Jou-An Chen

College of Engineering

Works (4)

Updated: April 5th, 2024 16:07

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 journal article

Survey: Exploiting Data Redundancy for Optimization of Deep Learning

ACM COMPUTING SURVEYS, 55(10).

By: J. Chen n, W. Niu *, B. Ren *, Y. Wang * & X. Shen n 

author keywords: Data redundancy; representation redundancy; deep neural network; convolutional neural network; transformer
TL;DR: This article surveys hundreds of recent papers on data redundancy, introduces a novel taxonomy to put the various techniques into a single categorization framework, and offers a comprehensive description of the main methods used for exploiting data redundancy in improving multiple kinds of DNNs on data. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: March 6, 2023

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