Intel oneAPI AI Analytics Toolkit — Introduction and Install with conda

I recently wrote a post introducing Intel oneAPI that included a simple installation guide of the Base Toolkit. In that post I promised a follow-up about the the oneAPI AI Analytics Toolkit. This is it! I’ll describe what it is and give recommendations for doing an install setup of the AI toolkits using conda with Anaconda Python.

Intel oneAPI Developer Tools — Introduction and Install

Intel oneAPI is a massive collection of very high quality developer tools, and, it’s free to use! In this post I’ll give you a little background on what oneAPI is and my recommendations for doing an install setup to get started exploring the collection of tool-kits.

PyTorch for Scientific Computing – Quantum Mechanics Example Part 4) Full Code Optimizations — 16000 times faster on a Titan V GPU

This is the 16000 times speedup code optimizations for the scientific computing with PyTorch Quantum Mechanics example. The following quote says a lot,

“The big magic is that on the Titan V GPU, with batched tensor algorithms, those million terms are all computed in the same time it would take to compute 1!!!”

PyTorch for Scientific Computing – Quantum Mechanics Example Part 3) Code Optimizations – Batched Matrix Operations, Cholesky Decomposition and Inverse

An amazing result in this testing is that “batched” code ran in constant time on the GPU. That means that doing the Cholesky decomposition on 1 million matrices took the same amount of time as it did with 10 matrices!

In this post we start looking at performance optimization for the Quantum Mechanics problem/code presented in the first 2 posts. This is the start of the promise to make the code over 15,000 times faster! I still find the speedup hard to believe but it turns out little things can make a big difference.