Now, we will take a look at the CUDA Math API. This is a library that consists of device functions similar to those in the standard C math.h library that can be called from individual threads in a kernel. One difference here is that single and double valued floating-point operations are overloaded, so if we use sin(x) where x is a float, the sin function will yield a 32-bit float as the output, while if x were a 64-bit double, then the output of sin would also be a 64-bit value (Usually, this is the proper name for a 32-bit function, but it has an f at the end, such as sinf). There are also additional instrinsic functions. Intrinsic functions are less accurate but faster math functions that are built into the NVIDIA CUDA hardware; generally, they have similar names to the original function, except that they are preceded with two underscores—therefore,...
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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