Podcast
Questions and Answers
What is the main purpose of CUDA programming?
What is the main purpose of CUDA programming?
- To restrict programming flexibility
- To accelerate applications using only the CPU
- To limit the use of GPU computing
- To enable parallel computing on GPU and CPU (correct)
Which programming languages are commonly used for GPU programming?
Which programming languages are commonly used for GPU programming?
- C++ and Python (correct)
- C# and Swift
- HTML and CSS
- Java and Ruby
What does CUDA stand for?
What does CUDA stand for?
- Central Unified Design Architecture
- Central Unified Device Architecture
- Computer Unified Design Architecture
- Computer Unified Device Architecture (correct)
How many fundamental issues are mentioned in programming a GPU?
How many fundamental issues are mentioned in programming a GPU?
What distinguishes CUDA-enabled GPUs from other GPUs?
What distinguishes CUDA-enabled GPUs from other GPUs?
What is the primary benefit of using OpenACC directives in GPU programming?
What is the primary benefit of using OpenACC directives in GPU programming?
What is the primary purpose of CUDA in extending C?
What is the primary purpose of CUDA in extending C?
Which key abstractions primarily define the capabilities of a GPU in CUDA?
Which key abstractions primarily define the capabilities of a GPU in CUDA?
When writing programs for optimal performance in CUDA, what is important to understand?
When writing programs for optimal performance in CUDA, what is important to understand?
Where does the host code run in a CUDA program?
Where does the host code run in a CUDA program?
In CUDA, what is the sequence of executions represented by a thread?
In CUDA, what is the sequence of executions represented by a thread?
What allows multiple hosts to share the GPU in the Kepler model?
What allows multiple hosts to share the GPU in the Kepler model?
What is the main purpose of using 'nvprof --metrics gld_efficiency,gst_efficiency' in CUDA?
What is the main purpose of using 'nvprof --metrics gld_efficiency,gst_efficiency' in CUDA?
According to the discussion on CUDA, what is one way to optimize performance on the GPU?
According to the discussion on CUDA, what is one way to optimize performance on the GPU?
What does Amdahl's law help in estimating?
What does Amdahl's law help in estimating?
In Amdahl's law formula (S / P) = 1 / (1 - P) + S, what do 'S' and 'P' represent?
In Amdahl's law formula (S / P) = 1 / (1 - P) + S, what do 'S' and 'P' represent?
What happens if data access conflicts occur in a GPU application, according to the CUDA discussion?
What happens if data access conflicts occur in a GPU application, according to the CUDA discussion?
How can one ensure data parallelism in CUDA, based on the provided content?
How can one ensure data parallelism in CUDA, based on the provided content?
