Next NVIDIA GTC is coming up on September 19-22. The event is free to attend and fully virtual.
GTC Conference is hosted by NVIDIA and is a global conference where you can explore cutting-edge technology and business trends in AI. Meet the ACM Turing Award winners, Yoshua Bengio, Geoffrey Hinton, and Yann LeCun and many other thought leaders and researchers in AI as they will share their knowledge and provide perspective on how AI will transform various aspects of our life.
Register for this virtual, free conference at https://www.nvidia.com/gtc/?ncid=GTC-NVMPALMER

Get the knowledge to do your best work.
GTC has over 900 sessions to choose from, featuring speakers at renowned companies like Mercedes-Benz, Visa, Google, and others. Learn the latest AI technologies, hear from leading researchers on the impact of their work, and get insight from companies who are evolving their work with accelerated computing. Plus, network with peers to share ideas.
Keynote: March 22 | Conference: March 21 ‑ 24
All talks available on-demand for everyone who registers until March, 24th.

NVIDIA is offering free, virtual faculty development workshops to provide hands-on experience and teaching advice from real educators in academia in the areas of deep learning, accelerated computing, and accelerated data science. Achieving a certificate in these workshops is a major step towards DLI instructor certification should you decide to take the next step after the workshop.
These are exclusive workshops for educators with a valid email address from a college or university in Europe, Middle East or Africa.
Fundamentals of Deep Learning: Thu, January 20, 9:00 a.m. to 5:00 p.m. CET, UTC +1
Fundamentals of Accelerated Computing with CUDA Python: Thu, January 20, 9:00 a.m. to 5:00 p.m. CET, UTC +1
Fundamentals of Accelerated Computing with CUDA C/C++: Thu, January 20, 9:00 a.m. to 5:00 p.m. CET, UTC +1
Fundamentals of Accelerated Data Science: Wed, January 26, 9:00 a.m. to 5:00 p.m. CET, UTC +1
Your faculty development workshop registration is subject to approval and each workshop is limited to specific regions. DLI plans to extend these faculty development workshops to other global regions soon.
REGISTER NOW

If you haven't had a chance to check out all the great Research sessions at GTC, including ‘Illuminating the Dark Spaces of Healthcare with Ambient Intelligence’ with Stanford’s Fei-Fei Li, there’s still time to register until 19 November and you’ll gain exclusive access to all the session recordings until 29 November.

Master Complex Systems Simulations with FGPU2 at NVIDIA GTC
Complex systems exist everywhere, from cellular systems to crowds and road networks.
Understanding and managing these complex systems is fast becoming one of the biggest challenges for research, policy, and industry. Unlocking their secrets will not only embolden our understanding of critical biological processes but also enable us to better grasp man-made systems and predict their behavior in light of certain variables.
Join Paul Richmond, Director of Research Software Engineering, University of Sheffield, at GTC for ‘Simulate the World and Everything in It’ to learn the latest techniques for successfully simulating complex systems. You’ll explore:
How to simulate complex systems at any biological scale
FGPU2 - a new open-source C++ and Python runtime for automatically translating agent-based descriptions of a complex system to the GPU
How to create highly efficient simulations of interacting agents with a variety of interaction patterns using FGPU2
Key techniques, such as runtime compilation, that make this magic happen

Can your AI infrastructure scale to meet every need?
Discover how the German Research Center for AI scaled their innovation to support over 1000 researchers with NVIDIA GPUs.
Watch our on-demand webinar to learn about their approach, their challenges, and the speedups achieved.

Advances in AI technology have led to improved methods of data analysis and complex computations. We can now solve problems that, only a few years ago, were well beyond our reach.
Watch this free webinar, hosted by NVIDIA, to discover how deep learning is transforming the education sector. You will also learn the fundamentals of accelerated data analytics, high-level use cases, and problem-solving methods.
This webinar will cover:
Demystifying AI
Machine Learning
Deep Learning
Key challenges and how to address them
Find out more about the latest technologies in Deep Learning and the tools you need to deliver breakthrough results.

