Graphics processing units (GPUs), originally developed for accelerating graphics processing, can dramatically speed up computational processes for deep learning. They are an essential part of a modern artificial intelligence infrastructure, and new GPUs have been developed and optimized specifically for deep learning Graphics processing units (GPUs) are specialized processing cores that you can use to speed computational processes. These cores were initially designed to process images and visual data. However, GPUs are now being adopted to enhance other computational processes, such as deep learning. This is because GPUs can be effectively used in parallel for massive distributed computational processes.
Biomedical research increasingly depends on high-performance computing for modeling and large-scale simulations of the molecular building blocks for biological functions. Recent advances in imaging technology, like cryo-electronic microscopy, produce images of molecules at unprecedented resolution, but also generate enormous amounts of data that need equally enormous amounts of computing horsepower to analyze. GPUs, commonly known for their use in processing graphics for gaming and video editing, are also widely used for a range of scientific applications. They provide a huge boost in performance by offloading computing-intensive portions of an application to dedicated processors in the GPU while the remainder of the code runs on the central processing unit (CPU).
People are now used to watching media or entertainment with delicate animation or 3D rendering. Some popular software like Autodesk Maya, MAxon Cinema 4D, Adobe After Effects CC, Adobe Photoshop CC, Apple FinalCut, etc., need enough computing power to finish heavy graphic tasks. GPU systems technology solve the heavy computing loads in Media & Entertainment industry. GPU technology is the answer to complicated tasks combining visual power and computing power.
Medical imaging currently plays a crucial role throughout the entire clinical applications from medical scientific research to diagnostics and treatment planning. However, medical imaging procedures are often computationally demanding due to the large three-dimensional (3D) medical datasets to process in practical clinical applications. With the rapidly enhancing performances of graphics processors, improved programming support, and excellent price-to-performance ratio, the graphics processing unit (GPU) has emerged as a competitive parallel computing platform for computationally expensive and demanding tasks in a wide range of medical image applications.
Within this survey, the continuous advancement of GPU computing is reviewed and the existing traditional applications in three areas of medical image processing, namely, segmentation, registration and visualization, are surveyed. The potential advantages and associated challenges of current GPU-based medical imaging are also discussed to inspire future applications in medicine.
As the adoption of multi-megapixel and new 4k cameras increases, it will become necessary to upgrade existing workstations to cope with the demands of decoding these higher resolution video streams. A video management system (VMS) that solely utilizes a workstation’s CPU to decode video will use a significant amount of processing power to display only a few cameras, thereby inhibiting an operator’s ability to view additional cameras or run concurrent operations. GPU decoding makes deploying these next-generation cameras far more practical and affordable. By leveraging the video graphics card to decode video and keeping the CPU available for other processes to run the system, GPU decoding makes it possible to display a greater number of cameras using off-the-shelf graphics hardware, and without having to invest in additional workstations.
Graphics cards have made significant strides in the past several years and are impacting all stages of product development, from design to production.
GPU technologies come equipped with graphics processing power to execute an intensive task. Due to their design and functionality, they are unrivaled at parallel task processing.
High Performance Processing and Analysis of Geospatial Data Using CUDA on GPU is achieved through high-performance processing of massive geospatial data on many-core GPU (Graphic Processing Unit). There are be used CUDA (Compute Unified Device Architecture) programming framework to implement parallel processing of common Geographic Information Systems (GIS) algorithms, such as viewshed analysis and map-matching. Experimental evaluation indicates the improvement in performance with respect to CPU-based solutions and shows feasibility of using GPU and CUDA for parallel implementation of GIS algorithms over large-scale geospatial datasets. Due to the growing popularity of the graphics processing unit (GPU) for general purpose applications, geospatial analysators aim to accelerate geospatial analysis via a GPU based parallel computing approach. The CUDA-based implementations of IDW interpolation and viewshed indicate that the architecture of GPU is suitable for parallelizing the algorithms of geospatial analysis. Experimental results show that the CUDA-based implementations running on GPU can lead to dataset dependent speedups in the range of 13–33-fold for IDW interpolation and 28–925-fold for viewshed analysis. Their computation time can be reduced by an order of magnitude compared to classical sequential versions, without losing the accuracy of interpolation and visibility judgment.
The Graphics Processing Unit (GPU) has emerged as a alternative for high performance computing, enabling impressive speed-ups for a range of cosmological computing applications. The adopters in astronomy are already benefiting in adapting their codes to take advantage of the GPU’s massively parallel processing paradigm. Current methods of calculating cosmological quantities scale as O(number of data^2), which are not feasible for datasets containing billions of points. Fortunately, these calculations are easy to parallelize, as they involve independent calculations of the same quantity. There are used GPUs for these calculations, rather than clusters of CPU.