- Suppose you get a job translating some old legacy FORTRAN BLAS code to CUDA. You open a file and see a function called SBLAH, and another called ZBLEH. Can you tell what datatypes these two functions use without looking them up?
- Can you alter the cuBLAS level-2 GEMV example to work by directly copying the matrix A to the GPU, without taking the transpose on the host to set it column-wise?
- Use cuBLAS 32-bit real dot-product (cublasSdot) to implement matrix-vector multiplication using one row-wise matrix and one stride-1 vector.
- Implement matrix-matrix multiplication using cublasSdot.
- Can you implement a method to precisely measure the GEMM operations in the performance measurement example?
- In the example of the 1D FFT, try typecasting x as a complex64 array, and then switching the FFT and inverse FFT plans to be complex64 valued in both directions. Then confirm whether...
Hands-On GPU Programming with Python and CUDA
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Hands-On GPU Programming with Python and CUDA
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Overview of this book
Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory.
As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS.
With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain.
By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.
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Current Title:
Hands-On GPU Programming with Python and CUDA
Table of Contents (15 chapters)
Preface
Free Chapter
Why GPU Programming?
Setting Up Your GPU Programming Environment
Getting Started with PyCUDA
Kernels, Threads, Blocks, and Grids
Streams, Events, Contexts, and Concurrency
Debugging and Profiling Your CUDA Code
Using the CUDA Libraries with Scikit-CUDA
The CUDA Device Function Libraries and Thrust
Implementation of a Deep Neural Network
Working with Compiled GPU Code
Performance Optimization in CUDA
Where to Go from Here
Assessment
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