Matrix multiplication is a key operation in scientific computing and machine learning, with GPU libraries like NVIDIA Cutlass and cuBLAS providing optimized implementations of the three nested loop ...

ABSTRACT: Purpose: The calculation of triangular numbers using the conventional formula T n = n( n+1 )/2 becomes computationally infeasible for astronomically large values of n (e.g., numbers with 10 ...

Matrix multiplication is one of the most basic algebraic operations. Since Strassen's surprising breakthrough algorithm from 1969, which showed that matrices can be multiplied faster than the most ...

DeepSeek researchers are trying to solve a precise issue in large language model training. Residual connections made very deep networks trainable, hyper connections widened that residual stream, and ...

Siddhesh Surve is an accomplished Engineering leader with topics of interest including AI, ML, DS, DE, Cloud compute. We need to talk about the "Grind." You know the one. You spend weeks memorizing ...

The original version of this story appeared in Quanta Magazine. If you want to solve a tricky problem, it often helps to get organized. You might, for example, break the problem into pieces and tackle ...

In 1971, German mathematicians Schönhage and Strassen predicted a faster algorithm for multiplying large numbers, but it remained unproven for decades. Mathematicians from Australia and France have ...

Here's what you'll learn when you read this story: In 1971, German mathematicians Schönhage and Strassen predicted a faster algorithm for multiplying large numbers, but it remained unproven for ...

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 ...