- Change the random vector in simple_scalar_multiply_kernel.py so that it is of a length of 10,000, and modify the i index in the definition of the kernel so that it can be used over multiple blocks in the form of a grid. See if you can now launch this kernel over 10,000 threads by setting block and grid parameters to something like block=(100,1,1) and grid=(100,1,1).
- In the previous question, we launched a kernel that makes use of 10,000 threads simultaneously; as of 2018, there is no NVIDIA GPU with more than 5,000 cores. Why does this still work and give the expected results?
- The naive parallel prefix algorithm has time complexity O(log n) given that we have n or more processors for a dataset of size n. Suppose that we use a naive parallel prefix algorithm on a GTX 1050 GPU with 640 cores. What does the asymptotic time complexity become in the case that n >>...
Hands-On GPU Programming with Python and CUDA
By :
Hands-On GPU Programming with Python and CUDA
By:
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.
Related Content you might be interested in
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
Other Books You May Enjoy
Customer Reviews