AFH-SpMM is a novel Auto-Fit Heterogeneous SpMM framework designed for adaptively parallelizing sparse-dense matrix multiplication on Tensor Core-equipped GPUs that achieves average speedups of 1.33 ×, and often leads cuSPARSE, ASpT, Sputnik, RoDe, Acc-SpMM, and MP-SpMM, with especially strong gains on medium and large...
Zhi-Rui Chen, Heng Zhang, Kai-Fan Jia· Proceedings of the Internati...· 0 citations
CaN is proposed, a core-aware neural generation framework for attributed hypergraphs that integrates the hierarchical k-core structure as an explicit generative prior and uses deep neural encoders to model dependencies among multi-dimensional node attributes.
Xiangfei Fang, Ran Bao, Heng Zhang· Proceedings of the 32nd ACM...· 0 citations
Sparse-dense matrix multiplication (SpMM), a fundamental computational kernel in graph analytics and scientific computing, can be substantially accelerated on modern GPUs by leveraging both dense and sparse Tensor Core units, thereby enabling high-throughput computation. However, existing methods often fail to exploit...
Zhi-Rui Chen, Heng Zhang, Kai-Fan Jia· Proceedings of the Internati...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
