A high-performance implementation of basic tensor operations using CUDA C, with Python bindings. This project demonstrates how to write custom CUDA kernels and compare their performance with PyTorch's ...

Parallel computing continues to advance, addressing the demands of high-performance tasks such as deep learning, scientific simulations, and data-intensive computations. A fundamental operation within ...

A Python-based web service for tensor operations and differential geometry calculations, supporting both symbolic (using SymPy) and numeric (using NumPy) computations. The backend is built with Django ...

Numerical libraries like NumPy, Tensorflow, and PyTorch implement various types of vectorized operations to facilitate scientific computing. This chapter examines various operations that are used on a ...

NVIDIA has this week announced the availability of its cuTENSOR v1.4, which now supports up to 64-dimensional tensors, distributed multi-GPU tensor operations, and helps improve tensor contraction ...