C++ is an object-oriented programming (OOP) language but also supports generic and functional features. OOP is a potent tool when used wisely. Let me present you the good parts. You can design intuitive classes, which encapsulate its internals and allow restricted access. Further, inheritance applied appropriately enables you to build libraries or frameworks that are easy to use and difficult to misuse. The hidden source is the separation of interface and implementation. You can apply "Lesson Learned" from the best and implement design patterns using OOP techniques. Learn in my lecture the appropriate use of the keywords virtual, override, and final. Learn when you should build a class hierarchy to separate the interface from the implementation. Besides interface inheritance, you can also use implementation inheritance. Learn to distinguish between those.
During this lecture, I also present a few design patterns as examples of good object-oriented design. PUBLICATION PERMISSIONS:
CppCon Organizer provided Coding Tech with the permission to republish CppCon tech talks. CREDITS:
CppCon YouTube channel: https://www.youtube.com/channel/UCMlGfpWw-RUdWX_JbLCukXg https://www.youtube.com/watch?v=3dsCKaiC10c

Graphics processing unit (GPU) has become central to today’s scientific computing applications, such as machine learning and simulation. As the application complexity continues to grow, the need to quickly execute thousands of dependent GPU tasks has become a major bottleneck in the development flow. To overcome this challenge, modern CUDA has introduced CUDA Graph for users to directly offload a GPU task graph on a GPU to minimize scheduling overheads. However, programming CUDA Graph is extremely tedious and involves many low-level details that are difficult to program correctly. Consequently, we introduce in this paper, cudaFlow, a modern C++ programming model to streamline the building of large GPU workloads using CUDA Graph. cudaFlow enables efficient implementations of GPU decomposition strategies supplied with incremental update methods to express complex GPU algorithms that are hard to execute efficiently by mainstream stream-based models. We have demonstrated the simplicity and efficiency of cudaFlow on large-scale GPU applications composed of thousands of tasks and dependencies. The talk will cover five major components:
1. What is the new CUDA Graph programming model?
2. Why do we need a C++ programming model for GPU task graph parallelism?
3. Designs, implementations, and deployments of the proposed cudaFlow programming model.
4. Real use cases of cudaFlow and its performance advantages in large GPU workloads.
5. Remarks and roadmap suggestions for the GPU programming community. By the end of the presentation, the audience will know how to leverage the new GPU task graph parallelism to boost the performance of large-scale GPU applications, such as machine learning and scientific simulations. PUBLICATION PERMISSIONS:
CppCon Organizer provided Coding Tech with the permission to republish CppCon Tech Talks. CREDITS:
CppCon YouTube channel: https://www.youtube.com/channel/UCMlGfpWw-RUdWX_JbLCukXg https://www.youtube.com/watch?v=NwrFzWX5lnM

In this video, Surma and Jake pick up the topic of Color Spaces again, because there’s more to talk about. Device capabilities, perceptually uniform color spaces, color mixing and contrast calculations. And all of that is coming to CSS! PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=Uh95jZPTDfw https://www.youtube.com/watch?v=zdvyVV4QRz8