Taking the Coursera GPU Specialization was a great way to dive deeper into CUDA programming and parallel computing. This course helped me get hands-on experience with NVIDIA’s ecosystem, from writing CUDA kernels to working with high-level libraries. It wasn’t just about coding—it was about understanding how GPUs work and how to make the most out of them.
Why I Took This Specialization
At work, I was developing deep learning applications using TensorFlow for environment perception in self-driving cars. While working on these projects, I realized there was a lot of room for optimizing runtime performance. GPUs were already accelerating training, but I wanted to dig deeper into how to make inference more efficient. That’s what led me to CUDA and this specialization—it seemed like the perfect way to learn how to fine-tune performance at a lower level.
What the Specialization Covers
The Coursera GPU Programming Specialization, offered by Johns Hopkins University, is split into four courses, each covering different aspects of GPU programming:
- Introduction to Concurrent Programming with GPUs – This course sets the stage by explaining the basics of parallel programming with Python and C/C++. It also introduces CUDA and walks through CPU vs. GPU architectures.
- Introduction to Parallel Programming with CUDA – Here, things get more hands-on with CUDA development. The course teaches how to transform regular CPU algorithms into efficient CUDA kernels.
- CUDA at Scale for the Enterprise – This part covers working with multiple GPUs and CPUs, as well as optimizing GPU workloads for scalability and efficiency.
- CUDA Advanced Libraries – The final course focuses on using powerful NVIDIA libraries like cuFFT, cuBLAS, and cuDNN for high-performance computing and machine learning applications.
Each course includes practical projects, giving students a chance to apply what they learn in real-world scenarios.
Highlights of the Learning Journey
- Understanding CUDA and NVIDIA Libraries – The specialization does a great job of not just teaching CUDA but also giving a broad overview of NVIDIA’s extensive library ecosystem. It also sheds light on GPU hardware architecture and how it influences software optimization, helping me write more efficient code.
- Hands-on Projects – The course offers a great balance between accessibility and hands-on learning. On one hand, the projects have a low entrance barrier since they can be implemented directly within Coursera’s browser-based development environment. On the other hand, the specialization challenges students to set up their own local or cloud-based development environments, requiring them to configure the NVIDIA CUDA setup themselves. This combination ensures that students gain both a smooth introduction and real-world experience dealing with practical GPU programming challenges.
- Capstone Project: Edge Detection with CUDA and cuDNN – The capstone project was an exciting challenge that I tackled with a lot of motivation. As part of my work, I developed a custom edge detection filter using CUDA and cuDNN, replacing an older implementation that relied on NVIDIA’s NPP library. I put significant effort into optimizing the runtime, including leveraging CUDA Graphs to improve execution efficiency. To visually showcase the results of my work, I created a comparative video clip (watch here). The left side of the video displays the original driving footage from inside a car, while the right side features the same footage with my developed filter applied. This 10-second clip demonstrates the effectiveness of the filter in highlighting edges in real-world driving conditions. The full implementation of this project, along with detailed explanations, can be found in the GitHub repository coursera_cuda_advanced_libraries. Additionally, I published a presentation video clip on YouTube.
This project not only strengthened my CUDA skills but also deepened my understanding of GPU-accelerated image processing and optimization techniques.
Things That Could Be Better
While the specialization is solid, there are a few areas that could be improved:
- Lack of Coverage on Recent CUDA Features – Some newer CUDA developments, like CUDA Graphs and cuDNN Graphs, aren’t covered in the course. These are important for optimizing execution and should be included in future versions.
- Use of Deprecated cuDNN Functions – Some of the course materials rely on cuDNN functions that NVIDIA has since deprecated. Updating these lessons with best practices would make the content more relevant and useful for real-world applications.
- Not Enough Focus on Profiling, Benchmarking, and Debugging – Knowing how to write CUDA kernels is one thing, but optimizing and debugging them is another. The course could really benefit from more content on profiling tools, benchmarking different approaches, and debugging CUDA applications. These are essential skills for working on high-performance GPU applications.
Final Thoughts
Overall, the Coursera GPU Specialization was a great experience. It helped me gain confidence in CUDA programming and GPU optimization, and I now have a much better understanding of how to accelerate applications beyond just using high-level frameworks like TensorFlow. While there’s room for improvement, especially in covering newer CUDA features and performance profiling tools, I’d still highly recommend this specialization to anyone interested in GPU computing.
Dr. Alexander Braun 