Connect With the Experts

Join this interactive support session with Omniverse technical product managers, engineers, and solutions architects, where you can get direct help and surface your questions, issues, and ideas specific to your use case.

Do you want to write modern C++ on your GPU? Are you curious about C++ Standard Parallelism?

Come join NVIDIA's C++ library and standards team for a Q&A session on: C++ Standard Parallelism and NVC++, Thrust (CUDA C++'s high-productivity general-purpose library and parallel algorithms implementation), CUB (CUDA C++'s high-performance collective algorithm toolkit), and libcu++ (the CUDA C++ standard library).

Usage questions, feature requests, and bug reports are most welcome!

Developing robotic applications? Bring your questions and challenges to our robotics and simulation experts who will be on hand to discuss leveraging the NVIDIA Isaac™ robotics platform.

Learn about the latest optimizations in NVIDIA's image/signal processing libraries like CV-CUDA, NPP, nvJPEG, and DALI™—a fast, flexible data loading and augmentation library. We'll discuss how to use various data processing solutions spanning low-level image and signal-processing primitives in NPP through a library of easy-to-use building blocks CV-CUDA to define high-level, high-performance data processing workflows in DALI.  

We'll talk about new libraries in our portfolio—CV-CUDA and nvTiff—as well as new functionalities in the existing ones (DALI, NPP, and nvJPEG).

After this short update, we'll have an open forum and Q&A session with the experts.  

Join this session to discuss with experts everything related to inter-GPU communication through NVIDIA NVLink®, Infiniband, or other networks.

This session covers all communication libraries: NCCL, MPI, UCX/UCC and NVSHMEM. It’s also the perfect place to discuss performance benefits of GPU Direct, NVLink, Infiniband, and SHARP to accelerate your DL training workload or HPC application.   
 

Join NVIDIA experts for a question-and-answer session on accelerating generative art with NVIDIA technologies such as Triton Inference Server™, TensorRT™, and Omniverse™. Generative AI is revolutionizing artist workflows and fascinating the artists and non-artists alike. We’ll discuss strategies for implementing and optimizing  generative AI workflows on GPUs in the cloud and on desktops, considering features, performance, and compute cost to enable your solutions to scale and deliver high performance for users. 

Our experts are highly experienced with moving AI Inference models from research to production environments and are happy to share these experiences, tools, and techniques with you, including topics such as:
- Moving from research to production
- Minimizing device memory usage
- Performance Optimization
- Integrating with existing code bases and end-to-end AI pipelines

Technologies include :
- TensorRT
- ONNX and DirectML
- CUDA and cuDNN
- Triton Inference Server
 

Imagine that you’ve trained your model with PyTorch, TensorFlow, or the framework of your choice, are satisfied with its accuracy, and are considering deploying it as a service. There are two important objectives to consider: maximizing model performance and building the infrastructure needed to deploy it as a service. Join us for a discussion about two key NVIDIA products that can address these two objectives: NVIDIA TensorRT, a high-performance deep learning inference, and the Triton Inference Server, an open-source inference-serving software that provides a single standardized inference platform.

If you’re new to either of these SDKs, we highly encourage you to refer to the following resources:

Triton Inference Server: https://www.youtube.com/watch?v=1kOaYiNVgFs

NVIDIA TensorRT: https://developer.nvidia.com/blog/speeding-up-deep-learning-inference-using-tensorrt-updated/

In this session, you'll get a chance to talk to the NVIDIA Developer Tools Team about the latest cutting-edge tools and how they can be used to optimize ray racing applications. We'll show you how NVIDIA Nsight systems can be used to determine if you’re GPU or CPU bound. You'll also learn about how Nsight Graphics can be used to debug and optimize GPU workloads in GPU-bound applications. Finally, you'll see how the Nsight Aftermath SDK and Nsight PerfSDK can be integrated into your application to help debug GPU crashes and analyze performance in real time.

After this session, you should have a better understanding of NVIDIA tools and have your questions answered.     

NVIDIA GPUs accelerate the most important applications in quantum chemistry (such as Gaussian, VASP, Quantum ESPRESSO, GAMESS, NWChem, and CP2K) and molecular dynamics (such as GROMACS, NAMD, LAMMPS, and Amber) that are also very popular in materials science, biophysics, drug discovery, and other domains. We'll answer your questions about how to get the best performance for your specific workload or figure out how you can benefit from accelerated computing.