Yufei Ding

Works (5)

Updated: July 5th, 2023 15:41

2018 article

Reuse-Centric K-Means Configuration

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

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 conference paper

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

ACM SIGPLAN Notices, 52(6), 33–48.

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

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

2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017), pp. 621–632.

By: G. Chen n, Y. Ding n & X. Shen n 

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)
UN Sustainable Development Goal Categories
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., O'Reilly, U.-M., & Amarasinghe, S. (2015, June). ACM SIGPLAN NOTICES, Vol. 50, pp. 379–390.

By: Y. Ding n, J. Ansel*, K. Veeramachaneni*, X. Shen n , U. O'Reilly* & S. Amarasinghe *

author keywords: Algorithms; Languages; Performance; Petabricks; Autotuning; Algorithmic Optimization; Input Adaptive; Input Sensitivity; Two-level Input Learning
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). 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
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)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

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