Implemented the conventional and Strassen's matrix multiplication algorithms for 𝑛 × 𝑛 matrices and determined the optimal cross-over point both analytically and experimentally. For 𝑛 × 𝑛 matrices ...

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

The project presents the development and implementation of parallel algorithms for matrix-matrix multiplication aimed at effectively large scale computational tasks.Leveraging modern parallel ...

These include algorithms for solving linear systems, least squares problems, eigenvalue problems, and parallelization of Strassen’s matrix multiplication algorithm. In particular, not only does ...

According to Google DeepMind, AlphaEvolve has successfully discovered multiple new algorithms for matrix multiplication, surpassing the previous AlphaTensor model in efficiency and performance (source ...

For many scientific applications, dense matrix multiplication is one of the most important and computation intensive linear algebra operations. An efficient matrix multiplication on high performance ...