Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

4.3 (9 reviews total)
By Bhaumik Vaidya
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  1. Introducing CUDA and Getting Started with CUDA

About this book

Computer vision has been revolutionizing a wide range of industries, and OpenCV is the most widely chosen tool for computer vision with its ability to work in multiple programming languages. Nowadays, in computer vision, there is a need to process large images in real time, which is difficult to handle for OpenCV on its own. This is where CUDA comes into the picture, allowing OpenCV to leverage powerful NVDIA GPUs. This book provides a detailed overview of integrating OpenCV with CUDA for practical applications.

To start with, you’ll understand GPU programming with CUDA, an essential aspect for computer vision developers who have never worked with GPUs. You’ll then move on to exploring OpenCV acceleration with GPUs and CUDA by walking through some practical examples.

Once you have got to grips with the core concepts, you’ll familiarize yourself with deploying OpenCV applications on NVIDIA Jetson TX1, which is popular for computer vision and deep learning applications. The last chapters of the book explain PyCUDA, a Python library that leverages the power of CUDA and GPUs for accelerations and can be used by computer vision developers who use OpenCV with Python.

By the end of this book, you’ll have enhanced computer vision applications with the help of this book's hands-on approach.

Publication date:
September 2018


Introducing CUDA and Getting Started with CUDA

This chapter gives you a brief introduction to CUDA architecture and how it has redefined the parallel processing capabilities of GPUs. The application of CUDA architecture in real-life scenarios will be demonstrated. This chapter will serve as a starting guide for software developers who want to accelerate their applications by using general-purpose GPUs and CUDA. The chapter describes development environments used for CUDA application development and how the CUDA toolkit can be installed on all operating systems. It covers how basic code can be developed using CUDA C and executed on Windows and Ubuntu operating systems.

The following topics will be covered in this chapter:

  • Introducing CUDA
  • Applications of CUDA
  • CUDA development environments
  • Installing CUDA toolkit on Windows, Linux, and macOS
  • Developing simple code, using CUDA C

Technical requirements


Introducing CUDA

Compute Unified Device Architecture (CUDA) is a very popular parallel computing platform and programming model developed by NVIDIA. It is only supported on NVIDIA GPUs. OpenCL is used to write parallel code for other types of GPUs such as AMD and Intel, but it is more complex than CUDA. CUDA allows creating massively parallel applications running on graphics processing units (GPUs) with simple programming APIs. Software developers using C and C++ can accelerate their software application and leverage the power of GPUs by using CUDA C or C++. Programs written in CUDA are similar to programs written in simple C or C++ with the addition of keywords needed to exploit parallelism of GPUs. CUDA allows a programmer to specify which part of CUDA code will execute on the CPU and which part will execute on the GPU.

The next section describes the need for parallel computing and how CUDA architecture can leverage the power of the GPU, in detail.

Parallel processing

In recent years, consumers have been demanding more and more functionalities on a single hand held device. So, there is a need for packaging more and more transistors on a small area that can work quickly and consume minimal power. We need a fast processor that can carry out multiple tasks with a high clock speed, a small area, and minimum power consumption. Over many decades, transistor sizing has seen a gradual decrease resulting in the possibility of more and more transistors being packed on a single chip. This has resulted in a constant rise of the clock speed. However, this situation has changed in the last few years with the clock speed being more or less constant. So, what is the reason for this? Have transistors stopped getting smaller? The answer is no. The main reason behind clock speed being constant is high power dissipation with high clock rate. Small transistors packed in a small area and working at high speed will dissipate large power, and hence it is very difficult to keep the processor cool. As clock speed is getting saturated in terms of development, we need a new computing paradigm to increase the performance of the processors. Let's understand this concept by taking a small real-life example.

Suppose you are told to dig a very big hole in a small amount of time. You will have the following three options to complete this work in time:

  • You can dig faster.
  • You can buy a better shovel.
  • You can hire more diggers, who can help you complete the work.

