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High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers continually ...

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When you create an algorithm, you need to include precise, step-by-step instructions. This means you will need to break down the task or problem into smaller steps. We call this process decomposition.

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As Transformer models continue to grow in size and complexity, numerous high-fidelity pruning methods have been proposed to mitigate the increasing parameter count. However, transforming these ...

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Abstract: General sparse matrix-matrix multiplication (SpGEMM) is a fundamental computational method with wide-ranging applications in scientific simulations, machine learning, and image processing.

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