3 papers
cs.PF2026
Scaling Fourier-Based Sparse Matrix Analysis on GPUs
Ruifeng Zhang, Sai Krishna Teja Varma Manthena, Jiajia Li +1
Sparse computations are important workloads in applications such as scientific computing, graph neural networks (GNNs), and machine learning. While many sparse operations can benef…
cs.PF2026
Spectral Analysis for Sparse Matrix Computation: Insights and Potential
Ruifeng Zhang, Xipeng Shen
Sparse computations are fundamental to scientific computing, graph analytics, and machine learning, yet their performance is highly sensitive to the diverse sparsity and patterns.…
cs.DC2024
ESG: Pipeline-Conscious Efficient Scheduling of DNN Workflows on Serverless Platforms with Shareable GPUs
Xinning Hui, Yuanchao Xu, Zhishan Guo +1
Recent years have witnessed increasing interest in machine learning inferences on serverless computing for its auto-scaling and cost effective properties. Existing serverless compu…