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CUDA Programming

Unlock the power of the GPU. Master parallel programming with NVIDIA CUDA to accelerate scientific computing, AI, and simulations.

CPUs have few cores; GPUs have thousands. This course teaches Heterogeneous Computing using NVIDIA CUDA C++. You will learn the GPU architecture (Grids, Blocks, Threads), memory hierarchy (Global, Shared, Constant), and how to write Kernels that execute in parallel. We apply these skills to matrix multiplication, image processing, and simulation. This is the enabling technology behind modern AI training, crypto mining, and high-performance physics simulations.

✅ 100% Free & Lifetime Access
⏱️ 5-Minute Lessons (Bite-sized learning)
🚀 21-Lesson Path (Independent modules)
📱 Mobile Friendly (Learn anywhere)
GPU Devs
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Secure Enrollment via SSL

Complete Course Syllabus

  • 1
    GPU Architecture
    Streaming Multiprocessors and the thread hierarchy.
  • 2
    Writing Kernels
    Defining parallel functions and launching grids.
  • 3
    Memory Hierarchy
    Using fast Shared Memory to reduce global bandwidth.
  • 4
    Synchronization
    Coordinating threads within blocks safely.
  • 5
    Profiling
    Identifying bottlenecks and optimizing occupancy.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary
$140k - $190k

Career Paths

GPU Compute Engineer $150k-$200k
HPC Engineer $140k-$190k
AI Infrastructure Eng $145k-$195k

What You Will Learn

Write parallel CUDA kernels to accelerate C++ applications
Optimize memory access using Shared and Constant memory
Manage data transfer between Host (CPU) and Device (GPU)
Debug and profile GPU code using Nsight Systems
Understand the SIMT (Single Instruction Multiple Thread) model

Skills You Will Gain

CUDA C++ Parallel Computing GPU Architecture Performance Optimization HPC

Who Is This For

HPC Engineers
AI Researchers
Graphics Programmers

Prerequisites

Strong C/C++
Matrix Math

CUDA Programming FAQs

Gaming?

Focus is Compute (GPGPU), not rendering graphics.

NVIDIA only?

CUDA is NVIDIA specific; concepts apply to OpenCL/HIP.

Hardware?

Need an NVIDIA GPU (or cloud instance).

AI?

This is the low-level code that powers PyTorch/TF.

Need more info?
All Reork courses are 100% free, self-paced, and fully accessible on mobile devices. No prior experience is required. Read our full Platform FAQ.

Tech Stack & Tools

NVCC Compiler
NVIDIA GPU
Nsight
Visual Studio

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