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

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Bio

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.

Publication

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.

 

News

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

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)

Best paper nominee of IPDPS'18 (4 out of 461 submissions).

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

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, the 37th IEEE International Conference on Distributed Computing Systems (ICDCS 2017), Atlanta, GA, June 2017.

StoreRush: An Application-Level Approach to Harvesting Idle Storage in a Best Effort Environment, accepted to the International Conference on Computational Science (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'18, CCGrid'18, ICPADS'17, SC'17 doctoral show case, and poster committee, CCGrid'17, ICDCS'17, and NVMSA'17, Publication co-chair of ICNSC 2018.

 

                         DOD Highlights

 

 

               DOE  Highlights

                    (https://science.energy.gov/~/media/ascr/images/ADIOS.jpg)

 

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

[HotStorage'17] Canopus: Enabling Extreme-Scale Data Analytics on Big HPC Storage via Progressive Refactoring

[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.

Highlights

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

Awards

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

Teaching

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.

HPC Lab

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

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