51 citations · 190 across the 18 of their papers we have counts for
20 papers
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization
Hengrui Luo, Younghyun Cho, James W. Demmel +2
This paper presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualita…
Dr. Top-k: Delegate-Centric Top-k on GPUs
Anil Gaihre, Da Zheng, Scott Weitze +5
Recent top- computation efforts explore the possibility of revising various sorting algorithms to answer top- queries on GPUs. These endeavors, unfortunately, perform signifi…
Non-smooth Bayesian Optimization in Tuning Problems
Hengrui Luo, James W. Demmel, Younghyun Cho +2
Building surrogate models is one common approach when we attempt to learn unknown black-box functions. Bayesian optimization provides a framework which allows us to build surrogate…
TRUST: Triangle Counting Reloaded on GPUs
Santosh Pandey, Zhibin Wang, Sheng Zhong +8
Triangle counting is a building block for a wide range of graph applications. Traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric tr…
Resiliency in Numerical Algorithm Design for Extreme Scale Simulations
Emmanuel Agullo, Mirco Altenbernd, Hartwig Anzt +33
This work is based on the seminar titled ``Resiliency in Numerical Algorithm Design for Extreme Scale Simulations'' held March 1-6, 2020 at Schloss Dagstuhl, that was attended by a…
C-SAW: A Framework for Graph Sampling and Random Walk on GPUs
Santosh Pandey, Lingda Li, Adolfy Hoisie +2
Many applications require to learn, mine, analyze and visualize large-scale graphs. These graphs are often too large to be addressed efficiently using conventional graph processing…