CUDA
NVIDIA’s CUDA GPU architecture, which was unveiled on June 23, 2007, is a design for GPUs. “Compute Unified Device Architecture” was the first abbreviation for the term “CUDA.
The performance of computer jobs that benefit from parallel processing is enhanced by CUDA. These workloads are sometimes said to as “embarrassingly parallel” since they naturally lend themselves to being calculated by individual cores, such as real-time 3D picture rendering. Many of these CUDA cores, which might number in the thousands, are built onto a single video card using CUDA GPUs. Software must be created particularly for the architecture using the NVIDIA-provided low-level CUDA libraries and APIs. These libraries’ original programming language is C++, although wrappers are created.
Why do you need CUDA?
By using the capability of GPUs for any element of a calculation that can be parallelized. Since it is compatible with many widely used programming languages like C, C++, or Fortran, CUDA makes it much simpler for developers and software engineers to implement parallel programming than previous GPU programming interfaces like Direct3D and OpenGL, which required advanced skills in graphics programming. The developer can execute compute kernels directly on the GPU’s virtual instruction set and parallel computational components by adding modifications to these languages in the form of a few simple phrases.
OpenCL vs. CUDA
OpenCL, a CUDA rival, was introduced in 2009 in an effort to offer a heterogeneous computing standard that was not restricted to Intel/AMD CPUs with NVIDIA GPUs. Although OpenCL’s generality makes it seem appealing, it hasn’t outperformed CUDA on NVIDIA GPUs. Many deep learning frameworks either don’t support OpenCL or only do so after releasing support for CUDA.
CUDA performance boost
Over time, CUDA has advanced and expanded in breadth, mostly in tandem with the advancement of NVIDIA GPUs. You may get performance advantages over CPUs of up to 50x by using numerous P100 server GPUs. The A100 (also not shown) is another 2x quicker (up to 300x CPUs), and the V100 (not shown in this picture) is an additional 3x faster for specific loads (so up to 150x CPUs). The K80 server GPU generation offers 5x to 12x performance enhancements over CPUs.
Not everyone claims to have had the same speed increases, and model training software for CPUs has improved, for instance utilizing the Intel Math Kernel Library. Additionally, the CPUs themselves have improved, mostly to offer additional cores.
CUDA application domains
As seen pictorially in the graphic above, CUDA and NVIDIA GPUs have been used in numerous applications that need high floating-point processing performance. An even longer list contains:
- Financial computation
- Ocean, weather, and climate modeling
- Data analytics and science
- Artificial intelligence and deep learning
- Defense and espionage
- Production/AEC (Architecture, Engineering, and Construction): CAD and CAE (including computational fluid dynamics, computational structural mechanics, design and visualization, and electronic design automation)
- both entertainment and media (including animation, modeling, and rendering; color correction and grain management; compositing; finishing and effects; editing; encoding and digital distribution; on-air graphics; on-set, review, and stereo tools; and weather graphics)
- health photography
- Oil and gas
- study: supercomputers and higher education (including computational chemistry and biology, numerical analytics, physics, and scientific visualization)
- Security and protection
- Management and tools
CUDA in deep learning
Speedy computation is a significant need for deep learning. For instance, the Google Brain and Google Translate teams used GPUs to execute hundreds of one-week TensorFlow runs to train the models for Google Translate in 2016. They had purchased 2,000 server-grade GPUs from NVIDIA for the project. Those training cycles would have taken far longer to converge without GPUs—months as opposed to a week. TensorFlow translation models were employed in production by Google using the TPU, a new specialized processing processor (tensor processing unit).
Many deep learning frameworks, including Caffe2, Chainer, Databricks, H2O.ai, Keras, MATLAB, MXNet, PyTorch, Theano, and Torch, depend on CUDA in addition to TensorFlow for GPU support. The cuDNN package is typically used for deep neural network calculations. A particular version of cuDNN is used by all deep learning frameworks, and as a result, all of the performance metrics for comparable use cases are nearly identical across all of the frameworks. Performance benefits are seen by all deep learning frameworks that upgrade to the latest version when CUDA and cuDNN get better with each new release. The ability of each framework to scale to many GPUs and numerous nodes is where performance tends to vary.
CUDA Toolkit
The CUDA Toolkit comes with libraries, runtime libraries for your programs, compilers, debugging and optimization tools, and documentation. Deep learning, linear algebra, signal processing, and parallel algorithms are all supported by their constituent parts.
The current generation of NVIDIA GPUs, such as the V100, which may be up to three times faster than the P100 for deep learning training workloads as shown below, and the A100, which can provide an additional two times speedup, are optimal for CUDA libraries’ performance. The simplest approach to utilize GPUs is by using one or more libraries, provided that the necessary algorithms have been included in the relevant libraries.
CUDA deep learning libraries (CUDNN)
There are three main GPU-accelerated libraries in the deep learning space: TensorRT, which is NVIDIA’s high-performance deep learning inference optimizer and runtime; cuDNN, which I previously mentioned as the GPU component for most open-source deep learning frameworks; and DeepStream, a video inference library. TensorRT assists with neural network model optimization, calibration for higher accuracy with reduced precision, and deployment of trained models to hyper-scale data centers, embedded devices, or platforms for automotive products.
