Hsin-Hsuan Sung

College of Engineering

Works (5)

Updated: April 5th, 2024 14:42

2023 journal article

Accelerating matrix-centric graph processing on GPUs through bit-level optimizations

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 177, 53–67.

By: J. Chen n, H. Sung n, X. Shen n , N. Tallent *, K. Barker * & A. Li *

author keywords: GraphBLAS; Bit manipulation; GPU; Sparse matrix; Deep reinforcement learning
Sources: Web Of Science, NC State University Libraries
Added: April 11, 2023

2023 article

BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs

PROCEEDINGS OF THE 37TH INTERNATIONAL CONFERENCE ON SUPERCOMPUTING, ACM ICS 2023, pp. 264–276.

By: J. Chen n, H. Sung n, X. Shen n , S. Choudhury * & A. Li *

author keywords: graph neural networks; binarized GNN; bit manipulation; GPU; sparse matrix
TL;DR: This work redesigns thebinary GNN inference backend from the efficiency perspective by proposing a series of abstractions and techniques to map binary GNNs and their computations best to fit the nature of bit manipulations on GPUs. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: January 29, 2024

2022 article

Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU

2022 IEEE 36TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS 2022), pp. 515–525.

By: J. Chen n, H. Sung n, X. Shen n , N. Tallent *, K. Barker * & A. Li*

TL;DR: A two-level representation named Bit-Block Compressed Sparse Row (B2SR) is proposed and a series of optimizations to the graph operations on B2SR by leveraging the intrinsics of modern GPUs are presented. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: September 29, 2022

2022 article

Brief Industry Paper: Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card

2022 IEEE 28TH REAL-TIME AND EMBEDDED TECHNOLOGY AND APPLICATIONS SYMPOSIUM (RTAS), pp. 297–300.

By: H. Sung n, Y. Xu n, J. Guan*, W. Niu *, B. Ren *, Y. Wang *, S. Liu, X. Shen n 

TL;DR: It is shown that it is feasible to enable full leve1-4 autonomous driving workloads on a single off-the-shelf card (Jetson AGX Xavier) for less than 1.1 times less than the state-of- the-art systems, while meeting all the requirements of latency. (via Semantic Scholar)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: April 17, 2023

2021 article

Brief Industry Paper: Towards Real-Time 3D Object Detection for Autonomous Vehicles with Pruning Search

2021 IEEE 27TH REAL-TIME AND EMBEDDED TECHNOLOGY AND APPLICATIONS SYMPOSIUM (RTAS 2021), pp. 425–428.

By: P. Zhao *, W. Niu *, G. Yuan *, Y. Cai *, H. Sung n, S. Liu, S. Liu *, X. Shen n  ...

author keywords: 3D object detection; real-time; point cloud
TL;DR: It is demonstrated in experiments that for the first time, the pruning search framework can achieve real-time 3D object detection on mobile with state-of-the-art detection performance. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: November 29, 2021

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