High Performance Scientific Computing with C 4.4: GPGPU with CUDA

The video tutorial explores the use of Nvidia's Cuda Library to accelerate code execution by leveraging GPU capabilities. It demonstrates how to modify C code to utilize Cuda, compile both serial and Cuda versions, and compare their performance. The tutorial also covers optimizing Cuda performance by adjusting threadpool sizes and concludes with a summary of the course's key learnings.

What is the primary purpose of using CUDA in programming?

• To simplify the syntax of C programming

• To reduce the memory usage of applications

• To enhance the graphical interface of applications

• To accelerate code by utilizing the GPU's processing power

Which keyword is added to a function to indicate it will run on the GPU in CUDA?

• local

• shared

• global

• device

Why is a different random number generator used for CUDA compared to the CPU?

• CUDA requires a more complex algorithm

• The default CPU generator is not compatible with GPU threads

• GPU threads need synchronized random numbers

• CUDA does not support random number generation

What is the role of threads and blocks in CUDA programming?

• They define the structure of the code

• They determine the memory allocation for the program

• They organize the execution of parallel tasks on the GPU

• They are used to debug the CUDA code

What is a significant advantage of using CUDA for large data sets?

• It reduces the code complexity

• It provides a graphical user interface

• It significantly speeds up computation time

• It decreases the need for memory

What is a potential drawback of using CUDA for small data sets?

• It requires more memory

• It can be slower due to initialization overhead

• It complicates the code structure

• It limits the number of threads

What is the main takeaway from the course regarding the use of CUDA?

• CUDA simplifies the process of writing algorithms

• CUDA is primarily for reducing memory usage

• CUDA can transform unused hardware into a powerful computing resource

• CUDA is only useful for graphical applications