Gene changes that occur in human cells over a person’s lifetime, known as somatic mutations, cause the vast majority of cancers. They can be triggered by environmental or behavioral factors such as exposure to ultraviolet light or radiation, drinking or smoking.
By using NVIDIA GPUs to analyze the signature, or molecular fingerprint, of these mutations, researchers at the Wellcome Sanger Institute can better understand known causes of cancer and investigate why certain cancers are more common in certain areas of the world than others.
Watch this free webinar to learn more about the Wellcome Sanger Institute's work and its hopes for revolutionizing cancer prevention, monitoring, and treatment.

NVIDIA’s GPU Technology Conference (GTC) is the must attend digital event for developers, researchers, engineers, and innovators looking to enhance their skills, exchange ideas, and gain a deeper understanding of how AI will transform their work.
Education, research, government, and non-profit attendees can register for free.

How well can we really understand viruses?
Investigating how viruses function, how they could mutate, and how they’re transmitted is critical for containing and overcoming viral outbreaks.
But to get a thorough understanding of these points, you need to fully sequence a virus's genome.
Oxford Nanopore have developed the world's first and only nanopore DNA sequencing platform which is uniquely scalable from pocket-sized formats through to ultra-high throughput devices.
Their GPU accelerated sequencers have already been used for the rapid sequencing of outbreaks including Ebola and Zika, directly in the field. Now Oxford Nanopore’s technology is being put to use to monitor and study SARS-CoV-2.
Join NVIDIA’s Mike Vella, Senior Solutions Architect Genomics, for a webinar diving deeper into how you can harness GPU-accelerated genomic sequencing technology to transform our ability to handle viral pandemics.

Ready to speed up your genomics research? Looking for a way to bring drugs to market faster?
Tinker-HP is an evolution of the popular Tinker computer software application for molecular dynamics simulation.
It conserves Tinker’s simplicity of use and reference double precision implementation for CPUs and is tailored for multiscale simulations of large complex systems with advanced point dipole polarizable force.
Join this on demand webinar to hear from Professor Jean-Philip Piquemal, Sorbonne University, on how his research team is mobilizing Tinker-HP to model various viral proteins and perform free energy simulation of potential drugs.
Don’t miss your chance to transform your drug discovery research.

Schools, colleges, and universities are undergoing an unprecedented challenge to provide remote teaching and learning campus-wide. While remote learning isn’t a new phenomenon, providing quality service at scale is now a requirement, along with a new set of challenges that span user experience, mobility, effective management of a distributed deployment, and data security.
Solutions that enable remote work, such as NVIDIA virtual GPU (vGPU) and Virtual Compute Server (vComputerServer), enable you to meet these new requirements with cost-effective solutions for existing on-premise infrastructure assets and in the cloud.
By attending this webinar, you'll learn:
How NVIDIA vGPU technology solutions enable remote work and learning.
How vGPU solutions are helping universities.
How to get started with vGPU and vComputeServer to accelerate VDI and computational workloads in your institution.
Join here: https://info.nvidia.com/learn-work-remote-with-nvidia-challenges-in-higher-ed-and-research-reg-page.html

By attending this webinar, you’ll learn:
What deep learning is and how it works
How deep learning can be applied to tasks in the earth and space sciences, including weather prediction, climate modeling, satellite observation, and space exploration
How to apply deep learning to identify tropical storms and solar storms observed by NASA Solar Dynamics Observatory.
How to implement GPU accelerated deep learning in Keras and Pytorch, leveraging NGC containers
SPEAKER
DR. DAVID M. HALL
Sr. Solutions Architect, NVIDIA
David Hall joined NVIDIA in January 2018, after working as an Assistant Professor of Research in Computer Science at CU Boulder. Dr. Hall has technical expertise in theoretical physics, numerical methods, computational fluid dynamics, and artificial intelligence. Dr. Hall spent the previous decade developing non-hydrostatic atmospheric models for high resolution global climate models in HPC environments.

