At GTC, NVIDIA GPU Technology Supercharges the Data Center

by Christina Olmsted
Learn how luminaries in the world of AI are unleashing the power of GPUs to accelerate artificial intelligence and high performance computing workloads.  

Personalizing cancer therapy. Predicting the next big hurricane. Reading your mind with AI algorithms. Incredible advances like these are happening with NVIDIA GPU-accelerated technologies.

You can learn about these and many more topics at the GPU Technology Conference, taking place March 26-29 in San Jose, from companies like Facebook, Amazon and Ebay, which are advancing AI.

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Here’s a list of data center-focused sessions you shouldn’t miss:

Asif Khan, Amazon – Continuous Delivery of AI Applications
Learn how to connect the workflow between the data scientists who typically develop deep learning systems and the devops teams who deploy and operationalize them. Also explore basic continuous integration and delivery concepts and how they can be applied to deep learning models.

Henry Saptura, Ebay – Introducing Krylov: AI Platform That Empowers eBay Data Science and Engineering Teams
Learn about the Krylov Project, the key component in eBay’s AI Platform initiative to provide an easy-to-use, open and fast AI orchestration engine that is deployed as managed services in eBay cloud.

Sarah Bird and Yangqing Jia, Facebook – Research to Production: How Facebook Does AI at Scale
Hear how Facebook leverages open source software to perform iterative AI research, scale it for inference and deploy it across the data center and mobile environments with ONNX. Bird and Jia will walk through several real-world product use cases, such as computer vision and neural machine translation, as well as how they achieve large-scale distributed model training.

Kaz Sato, Google – BigQuery and TensorFlow: Data Warehouse + Machine Learning Enables the “Smart” Query
Learn about BigQuery, Google’s fully managed, petabyte-scale data warehouse. Its user-defined function realizes “smart” queries with the power of machine learning, such as similarity search or recommendation on images or documents with feature vectors and neural network prediction. See how TensorFlow and its GPU-accelerated training environment enables a powerful “data warehouse + machine learning” solution.

Jonathan McKinney, – World’s Fastest Machine Learning With GPUs
Meet H2O4GPU, a fully featured machine learning library that is optimized for GPUs with a robust python API that is a drop-dead replacement for scikit-learn. McKinney will demonstrate benchmarks for the most common algorithms relevant to enterprise AI and showcase performance gains as compared to running on CPUs.

Larry Brown and Khoa Huynh, IBM – Deep-Learning Inferencing on IBM Cloud with NVIDIA TensorRT
With a focus on the deep-learning neural network model deployment and inference on the IBM Cloud, learn how well NVIDIA GPUs perform in this area compared to FPGAs that have been tuned for deep-learning primitives.

Tariq Sharif, Microsoft Azure – GPUs for Every Workload in Microsoft Azure
Learn how you can take advantage of GPUs using CUDA or OpenCL on Azure N-series VMs for scenarios like ray-traced rendering, machine learning and artificial intelligence. Stream or remotely access content and engineering designs, digital media or graphics-rich applications using DirectX or OpenGL along with workstation in the cloud capabilities.

Charlie Boyle, NVIDIA – Breaking the Barriers to AI-Scale in the Enterprise
Find out how the latest advancements in scaling in GPU servers and deep learning software can solve your biggest AI platform challenges, such as fitting IT operational constraints while meeting workload performance needed by data scientists.

CJ Newburn, NVIDIA – HPC in Containers: Why Containers, Why HPC, How and Why NVIDIA
HPC applications are increasingly using containers to ease deployment and simplify use. Learn what containers are, how they are orchestrated to run together in the cloud and how communication among containers works.

Karan Batta, Oracle – Advantages of a Bare-Metal Cloud for CUDA Workloads
Learn how levers like bare-metal servers, a true flat network and high-performance storage can accelerate workloads using NVIDIA GPUs in the cloud. Walk through how easy it is to launch GPU clusters in Oracle Cloud Infrastructure, and learn about new announcements on Oracle Cloud Infrastructure in partnership with NVIDIA.

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In addition to sessions like these, the GTC exhibit hall will be packed with dozens of booths showcasing the latest GPU-based AI and deep learning technologies. AWS, Azure, Facebook, IBM, Cisco, Dell EMC, Google Cloud, Hewlett Packard Enterprise, Inspur, Lenovo and Supermicro’s booths are just a few of the many on the floor that you won’t want to miss.

Register to attend today, see the entire list of Data Center activities at GTC, and follow @NVIDIADC on Twitter to hear the latest in accelerated computing.


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