213 citations · 934 across the 47 of their papers we have counts for
6 papers · 1 filter
Accelerating CPU-Based Sparse General Matrix Multiplication With Binary Row Merging
Zhaoyang Du, Yijin Guan, Tianchan Guan +3
Sparse general matrix multiplication (SpGEMM) is a fundamental building block for many real-world applications. Since SpGEMM is a well-known memory-bounded application with vast an…
Efficient Sparse Matrix Kernels based on Adaptive Workload-Balancing and Parallel-Reduction
Guyue Huang, Guohao Dai, Yu Wang +2
Sparse matrix-vector and matrix-matrix multiplication (SpMV and SpMM) are fundamental in both conventional (graph analytics, scientific computing) and emerging (sparse DNN, GNN) do…
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
Yuke Wang, Boyuan Feng, Gushu Li +4
As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings). However, the…
Characterizing and Understanding GCNs on GPU
Mingyu Yan, Zhaodong Chen, Lei Deng +4
Graph convolutional neural networks (GCNs) have achieved state-of-the-art performance on graph-structured data analysis. Like traditional neural networks, training and inference of…
HyGCN: A GCN Accelerator with Hybrid Architecture
Mingyu Yan, Lei Deng, Xing Hu +6
In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator, HyGCN, using a hybrid arch…
AccD: A Compiler-based Framework for Accelerating Distance-related Algorithms on CPU-FPGA Platforms
Yuke Wang, Boyuan Feng, Gushu Li +3
As a promising solution to boost the performance of distance-related algorithms (e.g., K-means and KNN), FPGA-based acceleration attracts lots of attention, but also comes with num…