Pour télécharger le document DGX Station Infographic et la documentation DGX Station, veuillez remplir le formulaire suivant.

This webinar discusses how NVIDIA GPUs and CUDA can enable high-fidelity Computational Fluid Dynamics (CFD) for higher education and research.
Flow field computations for transient and turbulent flow problems are highly compute-intensive and time-consuming. Popular existing numerical techniques often compromise on the underlying physics or require a massive amount of computational resources. Hence, fast and accurate CFD simulations on energy- and cost-efficient hardware are greatly appreciated by both academia and industry.
Register now!

Ultrasound is a non-invasive, multi-organ, portable and affordable imaging system. While 2/3 of the world population does not have access to medical imaging, democratization of ultrasound in clinical practice has become a matter of public healthcare. Yet, its potential is currently limited by the difficulty of acquiring quality ultrasound images and the complexity of their interpretation, both requiring strong expertise.
DESKi specializes in building deep learning algorithms for medical image analysis with a focus on automatic cardiac ultrasound analysis. Partnering with Bordeaux’s University Hospital, we aim to spread cardiologist’s expertise to a broader medical community.
In this presentation, DESKi will present its partnership with Bordeaux’s University Hospital and the work on evaluation of the left ventricular ejection fraction using deep learning on a Nvidia DGX station.
After a brief introduction on cardiac echography and ejection fraction, we will cover:
How to leverage the hospital large clinical database
Methodologies to tackle automatic cardiac ultrasound analysis.
Register for this free webinar today!

The GPU hackathon event is organized by ORNL, NVIDIA, OpenACC and labs and universities around the globe. Hackathons are 5-day events designed to help teams of three to six developers accelerate their own codes on GPUs with OpenACC or other tools. Each team is assigned two mentors for the duration of 5 days to help teams port their code to GPUs or optimize its performance. No previous GPU experience required, teams are expected to be fluent with the code they bring to the event and motivated to make progress during these 5 days.
This is a unique opportunity for teams to jumpstart acceleration or optimization of your code on GPUs with OpenACC or any other preferred tools. With the help of two experienced mentors, by the end of the event each team should significantly accelerate their code or know which steps to take to continue work.
The hackathon in Sheffield, UK is scheduled for the week of August 19th with applications open until June 16th.
Please submit your application through the OLCF website: https://www.olcf.ornl.gov/gpu-hackathons/

Whether it is for analyzing data, developing algorithms, or creating models, researchers from around the globe are using MATLAB to accelerate their work. In this webinar, you will discover how MATLAB can support your deep learning research. You’ll also explore how to expedite your research using deep learning and scale your work on GPUs.
By attending this webinar, you'll learn:
How to scale training to multiple GPUs
How to use the NVIDIA GPU Cloud container for MATLAB
How to automate CUDA code generation, optimize performance, and deploy on NVIDIA GPU
Register today to attend this free Webinar!

NVIDIA is working with leading AI institutions to accelerate their research using best in class GPU platforms.
This webinar outlines the recommended infrastructure needed and the programs available for academic institutions to accelerate their research and advance discovery. With special education discounts, DGX systems allow universities to take their research to the next level.
By attending this webinar, you'll learn:
About the variety of different use cases for AI in EMEA
About the NVIDIA Academic Program, available discounts and special offerings to build, research, and educate on machine and deep learning.
How AI can be explored across desk, data center, and cloud.
Join Now!
Available in English, French, German, and Spanish Language.

Date: Wednesday, Jan 30, 2019
Time: 9:00am – 10:00am PT / 6:00pm - 7:00pm CET
Duration: 1 Hour
Data science gives enterprises the power to analyze and optimize business processes, supply chains and digital experiences. But data preparation and machine learning workloads typically take a long time. In cases when time is of the essence, such as predicting demand for perishable goods, being able to speed up data science workflows is a significant advantage.
With RAPIDS, a new set of open-source libraries for GPUs, data scientists can accelerate their python toolchain with minimal code changes and drastically improve productivity, while achieving top-model accuracy.
By attending this webinar, you’ll learn:
- What RAPIDS is and the key benefits for you
- How to get started with RAPIDS to accelerate machine learning workflows
- Why NVIDIA DGX-2, with 512 GB memory and 10,240 Tensor Cores, is the perfect tool to reap the benefits of accelerated data science workloads
Use cases from industries such as Retail, Finance and Research
Register now!

