Parallel processing

Lec 46: RAPIDS, Spark and Handling Skew

Lec 45: Dask and Ray Python-First Distributed Data

Lec 40: Graph Tracing, Batching & Concurrency

Lec 26: Memory and throughput techniques for LLM training

Code with Me: Run Your Polars Code on the GPU

Digital Design & Comp. Arch: L20b: GPU Programming (Spring 2026)

Digital Design & Comp. Arch: L19: SIMD Architectures (Spring 2026)

Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 5: GPUs, TPUs

CUDA Programming for NVIDIA H100s – Comprehensive Course

CUDA: New Features and Beyond | NVIDIA GTC

CUDA Live: Your Parallel Programming Guide

Unlocking GPU Performance with CUDA Tile

Comp. Arch. - Lecture 30: GPU Programming (Fall 2025)

Comp. Arch. - Lecture 29: SIMD and GPU Architectures (Fall 2025)

Comp. Arch. - Lecture 28: Systolic Array Architectures (Fall 2025)

Comp. Arch. - Lecture 21: Multiprocessors II, Memory Ordering and Cache Coherence (Fall 2025)

Deep Dive: How to Use cuTile Python

Accelerating Science and Engineering With NVIDIA CUDA-X Libraries | NVIDIA GTC D.C.

CUDA 13.0—New Features and Beyond | NVIDIA GTC D.C.

GPU Cache Locality

Implementing New Algorithm with CUDA Kernels | CUDA C++ Class Part 3

Asynchrony and CUDA Streams | CUDA C++ Class Part 2

Accelerating Applications with Parallel Algorithms | CUDA C++ Class Part 1

Day 1: Jack Dongarra - High Performance Computing and Responsibly Reckless Algorithms

Inmaculada García: “La informática del futuro tiene que ser también una informática sostenible”

Inmaculada García: “The computer science of the future must also be sustainable computer science”

Winning The Race: Multithreading & You | Andrew Joy | NZGDC 2025

CUDA Programming Course – High-Performance Computing with GPUs