* Program re-ordering for improved L2 cache hit rate. * Automatic performance tuning. # Motivations # Matrix multiplications are a key building block of most modern high-performance computing systems.

AMD and Intel have now published a full technical specification for ACE — AI Compute Extensions — the most significant overhaul to x86 AI compute in the architecture's history, co-authored by eight ...

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 ...

In this tutorial, we implement an advanced hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for writing efficient CUDA-style kernels directly in Python. We start by ...

Google develops search, advertising, cloud, and AI technologies at global scale. Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the ...

ABSTRACT: The deployment of Large Language Models (LLMs) on edge devices represents a paradigm shift in artificial intelligence, transitioning from cloud-centric dependence to pervasive, ...

When I wrote about recursive artificial intelligence last September, I described an innovation still in its early stages, powerful in concept, but not fully realized in practice. Six months on, AI has ...

This document is designed to help users quickly understand, use, and maintain the Python implementation of the Matrix-Sparsity-Based Pauli Decomposition (MSPD) algorithm. It specifies the function, ...

If it feels like social platforms suddenly “get” you more than they used to, you’re not imagining it! In 2026, feeds aren’t only reacting to what you click anymore. They’re predicting what you ...

In 2023, the website then known as Twitter partially open sourced its algorithm for the first time. In those days, Tesla billionaire Elon Musk had only recently acquired the platform, and he claimed ...