If we can draw a parallel between this example and a computing paradigm, then the first option is similar to having a faster clock. The second option is similar to having more transistors that can do more work per clock cycle. But, as we have discussed in the previous paragraph, power constraints have put limitations on these two steps. The third option is similar to having many smaller and simpler processors that can carry out tasks in parallel. A GPU follows this computing paradigm. Instead of having one big powerful processor that can perform complex tasks, it has many small and simple processors that can get work done in parallel. The details of GPU architecture are explained in the next section.

Introducing GPU architecture and CUDA

GeForce 256 was the first GPU developed by NVIDIA in 1999. Initially, GPUs were only used for rendering high-end graphics on monitors. They were only used for pixel computations. Later on, people realized that if GPUs can do pixel computations, then they would also be able to do other mathematical calculations. Nowadays, GPUs are used in many applications other than rendering graphics. These kinds of GPUs are called General-Purpose GPUs (GPGPUs).

The next question that may have come to your mind is the difference between the hardware architecture of a CPU and a GPU that allows it to carry out parallel computation. A CPU has a complex control hardware and less data computation hardware. Complex control hardware gives a CPU flexibility in performance and a simple programming interface, but it is expensive in terms of power. On the other hand, a GPU has simple control hardware and more hardware for data computation that gives it the ability for parallel computation. This structure makes it more power-efficient. The disadvantage is that it has a more restrictive programming model. In the early days of GPU computing, graphics APIs such as OpenGL and DirectX were the only way to interact with GPUs. This was a complex task for normal programmers, who were not familiar with OpenGL or DirectX. This led to the development of CUDA programming architecture, which provided an easy and efficient way of interacting with the GPUs. More details about CUDA architecture are given in the next section.

Normally, the performance of any hardware architecture is measured in terms of latency and throughput. Latency is the time taken to complete a given task, while throughput is the amount of the task completed in a given time. These are not contradictory concepts. More often than not, improving one improves the other. In a way, most hardware architectures are designed to improve either latency or throughput. For example, suppose you are standing in a queue at the post office. Your goal is to complete your work in a small amount of time, so you want to improve latency, while an employee sitting at a post office window wants to see more and more customers in a day. So, the employee's goal is to increase the throughput. Improving one will lead to an improvement in the other, in this case, but the way both sides look at this improvement is different.

In the same way, normal sequential CPUs are designed to optimize latency, while GPUs are designed to optimize throughput. CPUs are designed to execute all instructions in the minimum time, while GPUs are designed to execute more instructions in a given time. This design concept of GPUs makes them very useful in image processing and computer vision applications, which we are targeting in this book, because we don't mind a delay in the processing of a single pixel. What we want is that more pixels should be processed in a given time, which can be done on a GPU.

So, to summarize, parallel computing is what we need if we want to increase computational performance at the same clock speed and power requirement. GPUs provide this capability by having lots of simple computational units working in parallel. Now, to interact with the GPU and to take advantage of its parallel computing capabilities, we need a simple parallel programming architecture, which is provided by CUDA.

CUDA architecture

This section covers basic hardware modifications done in GPU architecture and the general structure of software programs developed using CUDA. We will not discuss the syntax of the CUDA program just yet, but we will cover the steps to develop the code. The section will also cover some basic terminology that will be followed throughout this book.

CUDA architecture includes several new components specifically designed for general-purpose computations in GPUs, which were not present in earlier architectures. It includes the unified shedder pipeline which allows all arithmetic logical units (ALUs) present on a GPU chip to be marshaled by a single CUDA program. The ALUs are also designed to comply with IEEE floating-point single and double-precision standards so that it can be used in general-purpose applications. The instruction set is also tailored to general purpose computation and not specific to pixel computations. It also allows arbitrary read and write access to memory. These features make CUDA GPU architecture very useful in general purpose applications.

