This issue is an evolving document describing the design and implementation of using sparse matrix instructions for optimizing skinny GEMM on AMDGPU in IREE. Using FP8 as an example: currently skinny ...

Abstract: Sparse Matrix-Matrix Multiplication (SpMM) is a widely used algorithm in Machine Learning, particularly in the increasingly popular Graph Neural Networks (GNNs). SpMM is an essential ...

CNBC's Squawk Box Asia Martin Soong and Chery Kang talk about AMD's chip supply deal with OpenAI, plus the web of alliances, cross shareholdings and the money loop that could shape the AI space. Major ...

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Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...

Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most difficult tasks in numerical ...

Abstract: We investigate the performance of algorithms for sparse tensor-sparse tensor multiplication (SpGETT). This operation, also called sparse tensor contraction, is a higher order analogue of the ...

ABSTRACT: Node renumbering is an important step in the solution of sparse systems of equations. It aims to reduce the bandwidth and profile of the matrix. This allows for the speeding up of the ...

ABSTRACT: Node renumbering is an important step in the solution of sparse systems of equations. It aims to reduce the bandwidth and profile of the matrix. This allows for the speeding up of the ...