GPU Programming with CUDA Training in Ontario

GPU Programming with CUDA Training in Ontario, Canada

The course covers fundamental CUDA concepts, including threads, blocks, and grids.

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GPU Programming with CUDA Training is a professional training program delivered by ProgNXT in Ontario, Canada. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this GPU Programming with CUDA Training course with hands-on, instructor-led sessions. GPU Programming with CUDA training provides participants with the knowledge to harness the computational power of GPUs for high-performance parallel...

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Designed and delivered by the ProgNXT Programming Expert Panel — a vetted team of industry professionals with 14+ years of hands-on experience in Python, Java, C++, and full-stack application development

ProgNXT Programming Expert Panel

Course Code: PRP06
21 Hrs
Last Updated: April 2026

Course Rating5/5

GPU Programming with CUDA training provides participants with the knowledge to harness the computational power of GPUs for high-performance parallel computing. The course covers fundamental CUDA concepts, including threads, blocks, and grids, enabling participants to write efficient parallel programs. Attendees will learn to optimize memory usage, handle data transfers between host and device, and debug GPU applications effectively. Advanced topics include leveraging libraries like cuBLAS and cuDNN for accelerated mathematical operations and integrating CUDA with existing workflows. By the end of the training, participants will be skilled in developing and optimizing GPU-accelerated applications for a wide range of computational tasks.

Basic programming knowledge in C/C++ or Python.
Understanding of parallel computing concepts (preferred but not mandatory).

Upon completing this training, participants will:

Understand GPU architecture and the CUDA programming model.
Develop, optimize, and debug CUDA applications for parallel computation.
Leverage GPU acceleration for performance-critical applications.

CUDA Basics: Understanding the fundamentals of CUDA and GPU programming.
GPU Architecture: Proficiency in leveraging GPU hardware for computation.
Parallel Programming: Ability to design and implement parallel algorithms using CUDA.
Memory Management: Skills in optimizing memory usage for GPU performance.
Debugging and Profiling: Knowledge of debugging and profiling CUDA applications.

Participants will be assessed on:

CUDA Fundamentals: Understanding of CUDA programming basics and syntax.
Kernel Implementation: Proficiency in writing and optimizing CUDA kernels.
Parallel Algorithm Design: Ability to implement parallel algorithms using CUDA.
Memory Optimization: Effective use of shared, global, and texture memory.
Debugging and Optimization: Skills in debugging and profiling CUDA applications.

Certificate of Completion: Participants who attend all sessions and successfully complete the assessments will receive a ProgNXT Certificate of Completion for GPU Programming with CUDA Training.

Introduction to GPU Programming

Introduction to GPU Computing

Evolution of GPU computing.
Comparison of CPU vs. GPU architectures.
Applications of GPU programming in various domains.

CUDA Fundamentals

Overview of the CUDA architecture and toolkit.
Setting up the CUDA environment.
Writing and executing the first CUDA program.

CUDA Programming Basics

Thread hierarchy: grids, blocks, and threads.
Kernel functions and their execution.
Practical exercise

Memory Model in CUDA

Types of memory: global, shared, constant, and texture.
Memory access patterns and optimization techniques.
Practical exercise

Intermediate CUDA Programming

Thread Synchronization and Communication

Synchronization techniques using shared memory.
Atomic operations and their usage.
Practical exercise

Performance Optimization Techniques

Optimizing memory access and minimizing latency.
Efficient utilization of registers and shared memory.
Profiling tools for performance analysis (Nsight, nvprof).
Practical exercise

CUDA Libraries and APIs

Introduction to Thrust, cuBLAS, and cuDNN libraries.
Using CUDA libraries for common computational tasks.
Practical exercise

Advanced Topics and Applications

Advanced CUDA Concepts

Dynamic parallelism and unified memory.
Streams and asynchronous execution.
Practical exercise

Multi-GPU Programming

Basics of multi-GPU programming.
Using CUDA-aware MPI for distributed GPU computation.
Practical exercise

Real-World Applications

Case studies: GPU-accelerated applications in AI, scientific computing, and image processing.
Hands-on project

Debugging and Error Handling

Common debugging tools and techniques.
Best practices for error handling in CUDA applications.
Practical exercise

Course Wrap-Up

Summary of key concepts and tools.
Open Q&A session and guidance on further learning.
Feedback and certification distribution.

Comprehensive course materials, including sample code and presentations.
Access to CUDA programming tools and libraries.
Additional resources for further exploration of GPU programming.

Evaluation through practical exercises and the final hands-on project.
ProgNXT Certification awarded upon successful completion of the course.

This course equips participants with the skills to harness the power of GPU computing for performance-critical applications using CUDA.

GPU Programming with CUDA Training Events in ontario

Online Events

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More Upcoming Session Dates

October 2026

📍 Ontario, Canada

22nd Oct 2026→24th Oct 2026

🌐 Online Instructor-Led

23rd Oct 2026→25th Oct 2026

November 2026

📍 Ontario, Canada

1st Nov 2026→3rd Nov 2026

🌐 Online Instructor-Led

10th Nov 2026→12th Nov 2026

📍 Ontario, Canada

23rd Nov 2026→25th Nov 2026

🌐 Online Instructor-Led

24th Nov 2026→26th Nov 2026

December 2026

🌐 Online Instructor-Led

7th Dec 2026→9th Dec 2026

📍 Ontario, Canada

10th Dec 2026→12th Dec 2026

🌐 Online Instructor-Led

25th Dec 2026→27th Dec 2026

📍 Ontario, Canada

28th Dec 2026→30th Dec 2026

Events for other courses in Ontario, Canada

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GPU Programming with CUDA Training in Ontario