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Dingwen Tao
University Of Alabama Tuscaloosa
$2,305,515
Attributed
$2,605,511
Total exposure
11
Grants
10
Lead (contact PI)
Attributed= this PI's even-split share of every grant they're on (the fair, additive number). Exposure = full size of all those grants.
Funding over time
peak $1.3M · FY2020–23$2M$1.5M$1M$500K$0
'20
'21
'22
'23
Funding mix
By agency
NSF$2,605,511 · 11
By mechanism
—$2,605,511 · 11
Top collaborators
- David A Dixon1 shared
- Jeffrey C Carver1 shared
- Xiaoyan Hong1 shared
Grant awards (11)
CAREER: A Highly Effective, Usable, Performant, Scalable Data Reduction Framework for HPC Systems and Applications$467,770
· FY2023 · CSE · contact PI
CAREER: A Highly Effective, Usable, Performant, Scalable Data Reduction Framework for HPC Systems and Applications$5,824
· FY2023 · CSE · contact PI
Collaborative Research: OAC Core: CEAPA: A Systematic Approach to Minimize Compression Error Propagation in HPC Applications$250,000
· FY2022 · CSE · contact PI
CDS&E: Collaborative Research: HyLoC: Objective-driven Adaptive Hybrid Lossy Compression Framework for Extreme-Scale Scientific Applications$203,483
· FY2022 · CSE · contact PI
CRII: OAC: An Efficient Lossy Compression Framework for Reducing Memory Footprint for Extreme-Scale Deep Learning on GPU-Based HPC Systems$92,649
· FY2022 · CSE · contact PI
Collaborative Research: Elements: ROCCI: Integrated Cyberinfrastructure for In Situ Lossy Compression Optimization Based on Post Hoc Analysis Requirements$280,000
· FY2021 · CSE · contact PI
CC* Compute: Accelerating Advances in Science and Engineering at The University of Alabama Through HPC Infrastructure$399,995
· FY2020 · CSE
CDS&E: Collaborative Research: HyLoC: Objective-driven Adaptive Hybrid Lossy Compression Framework for Extreme-Scale Scientific Applications$270,802
· FY2020 · CSE · contact PI
CDS&E: Collaborative Research: HyLoC: Objective-driven Adaptive Hybrid Lossy Compression Framework for Extreme-Scale Scientific Applications$270,802
· FY2020 · CSE · contact PI
CRII: OAC: An Efficient Lossy Compression Framework for Reducing Memory Footprint for Extreme-Scale Deep Learning on GPU-Based HPC Systems$189,593
· FY2020 · CSE · contact PI
CRII: OAC: An Efficient Lossy Compression Framework for Reducing Memory Footprint for Extreme-Scale Deep Learning on GPU-Based HPC Systems$174,593
· FY2020 · CSE · contact PI