The hardware is comprised of a system which can be a workstation or a server with one or more GPUs.
The system is provisioned with an operating system and an NVIDIA driver that enables the deep learning framework to leverage the GPU functions for accelerated computing
Containers are becoming the choice for development in organizations. NVIDIA provides many frameworks as Docker containers through NGC, which is a Cloud Registry for GPU accelerated software. It hosts over 100 containers for GPU accelerated applications, tools, and frameworks. These containers help with faster and more portable development and deployment of AI applications on GPUs across the Cloud, data center, etc and are optimized for accelerated computing on GPUs. Hence, the stack includes running the NVIDIA Docker Runtime specific for NVIDIA GPUs. The containers include all the required libraries to deliver high performance GPU acceleration during the processing required for training.
The CUDA toolkit is an NVIDIA groundbreaking parallel programming model that performs essential optimizations for deep learning, machine learning, and high performance computing, leveraging NVIDIA GPUs.
CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.
In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance – while the compute intensive portion of the application runs on thousands of GPU cores in parallel.
When using CUDA, developers program in popular languages such as C, C++, Fortran, Python and MATLAB and express parallelism through extensions in the form of a few basic keywords.
Vendor Support
CUDA is developed by NVIDIA and is primarily designed for use with NVIDIA GPUs, while OpenCL is an open standard supported by a range of vendors, including NVIDIA, AMD, and Intel, and can be used with a variety of hardware, including CPUs, GPUs, and etc.
Programming Model
CUDA provides a low-level programming model that requires developers to write code specifically for NVIDIA GPUs, while OpenCL provides a higher-level programming model that can be used with a variety of hardware devices and is more flexible in terms of the programming language used.
Performance
CUDA is generally considered to provide better performance on NVIDIA GPUs, as it is specifically designed for use with these devices, while OpenCL is more flexible but may not provide optimal performance on all hardware platforms.
Ecosystem
CUDA has a large and well-established ecosystem, including a range of libraries and tools, while OpenCL has a smaller ecosystem but is supported by a range of vendors.
The host is the CPU available in the system. The system memory associated with the CPU is called host memory. The GPU is called a device and GPU memory likewise called device memory.
To execute any CUDA program, there are three main steps:
- Copy the input data from host memory to device memory, also known as host-to-device transfer.
- Load the GPU program and execute, caching data on-chip for performance.
- Copy the results from device memory to host memory, also called device-to-host transfer.
Computer graphics
Startups and significant-tech companies use GPUs to create photo-realistic 3D graphics for games, movies, etc. GPUs allow for more detailed and realistic graphics than CPUs are capable of.
Deep learning
Deep learning has been used in various areas, including speech recognition, image recognition, and natural language processing.
Data mining
It is a way of discovering useful information from large amounts of data. GPUs make data mining faster and more efficient.
Scientific computing
It is used in fields like chemistry, biology, and physics. With GPU parallel computing, you can speed up simulations and modelings.
When deciding what platform to use at different stages of the development cycle, it's important to understand how cloud and on-prem approaches support these stages. For initial stages with smaller, less complex data sets, cloud compute instances backed by GPUs are ideal for early prototyping. However, as data sets get larger and deep learning models become more complex, it's better to deploy processing closer to where the data is located, which usually means bringing the training environment on-premises. This can help drive down costs, allow for faster iteration, and provide a fixed cost infrastructure managed by the IT department. It also helps them avoid potential concerns around data privacy.
A hybrid approach combining Cloud-hosted GPU resources for early experimentation and on-premises computing for large-scale deep learning training may be the best approach for most organizations.
https://www.coursera.org/learn/introduction-to-ai-in-the-data-center/
https://developer.nvidia.com/cuda-zone
https://www.turing.com/kb/understanding-nvidia-cuda
https://developer.nvidia.com/blog/cuda-refresher-cuda-programming-model/
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