All GPUs have many parallel processing units called cores. On the hardware side, these cores are divided into streaming processors and streaming multiprocessors (SMs). The GPU has a grid of these streaming multiprocessors. On the software side, a CUDA program is executed as a series of multiple threads running in parallel. Each thread is executed on a different core. The GPU can be viewed as a combination of many blocks, and each block can execute many threads. Each block is bound to a different SM on the GPU. How mapping is done between a block and SM is not known to a CUDA programmer, but it is known and done by a scheduler. The threads from same block can communicate with one another. The GPU has a hierarchical memory structure that deals with communication between threads inside one block and multiple blocks. This will be dealt with in detail in the upcoming chapters.

As a programmer, you will be curious to know what will be the programming model in CUDA and how the code will understand whether it should be executed on the CPU or the GPU. For this book, we will assume that we have a computing platform comprising a CPU and a GPU. We will call a CPU and its memory the host and a GPU and its memory a device. A CUDA code contains the code for both the host and the device. The host code is compiled on CPU by a normal C or C++ compiler, and the device code is compiled on the GPU by a GPU compiler. The host code calls the device code by something called a kernel call. It will launch many threads in parallel on a device. The count of how many threads to be launched on a device will be provided by the programmer.

Now, you might ask how this device code is different from a normal C code. The answer is that it is similar to a normal sequential C code. It is just that this code is executed on a greater number of cores in parallel. However, for this code to work, it needs data on the device's memory. So, before launching threads, the host copies data from the host memory to the device memory. The thread works on data from the device's memory and stores the result on the device's memory. Finally, this data is copied back to the host memory for further processing. To summarize, the steps to develop a CUDA C program are as follows:

  1. Allocate memory for data in the host and device memory.
  2. Copy data from the host memory to the device memory.
  3. Launch a kernel by specifying the degree of parallelism.
  4. After all the threads are finished, copy the data back from the device memory to the host memory.
  5. Free up all memory used on the host and the device.

CUDA applications

CUDA has seen an unprecedented growth in the last decade. It is being used in a wide variety of applications in various domains. It has transformed research in multiple fields. In this section, we will look at some of these domains and how CUDA is accelerating growth in each domain:

  • Computer vision applications: Computer vision and image processing algorithms are computationally intensive. With more and more cameras capturing images at high definition, there is a need to process these large images in real time. With the CUDA acceleration of these algorithms, applications such as image segmentation, object detection, and classification can achieve a real-time frame rate performance of more than 30 frames per second. CUDA and the GPU allow the faster training of deep neural networks and other deep-learning algorithms; this has transformed research in computer vision. NVIDIA is developing several hardware platforms such as Jetson TX1, Jetson TX2, and Jetson TK1, which can accelerate computer vision applications. NVIDIA drive platform is also one of the platforms that is made for autonomous drive applications.
  • Medical imaging: The medical imaging field is seeing widespread use of GPUs and CUDA in reconstruction and the processing of MRI images and Computed tomography (CT) images. It has drastically reduced the processing time for these images. Nowadays, there are several devices that are shipped with GPUs, and several libraries are available to process these images with CUDA acceleration.
  • Financial computing: There is a need for better data analytics at a lower cost in all financial firms, and this will help in informed decision-making. It includes complex risk calculation and initial and lifetime margin calculation, which have to be done in real time. GPUs help financial firms to do these kinds of analytics in real time without adding too much overhead cost.
  • Life science, bioinformatics, and computational chemistry: Simulating DNA genes, sequencing, and protein docking are computationally intensive tasks that need high computation resources. GPUs help in this kind of analysis and simulation. GPUs can run common molecular dynamics, quantum chemistry, and protein docking applications more than five times faster than normal CPUs.
  • Weather research and forecasting: Several weather prediction applications, ocean modeling techniques, and tsunami prediction techniques utilize GPU and CUDA for faster computation and simulations, compared to CPUs.
  • Electronics Design Automation (EDA): Due to the increasing complexity in VLSI technology and the semiconductor fabrication process, the performance of EDA tools is lagging behind in this technological progress. It leads to incomplete simulations and missed functional bugs. Therefore, the EDA industry has been seeking faster simulation solutions. GPU and CUDA acceleration are helping this industry to speed up computationally intensive EDA simulations, including functional simulation, placement and routing, Signal integrity and electromagnetics, SPICE circuit simulation, and so on.
  • Government and defense: GPU and CUDA acceleration is also widely used by governments and militaries. Aerospace, defense, and intelligence industries are taking advantage of CUDA acceleration in converting large amounts of data into actionable information.

