Blar i NTNU Open på forfatter "Liu, Weifeng"
-
Back-Dropout Transfer Learning for Action Recognition
Ren, Huamin; Kanhabua, Nattiya; Møgelmose, Andreas; Liu, Weifeng; Kulkarni, Kaustubh; Escalera, Sergio; Baró, Xavier; Moeslund, Thomas B. (Journal article; Peer reviewed, 2018)Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has ... -
clMF: A Fine-Grained and Portable Alternating Least Squares Algorithm for Parallel Matrix Factorization
Chen, Jing; Fang, Jianbin; Liu, Weifeng; Tang, Tao; Yang, Canqun (Journal article, 2018)Alternating least squares (ALS) has been proved to be an effective solver for matrix factorization in recommender systems. To speed up factorizing performance, various parallel ALS solvers have been proposed to leverage ... -
Exploring and Analyzing the Real Impact of Modern On-Package Memory on HPC Scientific Kernels
Li, Ang; Liu, Weifeng; Kristensen, Mads R. B.; Vinter, Brian; Wang, Hao; Hou, Kaixi; Marquez, Andres; Song, Shuaiwen Leon (Journal article; Peer reviewed, 2017)High-bandwidth On-Package Memory (OPM) innovates the conventional memory hierarchy by augmenting a new on-package layer between classic on-chip cache and off-chip DRAM. Due to its relative location and capacity, OPM is ... -
Fast synchronization-free algorithms for parallel sparse triangular solves with multiple right-hand sides
Liu, Weifeng; Li, Ang; Hogg, Jonathan D; Duff, Iain S; Vinter, Brian (Journal article, 2017)The sparse triangular solve kernels, SpTRSV and SpTRSM, are important building blocks for a number of numerical linear algebra routines. Parallelizing SpTRSV and SpTRSM on today's manycore platforms, such as GPUs, is not ... -
Register-Aware Optimizations for Parallel Sparse Matrix-Matrix Multiplication
Liu, Junhong; He, Xin; Liu, Weifeng; Tan, Guangming (Journal article; Peer reviewed, 2018)General sparse matrix–matrix multiplication (SpGEMM) is a fundamental building block of a number of high-level algorithms and real-world applications. In recent years, several efficient SpGEMM algorithms have been proposed ... -
Register-based Implementation of the Sparse General Matrix-matrix Multiplication on GPUs
Liu, Junhong; He, Xin; Liu, Weifeng; Tan, Guangming (Journal article; Peer reviewed, 2018)General sparse matrix-matrix multiplication (SpGEMM) is an essential building block in a number of applications. In our work, we fully utilize GPU registers and shared memory to implement an efficient and load balanced ... -
swSpTRSV: A Fast Sparse Triangular Solve with Sparse Level Tile Layout on Sunway Architectures
Wang, Xinliang; Liu, Weifeng; Xue, Wei; Wu, Li (Journal article; Peer reviewed, 2018)Sparse triangular solve (SpTRSV) is one of the most important kernels in many real-world applications. Currently, much research on parallel SpTRSV focuses on level-set construction for reducing the number of inter-level ...