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.