Data science workflow has traditionally been slow and cumbersome when it came to loading, filtering and manipulating data, as well as ML training itself. These processes were constrained to slow, CPU-based compute, and resulted in lengthy cycle times impacting data science productivity. RAPIDS delivers GPU accelerated machine learning and data analytics libraries, deployed on NVIDIA GPU platforms, for maximized data science productivity, performance and insights. Bring the power of GPU acceleration to your research and decrease your time to new discoveries with RAPIDS.
By watching this webinar you will learn:
- How RAPIDS accelerates your Python data science toolchain with minimal code changes and no new tools to learn.
- How RAPIDS can accelerate model training and time to deployment.
The wealth of accelerated apps available to maximize datacenter throughput, utilization, and efficiency.
Watch now!

DNA analysis is transforming the treatment of many diseases such as cancer, Alzheimer’s, and monogenic diseases in infants by customizing patients’ treatment based on their genetic characteristics. Due to the promise of precision medicine which is based on such analysis, the
number of genomes to be sequenced is expected to double every year and newer technologies are needed to accelerate the analysis process to make these analyses the standard of care. In this work, we show how Parabricks’ high performance Next Generation Sequencing (NGS) software, based on NVIDIA Graphics Processing Unit (GPU) accelerated computing, accelerates the process from two days to one hour at one-fourth the cost. Furthermore, we show the impact that this has on leading genomic research centers such as BIOTEC, which plans to analyze thousands of samples every year. By reducing the turnaround time from days to an hour, BIOTEC plans to gain deeper insights into data, which was not previously possible.
Download the free white paper by filling out the following form:

Imagine waiting for your flight at the airport. Suddenly, an important business call with a high profile customer lights up your phone. Tons of background noise clutters up the soundscape around you — background chatter, airplanes taking off, maybe a flight announcement. You have to take the call and you want to sound clear.
We all have been in this awkward, non-ideal situation. It’s just part of modern business.
Background noise is everywhere. And it’s annoying.
Now imagine that when you take the call and speak, the noise magically disappears and all anyone can hear on the other end is your voice. No whisper of noise gets through.
READ MORE

For several decades, using ultrasound imaging in a clinical setting meant choosing some application-specific parameters, acquiring a set of images manually, and analyzing them based on prior anatomical knowledge and imaging experience. Today, the AI revolution in the medical field also encompasses ultrasound, adding great benefit through learning-based analysis, e.g. anatomy detection or classification. But the versatility and the real-time nature of ultrasound allows us to go even further, fundamentally changing how images are acquired. In this webinar, we will discuss novel approaches to image acquisition, processing, and visualization that have the potential to radically change clinical practice and transform the ultrasound probe into an ever-more-indispensable point-of-care tool. After an introduction of NVIDIA's CLARA platform, we will look beyond conventional image processing and show how more original applications of recent GPU-Based technologies like deep learning enable real breakthroughs in both diagnostics and interventions.
Register to attend this free Webinar

Learn how RAPIDS, a new open source project, can speed up your data science workflows by bringing the power of GPU acceleration to your end-to-end ML machine learning (ML) pipeline.
RAPIDS includes a DataFrame manipulation library (cuDF) for data wrangling and feature engineering that’s very similar to Pandas. It also includes an ML library (cuML) that will provide GPU-accelerated versions of the algorithms available in scikit-learn.
By attending this webinar, you'll learn to use GPU-accelerated ML to:
• Use GPU-accelerated ML to Improve clinical care and operational efficiency;
• Speed up drug discovery and advance precision medicine; and
• Derive insight and value from all your data –structured, free text, imaging, and genomics.
REGISTER NOW