CUDA development environment

To start developing an application using CUDA, you will need to set up the development environment for it. There are some prerequisites for setting up a development environment for CUDA. These include the following:

  • A CUDA-supported GPU
  • An NVIDIA graphics card driver
  • A standard C compiler
  • A CUDA development kit

How to check for these prerequisites and install them is discussed in the following sub section.

CUDA-supported GPU

As discussed earlier, CUDA architecture is only supported on NVIDIA GPUs. It is not supported on other GPUs such as AMD and Intel. Almost all GPUs developed by NVIDIA in the last decade support CUDA architecture and can be used to develop and execute CUDA applications. A detailed list of CUDA-supported GPUs can be found on the NVIDIA website: https://developer.nvidia.com/cuda-gpus. If you can find your GPU in this list, you will be able to run CUDA applications on your PC.

If you don't know which GPU is on your PC, then you can find it by following these steps:

  • On windows:
    1. In the Start menu, type device manager and press Enter.
    2. In the device manager, expand the display adaptors. There, you will find the name of your NVIDIA GPU.
  • On Linux:
    1. Open Terminal.
    2. Run sudo lshw -C video.

This will list information regarding your graphics card, usually including its make and model.

  • On macOS:
    1. Go to the Apple Menu | About this Mac | More info.
    2. Select Graphics/Displays under Contents list. There, you will find the name of your NVIDIA GPU.

If you have a CUDA-enabled GPU, then you are good to proceed to the next step.

NVIDIA graphics card driver

If you want to communicate with NVIDIA GPU hardware, then you will need a system software for it. NVIDIA provides a device driver to communicate with the GPU hardware. If the NVIDIA graphics card is properly installed, then these drivers are installed automatically with it on your PC. Still, it is good practice to check for driver updates periodically from the NVIDIA website: http://www.nvidia.in/Download/index.aspx?lang=en-in. You can select your graphics card and operating system for driver download from this link.

Standard C compiler

Whenever you are running a CUDA application, it will need two compilers: one for GPU code and one for CPU code. The compiler for the GPU code will come with an installation of CUDA toolkit, which will be discussed in the next section. You also need to install a standard C compiler for executing CPU code. There are different C compilers based on the operating systems:

  • On Windows: For all Microsoft Windows editions, it is recommended to use Microsoft Visual Studio C compiler. It comes with Microsoft Visual Studio and can be downloaded from its official website: https://www.visualstudio.com/downloads/.

The express edition for commercial applications needs to be purchased, but you can use community editions for free in non-commercial applications. For running the CUDA application, install Microsoft Visual Studio with a Microsoft Visual Studio C compiler selected. Different CUDA versions support different Visual Studio editions, so you can refer to the NVIDIA CUDA website for Visual Studio version support.

  • On Linux: Mostly, all Linux distributions come with a standard GNU C Complier (GCC), and hence it can be used to compile CPU code for CUDA applications.
  • On Mac: On the Mac operating system, you can install a GCC compiler by downloading and installing Xcode for macOS. It is freely available and can be downloaded from Apple's website:


CUDA development kit

CUDA needs a GPU compiler for compiling GPU code. This compiler comes with a CUDA development toolkit. If you have an NVIDIA GPU with the latest driver update and have installed a standard C compiler for your operating system, you are good to proceed to the final step of installing the CUDA development toolkit. A step-by-step guide for installing the CUDA toolkit is discussed in the next section.


