The acceleration of Graph Neural Networks (GNNs) requires efficient and framework-
compatible Sparse-Dense Matrix-Matrix Multiplication (SpMM). From the compatibility …

The lottery ticket hypothesis (LTH) has shown that dense models contain highly sparse
subnetworks (ie, winning tickets) that can be trained in isolation to match full accuracy …

Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …

Important workloads, such as machine learning and graph analytics applications, heavily
involve sparse linear algebra operations. These operations use sparse matrix compression …

We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on
the GPU. Our algorithms expect the sparse input in the popular compressed-sparse-row …

Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph
data across various domains, yet their acceleration on mainstream GPUs is challenged by …

Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …

The latest hardware accelerators proposed for graph applications primarily focus on graph
neural networks (GNNs) and graph mining. High-level graph reasoning tasks, such as graph …

Graph representation learning aims to learn low-dimensional node embeddings for graphs.
It is used in several real-world applications such as social network analysis and large-scale …

Tiling is a key technique to reduce data movement in matrix computations. While tiling is well
understood and widely used for dense matrix/tensor computations, effective tiling of sparse …