CUDA Courses and Certifications

CUDA at Scale for the Enterprise

26 ratings at Coursera

Explore advanced GPU programming techniques for enterprise-scale applications, including multi-GPU systems, asynchronous workflows, and optimized algorithms for sorting and image processing.

• Coursera
• 1 day 4 hours 45 minutes
• Self-Paced
• Paid Course

CUDA Advanced Libraries

22 ratings at Coursera

Learn to use CUDA's advanced libraries like CuFFT, cuBLAS, and Thrust for complex computations and machine learning applications, including object detection and language translation.

• Coursera
• 1 day 2 hours 16 minutes
• Self-Paced
• Paid Course

Introduction to Parallel Programming with CUDA

Learn parallel programming with CUDA to process large datasets using GPUs. Explore thread management, memory types, and performance optimization techniques for complex problem-solving on Nvidia hardware.

• Coursera
• 21 hours 39 minutes
• Self-Paced
• Paid Course

Mastering Parallel programming with CUDA platform

1870 ratings at Udemy

Unofficial guide to parallel programming on GPU's with CUDA from basic concepts to advance algorithm implementations.

• Udemy
• 10 hours 48 minutes
• Self-Paced
• Paid Course

Packt

GPU Programming with C++ and CUDA

Master GPU programming with C++ and CUDA, covering parallelism, optimization strategies, multi-GPU usage, and Python integration to build high-performance computing applications.

• Coursera
• 12 hours 19 minutes
• Self-Paced
• Paid Course

Mastering GPU Parallel Programming with CUDA: ( HW & SW )

569 ratings at Udemy

Performance Optimization and Analysis for High-Performance Computing

• Udemy
• 23 hours 3 minutes
• Self-Paced
• Paid Course

CUDA Programming - High-Performance Computing with GPUs

3 ratings

Master CUDA programming for GPU-accelerated computing and deep learning. Covers ecosystem, setup, kernels, API, optimizations, Triton, PyTorch extensions, and practical MNIST implementation.

• freeCodeCamp
• 11 hours 55 minutes
• Self-Paced
• Free Video

Learn to use CUDA C/C++ tools and techniques to accelerate CPU-only applications to run on massively parallel GPUs.

• Independent
• 8 hours
• Self-Paced
• $90.00

Nvidia

Improvements to NVIDIA CUDA and Deep Learning Libraries - Session 1

Explore NVIDIA's latest AI tech advancements, including CUDA Graphs, WMMA, and cuDNN. Learn about performance improvements, Tensor Cores, and new features for deep learning acceleration.

• YouTube
• 42 minutes
• Self-Paced
• Free Video

Nvidia

Parallel Nsight 2.0 and CUDA 4.0 for GPU Computing - SIGGRAPH 2011

Explore GPU computing with CUDA 4.0 and Parallel Nsight 2.0, integrated into Visual Studio for enhanced graphics and parallel application development.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

CUTLASS: A CUDA C++ Template Library for Accelerating Deep Learning Computations

Explore CUTLASS, an open-source CUDA C++ template library for optimizing deep learning computations on NVIDIA GPUs. Learn to develop high-performance custom kernels using Tensor Core programming.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

Nvidia

1001 Ways to Write CUDA Kernels in Python

Explore best practices for writing CUDA kernels in Python, covering memory management, thread organization, and optimization techniques to harness GPU acceleration without leaving the Python ecosystem.

• YouTube
• 38 minutes
• Self-Paced
• Free Video

Nvidia

Accelerating Quantum Supercomputing with CUDA-Q

Unlock quantum supercomputing potential with CUDA-Q, an open-source platform for hybrid quantum-classical applications through expert panel discussions and practical guidance.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Eli the Computer Guy

NVIDIA CUDA on RISC-V - Kinda Cool, But Probably Worthless

Explore NVIDIA's CUDA implementation on RISC-V architecture and evaluate its practical applications and limitations in modern computing environments.

• YouTube
• 14 minutes
• Self-Paced
• Free Video

WeAreDevelopers

CUDA Programming in Python - From Abstractions to Native Implementation

Master GPU programming with CUDA directly in Python, exploring NVIDIA's native implementation and current platform access methods for high-performance computing.

• YouTube
• 21 minutes
• Self-Paced
• Free Video