Installing the CUDA toolkit on all operating systems

This section covers instructions on how to install CUDA on all supported platforms. It also describes steps to verify installation. While installing CUDA, you can choose between a network installer and an offline local installer. A network installer has a lower initial download size, but it needs an internet connection while installing. A local offline installer has a higher initial download size. The steps discussed in this book are for local installation. A CUDA toolkit can be downloaded for Windows, Linux, and macOS for both 32-bit and 64-bit architecture from the following link: https://developer.nvidia.com/cuda-downloads.

After downloading the installer, refer to the following steps for your particular operating system. CUDAx.x is used as notation in the steps, where x.x indicates the version of CUDA that you have downloaded.


This section covers the steps to install CUDA on Windows, which are as follows:

  1. Double-click on the installer. It will ask you to select the folder where temporary installation files will be extracted. Select the folder of your choice. It is recommended to keep this as the default.
  2. Then, the installer will check for system compatibility. If your system is compatible, you can follow the on screen prompt to install CUDA. You can choose between an express installation (default) and a custom installation. A custom installation allows you to choose which features of CUDA to install. It is recommended to select the express default installation.
  3. The installer will also install CUDA sample programs and the CUDA Visual Studio integration.
Please make sure you have Visual Studio installed before running this installer.

To confirm that the installation is successful, the following aspects should be ensured:

  1. All the CUDA samples will be located at C:\ProgramData\NVIDIA Corporation\CUDA Samples\vx.x if you have chosen the default path for installation.
  2. To check installation, you can run any project.
  3. We are using the device query project located at C:\ProgramData\NVIDIA Corporation\CUDA Samples\vx.x\1_Utilities\deviceQuery.
  4. Double-click on the *.sln file of your Visual Studio edition. It will open this project in Visual Studio.
  5. Then you can click on the local Windows debugger in Visual Studio. If the build is successful and the following output is displayed, then the installation is complete:


This section covers the steps to install CUDA on Linux distributions. In this section, the installation of CUDA in Ubuntu, which is a popular Linux distribution, is discussed using distribution-specific packages or using the apt-get command (which is specific to Ubuntu).

The steps to install CUDA using the *.deb installer downloaded from the CUDA website are as follows:

  1. Open Terminal and run the dpkg command, which is used to install packages in Debian-based systems:
sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb
  1. Install the CUDA public GPG key using the following command:
sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub
  1. Then, update the apt repository cache using the following command:
sudo apt-get update
  1. Then you can install CUDA using the following command:
sudo apt-get install cuda
  1. Include the CUDA installation path in the PATH environment variable using the following command:
If you have not installed CUDA at default locations, you need to change the path to point at your installation location.
  export PATH=/usr/local/cuda-x.x/bin${PATH:+:${PATH}}
  1. Set the LD_LIBRARY_PATH environment variable:
export LD_LIBRARY_PATH=/usr/local/cuda-x.x/lib64\

You can also install the CUDA toolkit by using the apt-get package manager, available with Ubuntu OS. You can run the following command in Terminal:

sudo apt-get install nvidia-cuda-toolkit

To check whether the CUDA GPU compiler has been installed, you can run the nvcc -V command from Terminal. It calls the GCC compiler for C code and the NVIDIA PTX compiler for the CUDA code.

