Introduction to CUDA C Programming
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Questions and Answers

What is the primary purpose of GPU-accelerated libraries?

  • To enable GPU acceleration without in-depth knowledge of GPU programming (correct)
  • To provide low-performance solutions
  • To require extensive code changes for implementation
  • To only support a limited set of applications

    • Which of the following libraries is used for deep learning?

  • cuDNN (correct)
  • cuBLAS
  • nvGRAPH
  • cuFFT

    • What does CUDA C allow programmers to control?

    Parallelism and data movement

    The compiler directives used for C, C++, and FORTRAN are referred to as ________.

    <p>OpenACC</p> Signup and view all the answers

    Using GPU-accelerated libraries requires extensive knowledge of GPU programming.

    <p>False</p> Signup and view all the answers

    What is a key advantage of using compiler directives for acceleration?

    <p>They take care of details of parallelism management and data movement.</p> Signup and view all the answers

    Match the following GPU programming languages with their appropriate applications:

    <p>CUDA C = General-purpose GPU programming MATLAB = Numerical analytics Python = High-level programming with GPU support Fortran = Scientific computing</p> Signup and view all the answers

    Study Notes

    Introduction to CUDA C

    • CUDA C is a programming language for GPU computing

    GPU Computing Venues

    • Libraries are easy to use and provide high-quality acceleration
    • Compiler directives are easy to use and portable, however, performance may vary
    • Programming languages offer the most flexibility and performance, but require more coding effort
    • Popular libraries for GPU accelerated computing include cuDNN, TensorRT, cuBLAS, cuSPARSE, cuSOLVER, cuFFT, NVIDIA NPP, and cuGRAPH

    Thrust

    • Thrust is a C++ template library that simplifies GPU programming
    • Thrust provides functions for common operations like vector addition and matrix multiplication

    Compiler Directives

    • OpenACC is a compiler directive language that allows for easy GPU acceleration
    • OpenACC allows for portable code that can be used with different GPUs and compilers

    GPU Programming Languages

    • Common GPU programming languages include MATLAB, Mathematica, LabVIEW, Python, Fortran, and C++
    • CUDA C is a programming language for GPU computing that provides high-performance and flexibility

    CUDA C Memory Management

    • CUDA C provides functions for allocating memory on the device and transferring data between the host and device
    • cudaMalloc allocates memory on the device
    • cudaMemcpy transfers data between host and device
    • cudaFree frees memory allocated on the device

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    Description

    This quiz covers the fundamentals of CUDA C and GPU computing. Explore the various libraries, compiler directives, and programming languages associated with GPU programming. Test your knowledge on Thrust, OpenACC, and the specific applications of CUDA C.

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