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 ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

Abstract: We demonstrate an optical general matrix multiplication using incoherent light source and wavelength multiplexing to multiply two two-dimensional matrices with positive and negative elements ...

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 ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

Matrix multiplication is a fundamental operation in linear algebra, but its behavior can seem a bit strange at first. The key to understanding it lies in understanding how the dimensions of the ...

Sikorsky to add add cutting-edge MATRIX technology to the UH-60M Black Hawk, pushing the limits of Autonomous Military Aviation. Sikorsky, has secured a $6 million contract from DARPA (Defense ...

Parallel Computing starter project to build GPU & CPU kernels in CUDA & C++ and call them from Python without a single line of CMake using PyBind11 ...