You can install the NVIDIA Nsight Eclipse plugin, which will give the GUI Integrated Development Environment for executing CUDA programs, using the following command:

sudo apt install nvidia-nsight

After installation, you can run the deviceQuery project located at ~/NVIDIA_CUDA-x.x_Samples. If the CUDA toolkit is installed and configured correctly, the output for deviceQuery should look similar to the following:


This section covers steps to install CUDA on macOS. It needs the *.dmg installer downloaded from the CUDA website. The steps to install after downloading the installer are as follows:

  1. Launch the installer and follow the onscreen prompt to complete the installation. It will install all prerequisites, CUDA, toolkit, and CUDA samples.
  1. Then, you need to set environment variables to point at CUDA installation using the following commands:
If you have not installed CUDA at the default locations, you need to change the path to point at your installation location.
  export PATH=/Developer/NVIDIA/CUDA-x.x/bin${PATH:+:${PATH}}
export DYLD_LIBRARY_PATH=/Developer/NVIDIA/CUDA-x.x/lib\
  1. Run the script: cuda-install-samples-x.x.sh. It will install CUDA samples with write permissions.
  2. After it has completed, you can go to bin/x86_64/darwin/release and run the deviceQuery project. If the CUDA toolkit is installed and configured correctly, it will display your GPU's device properties.

A basic program in CUDA C

In this section, we will start learning CUDA programming by writing a very basic program using CUDA C. We will start by writing a Hello, CUDA! program in CUDA C and execute it. Before going into the details of code, one thing that you should recall is that host code is compiled by the standard C compiler and that the device code is executed by an NVIDIA GPU compiler. A NVIDIA tool feeds the host code to a standard C compiler such as Visual Studio for Windows and a GCC compiler for Ubuntu, and it uses macOS for execution. It is also important to note that the GPU compiler can run CUDA code without any device code. All CUDA code must be saved with a *.cu extension.

The following is the code for Hello, CUDA!:

#include <iostream>
__global__ void myfirstkernel(void) {
int main(void) {
myfirstkernel << <1, 1 >> >();
printf("Hello, CUDA!\n");
return 0;

If you look closely at the code, it will look very similar to that of the simple Hello, CUDA! program written in C for the CPU execution. The function of this code is also similar. It just prints Hello, CUDA! on Terminal or the command line. So, two questions that should come to your mind is: how is this code different, and where is the role of CUDA C in this code? The answer to these questions can be given by closely looking at the code. It has two main differences, compared to code written in simple C:

  • An empty function called myfirstkernel with __global__ prefix
  • Call the myfirstkernel function with << <1,1> >>

__global__ is a qualifier added by CUDA C to standard C. It tells the compiler that the function definition that follows this qualifier should be complied to run on a device, rather than a host. So, in the previous code, myfirstkernel will run on a device instead of a host, though, in this code, it is empty.

Now where will the main function run? The NVCC compiler will feed this function to host the C compiler, as it is not decorated by the global keyword, and hence the main function will run on the host.

The second difference in the code is the call to the empty myfirstkernel function with some angular brackets and numeric values. This is a CUDA C trick to call device code from host code. It is called a kernel call. The details of a kernel call will be explained in later chapters. The values inside the angular brackets indicate arguments we want to pass from the host to the device at runtime. Basically, it indicates the number of blocks and the number of threads that will run in parallel on the device. So, in this code, << <1,1> >> indicates that myfirstkernel will run on one block and one thread or block on the device. Though this is not an optimal use of device resources, it is a good starting point to understand the difference between code executed on the host and code executed on a device.

Again, to revisit and revise the Hello, CUDA! code, the myfirstkernel function will run on a device with one block and one thread or block. It will be launched from the host code inside the main function by a method called kernel launch.

After writing code, how will you execute this code and see the output? The next section describes the steps to write and execute the Hello, CUDA! code on Windows and Ubuntu.

