What are GPUs?
Graphics Processing Unit or GPU is a processing module that works along with a CPU in computing systems. Traditionally, GPUs are used for graphics processing in display devices used for gaming, 3D rendering, etc.
In the year 2009, Andre Ng, who is popular for his work in the deep learning space, with two other authors, published a research paper titled, “Large-scale Deep Unsupervised Learning using Graphics Processors.” From this point, the popularity of using GPUs for deep learning and other machine learning & AI workloads has ever grown.
GPUs can perform parallel processing at a scale much better than traditional CPUs. The GPU parallel processing capabilities are much necessary to bring down the time and computational costs required for developing deep learning and machine learning systems.
E2E GPU Instances
NVIDIA V100 and NVIDIA T4 GPUs power E2E GPU Instances. NVIDIA V100 is known as the most advanced data center GPU ever built. NVIDIA T4 is known for its inferencing capabilities. Both the processors are equipped with NVIDIA Tensor Cores.
Tensor Cores are specialized processing units that can do matrice multiply and accumulate results in a single operation. Matrix operations are the heart of deep learning workloads, and Tensor Cores can provide the much-needed acceleration.
Machine learning is the ability of machines to learn from data and make decisions and again unlearn and relearn through the new data sets available. Machine learning is the building block of many technologies like recommendation systems, predictive analytics, data analytics, voice assistants, real-time fraud analysis, etc.
You can run all the popular machine learning frameworks like TensorFlow, MxNet, PyTorch, Keras, among others, on E2E GPU Instances.
Deep learning is a subset of machine learning — it uses artificial neural networks like convolutional layers, feed-forward, and recurrent neural networks. Typically, deep learning systems work with relatively larger data sets and use matrix operations at the core.
NVIDIA T4 and NVIDIA V100 GPUs are equipped with Tensor Cores — which are specialized processing units in a GPU that can perform matrix multiply and accumulate results in a single operation. This dramatically improves the performance of deep learning training and inference workloads. Click here to know more about how you can leverage Tensor Cores for your deep learning workloads.
Image processing is an area of study where images are processed using deep neural networks to find useful information. Image processing has huge applications in medical imaging where images can be processed by deep neural networks such as convolutional layers to detect disease, find cancer cells, among other applications.
Image processing is also employed in fields like 4D Construction, Autonomous Vehicles, Fashion Assistants, Recreation of Ancient Paintings, etc.
Also known as video content analysis, video analytics is the application of automated video analysis to identify objects, people, and movements. Video analytics is applied in perimeter intrusion detection systems, crowd management, counterflow detection, and suspect detection, among others. With smart cities highly-equipped with CCTV units, automated video analysis is vital to prevent crimes and theft.
Natural Language Processing (NLP)
NLP is the analysis of natural language either in text form or speech. NLP has a wide variety of applications in healthcare, diagnostics, sentimental analysis, analysis of financial markets, intelligent voice assistants, identifying fake news, legal litigations, talent recruitment, etc.
Click here to know more about E2E GPU Cloud powered by NVIDIA T4, NVIDIA A100 and NVIDIA V100.