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
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
Temporal Graph Neural Networks (T-GNNs) are widely used to model continuous-time dynamic graphs. However, training T-GNNs on large temporal graphs remains computationally prohibitive due to a fundamental challenge: existing systems enforce a stop-line execution paradigm that serializes batch data preparation with GPU c...
Yong-Chun Jiang, Yong-Chao Liu, Kai-Fan Jia et al.· Proceedings of the Internati...· 0 citations
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