Steps for creating a CUDA C program on Windows

This section describes the steps to create and execute a basic CUDA C program on Windows using Visual Studio. The steps are as follows:

  1. Open Microsoft Visual Studio.
  2. Go to File | New | Project.
  3. Select NVIDIA | CUDA 9.0 | CUDA 9.0 Runtime.
  4. Give your desired name to the project and click on OK.
  5. It will create a project with a sample kernel.cu file. Now open this file by double-clicking on it.
  6. Delete existing code from the file and write the given code earlier.
  7. Build the project from the Build tab and press Ctrl + F5 to debug the code. If everything works correctly, you will see Hello, CUDA! displayed on the command line, as shown here:

Steps for creating a CUDA C program on Ubuntu

This section describes the steps to create and execute a basic CUDA C program on Ubuntu using the Nsight Eclipse plugin. The steps are as follows:

  1. Open Nsight by opening Terminal and typing nsight into it.
  2. Go to File | New |CUDA C/C++ Projects.
  3. Give your desired name to the project and click on OK.
  4. It will create a project with a sample file. Now open this file by double-clicking on it.
  1. Delete the existing code from the file and write the given code earlier.
  2. Run the code by pressing the play button. If everything works correctly, you will see Hello, CUDA! displayed on Terminal as shown here:


To summarize, in this chapter, you were introduced to CUDA and briefed upon the importance of parallel computing. Applications of CUDA and GPUs in various domains were discussed at length. The chapter described the hardware and software setup required to execute CUDA applications on your PCs. It gave a step-by-step procedure to install CUDA on local PCs.

The last section gave a starting guide for application development in CUDA C by developing a simple program and executing it on Windows and Ubuntu.

In the next chapter, we will build on this knowledge of programming in CUDA C. You will be introduced to parallel computing using CUDA C by way of several practical examples to show how it is faster compared to normal programming. You will also be introduced to the concepts of threads and blocks and how synchronization is performed between multiple threads and blocks.



  1. Explain three methods to increase the performance of your computing hardware. Which method is used to develop GPUs?
  2. True or false: Improving latency will improve throughput.
  3. Fill in the blanks: CPUs are designed to improve ___ and GPUs are designed to improve __ .
  4. Take an example of traveling from one place to another that is 240 km away. You can take a car that can accommodate five people, with a speed of 60 kmph or a bus that can accommodate 40 people, with a speed of 40 kmph. Which option will provide better latency, and which option will provide better throughput?
  5. Explain the reasons that make GPU and CUDA particularly useful in computer vision applications.
  6. True or False: A CUDA compiler cannot compile code with no device code.
  7. In the Hello, CUDA! example discussed in this chapter, will the printf statement be executed by the host or the device?

About the Author

  • Bhaumik Vaidya

    Bhaumik Vaidya is an experienced computer vision engineer and mentor. He has worked extensively on OpenCV Library in solving computer vision problems. He is a University gold medalist in masters and is now doing a PhD in the acceleration of computer vision algorithms built using OpenCV and deep learning libraries on GPUs. He has a background in teaching and has guided many projects in computer vision and VLSI(Very-large-scale integration). He has worked in the VLSI domain previously as an ASIC verification engineer, so he has very good knowledge of hardware architectures also. He has published many research papers in reputable journals to his credit. He, along with his PhD mentor, has also received an NVIDIA Jetson TX1 embedded development platform as a research grant from NVIDIA.

    Browse publications by this author

Latest Reviews

(9 reviews total)
Mixes apples and bananas, jumps to Python from nowhere.
Good, thanks , nice book.
Ebooks seemed to be available, but after accidentally opening the PDF (which is horribly formatted and almost unreadable in its provided form) there were no ebooks any longer, only an online version, which is not comfortable to read. The book itself is OK, but less than expected: While it starts at almost zero, its didactics are only about "copy and paste this code, done". There isn't any in-depth explanation, no "learning effect", its title "hands-on" really means "I give you code that works, the rest is up to you". The openCV part is simply straight-forward from the web. I was hoping for some in-depth "how would I convert a typical computer vision problem into parallel processing, what would be the best approach to gain the best performance" - nothing of that kind. Anything you find in this book you can easily find in the interwebs. I still rate it 3 stars, because, the book is concise, the small amount of "point" it covers it does bring across and for what it speaks about it's quite understandable (although the "style" of English is slightly "quirky" - I assume that an editor would have helped a lot with making it a bit more "reader-friendly").

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