Yufei Ding

Works (5)

Updated: July 5th, 2023 15:41

2018 article

Reuse-Centric K-Means Configuration

Guan, H., Ding, Y., Shen, X., & Krim, H. (2018, April 1). 2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), pp. 1224–1227.

By: H. Guan n, Y. Ding n, X. Shen n  & H. Krim n 

topics (OpenAlex): Data Mining Algorithms and Applications; Data Stream Mining Techniques; Face and Expression Recognition
TL;DR: A set of novel techniques are presented, including reuse-based filtering, center reuse, and a two-phase design to capitalize on the reuse opportunities on three levels: validation, k, and feature sets, to accelerate k-means configuration. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: November 11, 2019

2017 article

Generalizations of the theory and deployment of triangular inequality for compiler-based strength reduction

Ding, Y., Ning, L., Guan, H., & Shen, X. (2017, June 14). ACM SIGPLAN Notices, Vol. 52, pp. 33–48.

By: Y. Ding n, L. Ning n, H. Guan n & X. Shen n 

topics (OpenAlex): Metaheuristic Optimization Algorithms Research; Machine Learning and Algorithms; Advanced Image and Video Retrieval Techniques
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018

2017 article

Sweet KNN: An Efficient KNN on GPU through Reconciliation between Redundancy Removal and Regularity

Chen, G., Ding, Y., & Shen, X. (2017, April 1). 2017 Ieee 33rd International Conference on Data Engineering (Icde 2017), pp. 621–632.

By: G. Chen  n, Y. Ding n & X. Shen n 

topics (OpenAlex): Advanced Image and Video Retrieval Techniques; Advanced Neural Network Applications; Data Management and Algorithms
TL;DR: This work gives a detailed study on how to effectively combine the strengths of both approaches to create a new KNN on GPU named Sweet KNN, the first high-performance triangular-inequality-based Knn on GPU that manages to reach a sweet point between redundancy minimization and regularity preservation for various datasets. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2015 article

Autotuning algorithmic choice for input sensitivity

Ding, Y., Ansel, J., Veeramachaneni, K., Shen, X., Una-May, O. R., & Amarasinghe, S. (2015, June 3). ACM SIGPLAN Notices, Vol. 50, pp. 379–390.

By: Y. Ding n, J. Ansel *, K. Veeramachaneni*, X. Shen n , O. Una-May * & S. Amarasinghe *

author keywords: Algorithms; Languages; Performance; Petabricks; Autotuning; Algorithmic Optimization; Input Adaptive; Input Sensitivity; Two-level Input Learning
topics (OpenAlex): Parallel Computing and Optimization Techniques; Logic, programming, and type systems; Numerical Methods and Algorithms
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2014 article

Call sequence prediction through probabilistic calling automata

Zhao, Z., Wu, B., Zhou, M., Ding, Y., Sun, J., Shen, X., & Wu, Y. (2014, October 15). ACM SIGPLAN Notices, Vol. 49, pp. 745–762.

By: Z. Zhao *, B. Wu *, M. Zhou *, Y. Ding n, J. Sun *, X. Shen n , Y. Wu*

author keywords: Languages; Performance; Function call; Call sequence prediction; Probabilistic calling automata; Dynamic optimizations; Just-in-time compilation; Parallel compilation
topics (OpenAlex): Software Engineering Research; Software System Performance and Reliability; Software Testing and Debugging Techniques
TL;DR: A new way to enable call sequence prediction is presented, which exploits program structures through Probabilistic Calling Automata (PCA), a new program representation that captures both the inherent ensuing relations among function calls, and the probabilistic nature of execution paths. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

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