Current Projects
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CAREER: Programming the Existing and Emerging Memory Systems for Extreme-scale Parallel Performance
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Agency: National Science Foundation
CCF 2015254, CCF 1833332, CCF 1652732
Duration: 02/2017 – 02/2024
This CAREER project develops innovative software techniques to address the programming and performance challenges of the existing and emerging memory systems. It combats the memory wall challenge by developing a memory-centric programming paradigm for helping achieve extreme-scale performance of parallel applications with minimum impairment to programmability. For education, the project will involve a broader community starting from high school in the area of HPC and computer science.
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OpenMP Source-to-source compiler for multi-core CPUs, many-core GPUs and SIMD architectures
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Performance tools for measurement, analysis and visualization of parallel appilcations that use OpenMP, CUDA, OpenCL and MPI.
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ML/DL algorithms for medical image processing focusing on radiotherapy and treatment analysis of cancers such as tumor registration and segmentation.
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Past Projects
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SHF: Medium: Compute on Data Path: Combating Data Movement in High Performance Computing
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Agency: National Science Foundation
CISE SHF-1409946
Duration: 05/2014 – 07/2017
Collaborator: Yong Chen (PI), Robert Ross (Co-PI), Yonghong Yan (Co-PI), Barbara Chapman (Co-PI), Dries Kimpe (Former Co-PI)
This project studies the feasibility of a new Compute on Data Path methodology that expects to improve the performance and energy efficiency for high performance computing.
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SHF: Small:Collaborative Research: Application-aware Energy Modeling and Power Management for Parallel and High Performance Computing
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Agency: National Science Foundation
CISE CCF-2001580, CISE CCF-1833312, CISE SHF-1551182
Duration: 08/2014 – 08/2017
Collaborator: Kirk Cameron (VT)
One of the critical challenges in scaling out current and future high performance computing (HPC) and enterprise computing systems is the requirement that their power envelope remain comparable to that of today's systems. This project addresses this power wall challenge from the system software aspect by developing application-aware methodologies of energy modeling and power management.
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High Performance Implementation using GPU and FPGA of for Deformable Image Registration for Cancer Treatment
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Agency: Cancer Institute of William Beaumont Health System,
Duration: 07/2015 - present
The project aims to develop the fastest algorithms and software/hardware implementation for deformable image registration algorithms for practical use in cancer treatment.
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II-NEW: Image Processing Cloud (IPC): A Domain-Specific Cloud Computing Infrastructure for Research and Education
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Agency: National Science Foundation
CISE CNS-1205708
Duration: 06/2012 – 05/2016
Collaborator: Lei Huang (PVAMU) and Xiaoming Li (University of Delaware)
To deploy a high-level domain specific language to develop image processing applications using MapReduce framework and on the cloud computing environments.
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Sponsor and Support
Our research are supported through the College of Engineering of Computing at the University of South Carolina, National Science Foundation, Xilinx, Intel/Altera,
Maxeler Technologies, and Beaumont Cancer Institute of Beaumont Health System.
