactions on Storage, 2018

A View from ORNL: Scientific Data Research Opportunities in the Big Data Age, accepted to ICDCS'18

Canopus+: Intent-driven Data Refactoring for Extreme-Scale Data Analytics, ICNSC'18 (abstract paper)

Understanding and Modeling Lossy Compression Schemes on HPC Scientific Data, IEEE IPDPS'18 (1st rounder, 38 out of 461 submissions, best paper nominee).

Canopus: A Paradigm Shift Towards Elastic Extreme-Scale Data Analytics on HPC Storage, IEEE Cluster'17 (acceptance rate 21%)

TGE: Machine Learning Based Task Graph Embedding for Large-scale Topology Mapping, IEEE Cluster'17 (acceptance rate 21%)

Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales, (invited paper) accepted to EuroPar'17. (acceptance rate 28%)

 DFS-Container: Achieving Containerized Block I/O for Distributed File Systems, ACM SOCC'17 (poster paper).

SELF: A High Performance and Bandwidth Efficient Approach to Exploiting Die-stacked DRAM as Part of Memory, IEEE MASCOT, Banff, Canada, September, 2017 (acceptance rate 30%)

Canopus: Enabling Extreme-Scale Data Analytics on Big HPC Storage via Progressive Refactoring, USENIX Hotstorage, Santa Clara, July, 2017

Co-organizing/organized The International Workshop on Data Reduction for Big Scientific Data (DRBSD-3, DRBSD-2, DRBSD-1) at ISC'18, SC'17, ISC'17, with ORNL, ANL, and Brown University (photo1, photo2).

Exacution: Enhancing Scientific Data Management for Exascale, ICDCS 2017, Atlanta, GA, June 2017.

StoreRush: An Application-Level Approach to Harvesting Idle Storage in a Best Effort Environment, accepted to ICCS, Zurich, Switzerland, June, 2017. (acceptance rate 28%)

Excited to be part of DOE Co-design Center for Online Data Analysis and Reduction (led by Ian Foster) to work on exascale data reduction techniques, and be part of DOE SIRIUS project (led by Scott Klasky) to work on exascale data storage.

Invited to DOE ASCR Panel Review, 2018,  IPDPS (16, 18), CCGrid (17, 18,19), ICPADS'17, SC (14, 15, 17),  Cluster (15), SSDBM (17,18), ICDCS'17, and NVMSA'17, Publication co-chair of ICNSC 2018.


                         DOD Highlights



               DOE  Highlights



Selected Papers

[IPDPS'18] Understanding and Modeling Lossy Compression Schemes on HPC Scientific Data, best paper nominee

[EuroPar'17] Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales, invited paper

[ICDCS'17] Exacution: Enhancing Scientific Data Management for Exascale

[SIGMETRICS'15] Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation

[HPDC'12] ISOBAR hybrid compression-I/O interleaving for large-scale parallel I/O optimization

[HPDC'11] Six degrees of scientific data: reading patterns for extreme scale science IO

[SC'10] Managing Variability in the IO Performance of Petascale Storage Systems

[IPDPS'10] PreDatA–preparatory data analytics on peta-scale machines




Software and Impact

Research by Dr. Liu and his collaborators have to led to software tools being used by a large number of applications.


Research by Dr. Liu and his collaborators have been highlighted from various media, facilities, and government agencies such as DOE, and DOD.


Dr. Liu has been very fortunate to receive a number of awards.


Dr. Liu teaches computer engineering related courses at ECE department at NJIT.

Experimental Facility

Dr. Liu's group uses cutting-edge HPC systems and testbeds for experimental research.


The HPC lab consists of graduate students, postdocs, and collaborators from various institutions.