Nvidia created CUDA, a computing framework and programming methodology for generic computing on its own GPUs. By utilizing the capability of GPUs for the component of the calculation, CUDA allows developers to speed up compute-intensive applications.
While competing for GPU APIs, like OpenCL, have been suggested, and comparable GPUs from rival companies (AMD) exist, the pairing of CUDA and Nvidia GPUs is the most powerful in various application areas, such as deep learning, and provides the basis for some of the world’s fastest computers.
Apple released OpenCL, a CUDA rival, in an effort to establish a heterogeneous computing standard that was not confined to Intel or AMD CPUs in combination with Nvidia GPUs. Even though the universality of OpenCL sounds appealing, it hasn’t outperformed CUDA, and plenty of machine learning algorithms don’t support it or support it as a second option behind CUDA.
GPU speed boosts have arrived just in time for computing. As chipmakers ran into physical limits, such as chip yield during the manufacturing process and size limits on-chip mask resolution, and heat limits at runtime, the single-threaded performance increase of CPUs, which Moore’s Law predicted would double every 18 months, has slowed to 10% per year.
Implementation of CUDA
CUDA has been embraced in many industries that require high processing capability. The following is a more thorough list:
- Safety and security
- Analytics and data science
- Machine learning and deep learning
- Intelligence and defense
- Manufacturing and AEC (design and visualization, computational mechanics, electronic design automation, and computational fluid dynamics)
- Entertainment (modeling, rendering, and animation; grain management; effects, compositing, and editing; digital distribution, encoding; review)
- Natural gas and oil
- Supercomputing research (analytics, scientific visualization, physics, computational chemistry, and biology)
- Imaging in medicine
CUDA and Deep Learning
Deep learning has a disproportionately high demand for computing power. If not for GPUs, such training runs would have taken a long time to merge instead of a week. Google employed a new bespoke processing device called the tensor processing unit to deploy those TensorFlow translation models in production.
Many additional deep learning frameworks, including CNTK, Caffe2, Databricks, Keras, H2O.ai, Theano, MXNet, PyTorch, and Torch, rely on CUDA for GPU support. For deep neural network calculations, they almost always employ the cuDNN package. Because libraries are so critical to deep learning framework training, all frameworks that employ a particular version of cuDNN have almost identical performance statistics for similar use cases.
Python, Fortran, C, C++, and MATLAB are just a few of the major languages supported by CUDA. The interface is written in C/C++, and the compiler uses abstractions to make parallelism and programming easier.
You may access three primary language extensions using the CUDA programming model:
- A group or collection of threads is referred to as a CUDA block.
- A block of shared memory that is spread across threads is known as shared memory.
- Multiple threads can sync at a specified completion point thanks to synchronization barriers.
You may scale your programs transparently using the CUDA approach. CUDA blocks may be used to break down applications and calculations into individual functions or issues. Each block is allocated to a sub-problem or function, and the tasks are further broken down to meet the threads available. The CUDA runtime schedules block your GPU multiprocessors automatically.