Abstract: Sparse General Matrix-Matrix Multiplication (SpGEMM) is a core operation in high-performance computing applications such as algebraic multigrid solvers, machine learning, and graph ...
Sparse Autoencoders (SAEs) have recently gained attention as a means to improve the interpretability and steerability of Large Language Models (LLMs), both of which are essential for AI safety. In ...
NVIDIA releases detailed cuTile Python tutorial for Blackwell GPUs, demonstrating matrix multiplication achieving over 90% of cuBLAS performance with simplified code. NVIDIA has published a ...
Sparse matrix-matrix multiplication (SpMM) is a crucial kernel in various applications, including sparse deep neural networks [1]–[6], graph analytics [7], triangle counting [8], and linear algebra ...
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