Introduction

With the rapid development of artificial intelligenc, GPUs have become a focal point in the arms race among major companies. Possessing more GPUs translates to greater computing power, enabling faster AI training and inference.

Why are GPUs so crucial in the age of AI ?

GPUs and Parallel Computing

Have you ever wondered why large-scale games run smoothly with realistic graphics, while document processing feels effortless for your computer? It's akin to the difference between one person cooking a meal and a group effort. No matter how fast an individual is, they can't match the efficiency of a team working in unison.The same principle applies to how computers process information. The CPU is like a seasoned chef, adept at handling various complex instructions. However, it has only one "brain," limiting its processing speed. In contrast, the GPU is like a group of strong and energetic workers. While each individual may not be as skilled as the chef, their combined power allows them to handle numerous simple tasks simultaneously.Imagine building a castle with LEGO bricks. The CPU acts as a highly skilled architect, responsible for designing blueprints and planning the steps. The GPU represents a group of children who, upon receiving the blueprints, can work together to assemble different sections concurrently, such as walls, towers, and gates. Finally, the CPU assembles these components, resulting in a complete castle.This exemplifies the essence of GPUs and parallel computing:

• GPU: Originally designed for processing graphics, GPUs have evolved into powerful parallel computing tools.
• Parallel Computing: Similar to a group working simultaneously, GPUs can break down a large task into smaller ones, distributing them among multiple "cores" for processing, thereby significantly enhancing efficiency.

Beyond gaming, GPUs and parallel computing find extensive applications in artificial intelligence, data analysis, scientific computing, and other fields. They serve as computer "accelerators," making our lives more convenient and efficient.

What is CUDA?

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA. It enables software developers and engineers to utilize NVIDIA GPUs for general-purpose computing tasks, extending beyond graphics rendering.At its core, CUDA is a software layer providing direct access to the GPU. This allows developers to harness the GPU's parallel processing capabilities to accelerate computationally intensive applications. CUDA offers a comprehensive API (Application Programming Interface) that supports programming languages like C, C++, and Fortran, empowering developers to write programs that can run on GPUs.Key features of CUDA include:

1. Parallelism: GPUs possess thousands of parallel processing cores capable of handling vast amounts of data simultaneously, making CUDA well-suited for parallel computing tasks.
2. Memory Management: CUDA provides direct access to GPU memory, including global, shared, and constant memory.
3. Threads and Blocks: CUDA programs consist of multiple threads organized into blocks, which are then mapped onto the GPU's multiprocessors.
4. Dynamic Parallelism: CUDA supports the dynamic launch of new kernels on the GPU, allowing for the creation of more parallel tasks at runtime as needed.
5. Compatibility: CUDA is compatible with existing programming models and toolchains, facilitating easy integration into existing applications.
6. Optimization: NVIDIA offers various tools to assist developers in optimizing the performance of their CUDA programs, including performance analyzers and optimizers.

CUDA Programming Model

Before CUDA, GPUs were primarily used for graphics rendering, making it challenging to leverage their power for general-purpose computing. CPUs and GPUs differ significantly in their memory architectures, instruction sets, and other aspects. Additionally, CPU programming models are typically sequential, while GPUs are better suited for parallel processing.CUDA introduced a heterogeneous computing model that allows developers to write code using familiar programming languages (like C++) while harnessing the parallel processing capabilities of GPUs. It abstracts away the underlying hardware details, freeing developers from concerns about the specific GPU architecture and allowing them to focus on the parallel implementation of their algorithms. Crucially, NVIDIA provides a wealth of libraries and tools that simplify GPU programming and performance optimization.The CUDA programming model lowers the barrier to entry for GPU-accelerated general-purpose computing, enabling developers to accelerate various computationally intensive tasks, such as scientific computing, machine learning, and image processing.

How to Deploy CUDA

Here are the detailed steps for installing CUDA on Ubuntu:Check System Compatibility

• Verify if your GPU is an NVIDIA GPU:
• Ensure your Linux kernel version meets CUDA requirements. Refer to the NVIDIA official documentation for compatibility information: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html

lspci | grep -i nvidia

Download the CUDA Toolkit

• Visit the NVIDIA official website's CUDA download page: https://developer.nvidia.com/cuda/cuda-downloads
• Select the download option suitable for your Ubuntu version, system architecture, and CUDA version.
• Download the runfile (local) file type.

Install CUDA

• Open a terminal, navigate to the download directory, and use the chmod +x command to grant executable permissions to the runfile file.
• Run the runfile file and follow the prompts to complete the installation:
• During installation, accept the license agreement and choose to install all components, including drivers, CUDA Toolkit, and cuDNN.
• After installation, add the CUDA path to your environment variables:

echo 'export PATH=/usr/local/cuda-<version>/bin:$PATH'~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-<version>/lib64:$LD_LIBRARY_PATH'~/.bashrc
source ~/.bashrc

sudo ./cuda_<version>_<distro>_<architecture>.run

chmod +x cuda_<version>_<distro>_<architecture>.run

Verify Installation

• Compile and run a CUDA sample program:
• If the installation is successful, you'll see information about your GPU, including the CUDA version.

cd /usr/local/cuda-<version>/samples/1_Utilities/deviceQuery
sudo make
./deviceQuery

Use CUDA on Novita AI

At Novita AI, our GPU Instance product integrates CUDA by default, allowing for one click startup without any additional tedious operations.

Conclusion

With CUDA, developers can fully utilize the immense computing power of NVIDIA GPUs to accelerate various computational tasks.As one of NVIDIA's most important products, CUDA forms the foundation of NVIDIA's software ecosystem. Numerous cutting-edge technologies are built upon CUDA, such as TensorRT, Triton, and Deepstream. These technology solutions, developed on the CUDA platform, demonstrate CUDA's ability to drive software innovation.NVIDIA's hardware boasts exceptional performance. However, unleashing its full potential requires matching software support.CUDA serves as that bridge, providing a robust interface for developers to leverage GPU hardware for high-performance computing acceleration. Like a skilled driver operating a high-performance car, CUDA ensures the hardware's capabilities are fully realized.

Novita AI is the All-in-one cloud platform that empowers your AI ambitions. Integrated APIs, serverless, GPU Instance - the cost-effective tools you need. Eliminate infrastructure, start free, and make your AI vision a reality.Recommended Reading:

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