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Dong Li

Researcher at Hunan University

Publications -  249
Citations -  6324

Dong Li is an academic researcher from Hunan University. The author has contributed to research in topics: Cache & Medicine. The author has an hindex of 35, co-authored 227 publications receiving 4417 citations. Previous affiliations of Dong Li include University of California & Oak Ridge National Laboratory.

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Journal ArticleDOI

PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications

TL;DR: A framework to isolate the power consumption of devices including disks, memory, NICs, and processors in a high-performance cluster and correlate these measurements to application functions is extended and conclusively how intelligent DVFS scheduling can enhance system energy efficiency while maintaining performance is revealed.
Journal ArticleDOI

3D Printable Graphene Composite.

TL;DR: For the first time, a graphene composite, with a graphene loading up to 5.6 wt%, can be 3D printable into computer-designed models and the composite’s linear thermal coefficient is below 75 ppm·°C−1 from room temperature to its glass transition temperature (Tg), which is crucial to build minute thermal stress during the printing process.
Journal ArticleDOI

Two-dimensional non-volatile programmable p–n junctions

TL;DR: Stable non-volatile programmable p-n junctions fabricated from the vertically stacked all-2D semiconductor/insulator/metal layers (WSe2/hexagonal boron nitride/graphene) in a semifloating gate field-effect transistor configuration are reported.
Journal ArticleDOI

Exosomal circPACRGL promotes progression of colorectal cancer via the miR-142-3p/miR-506-3p- TGF-β1 axis

TL;DR: This study is the first to reveal that cancer-derived exosomal circPACRGL plays an oncogenic role in CRC proliferation and metastasis, providing mechanistic insights into the roles of circRNAs in CRC progression and a valuable marker for CRC treatment.
Proceedings ArticleDOI

Hybrid MPI/OpenMP power-aware computing

TL;DR: A new power-aware performance prediction model of hybrid MPI/OpenMP applications is used to derive a novel algorithm for power-efficient execution of realis tic applications from ASCS equoia and N PB MZ bench marks.