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GTC 諮詢專家


在 NVIDIA GTC 諮詢專家,提供您與 NVIDIA 產品和研究專家們小組或一對一面談的獨家機會。

安排 60 分鐘的現場問答環節,解答有關「如何完成」和「最佳應用案例」等技術方面的疑問。




  • CWES1112
    Winning Kaggle Competitions with GPUs: Reflections from Kaggle Grandmasters
    Jean-Francois Puget, Senior Deep Learning Data Scientist
    Jiwei Liu, Senior Data Scientist
    Bo Liu, Senior Data Scientist
    Bojan Tunguz, Senior Data Scientist
    Gilberto Titericz Junior, Senior Data Scientist
    Kazuki Onodera, Senior Data Scientist
    Christof Henkel, Senior Deep Learning Data Scientist
    Ahmet Erdem, Senior Data Scientist
    Chris Deotte, Senior Data Scientist


      Meet Kaggle grandmasters and learn how to approach and succeed in different types of Kaggle competitions including tabular, image, natural language processing, and physics. Explore solutions and see how NVIDIA GPUs create top-performing models. Also learn how NVIDIA RAPIDS is allowing more possibilities with GPUs. Kaggle is an online platform that challenges participants to build models from real-world data to solve real-world problems while competing for highest model accuracy. NVIDIA RAPIDS is an open-source library that allows data scientists to build entire pipelines on GPU. RAPIDS accelerates feature search and engineering, as well as model training, validation, and inference.


演算法 / 數值技術

自主機器 / 機器人

  • CWES1134
    All Things Jetson
    Suhas Hariharapura Sheshadri, Product Manager - Jetson software
    Winnie Hsu, Senior Engineering Manager
    Amit Goel, Director of Product Management
    Phil Lawrence, Program Manager NVIDIA Jetson platform
    Stuart Yates, Director of Mobile FAE

      NVIDIA Jetson is the world's leading computing platform for AI at the edge. NVIDIA Jetson is powering the edge AI revolution and deployed in use cases including autonomous mobile robots, smart cities, agriculture, logistics, and more. Connect with Jetson Experts, who are available to discuss the platform capabilities, software, and ecosystem and answer any question you may have on Jetson. We'll have Jetson experts joining from various teams including product, system software, hardware, and AI/deep learning for an engaging discussion with you.


  • CWES1177
    GPU-Accelerated Quantum Chemistry and Molecular Dynamics
    Stefan Maintz, Development Technology Manager
    Evan Weinberg, Senior Compute Developer Technology Engineer
    Filippo Spiga, Developer Relations Manager
    Louis Stuber, Compute Developer Technology Engineer
    Kristopher Keipert, Solutions Architect
    Gaurav Garg , Senior Development Technology Engineer
    Abraham Stern , Product Architect - Clara Discovery
    Alan Gray , Principal Developer Technology Engineer
    Kyle Jacobs , Development Technology Engineer


      NVIDIA GPUs accelerate the most important applications in quantum chemistry (like Gaussian, VASP, Quantum ESPRESSO, GAMESS, NWChem, and CP2K) and molecular dynamics (like 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.


電腦視覺 / 影像處理


  • CWES1199
    Training and Deploying Conversational AI Applications
    Arun Venkatesan, Product Manager
    Sirisha Rella, Technical Product Marketing Manager
    Davide Onofrio, Technical Marketing Engineer Lead
    Poonam Chitale, Senior Product Manager
    Ryan Leary, Senior Applied Research Scientist
    Alex Qi, Product Manager - AI Software
    Oleksii Kuchaiev, Senior Applied Scientist
    Hugo Braun, AI Developer Technology Engine


      Conversational AI is the application of machine learning to develop language-based apps that allow humans to interact naturally with machines. In the past few years, deep learning has improved the state of the art in conversational AI and offered superhuman accuracy on certain tasks. Deep learning has also reduced the need for deep knowledge of linguistics and rule-based techniques for building language services, which has led to widespread adoption across industries. Learn how to use NVIDIA Conv AI frameworks and toolkits — Jarvis, Transfer Learning Toolkit (TLT), Nemo, and GPU-accelerated Kaldi — to train and deploy conversational AI applications.



  • CWES1132
    NVIDIA NGC for Deep Learning, Machine Learning, and HPC
    Chintan Patel, Senior Manager, Product Marketing, NGC
    Scott McMillan, Solutions Architect
    Adel El Hallak, Director of Product Management for NGC
    Monika Katariya, Product Manager, NGC
    Chris Parsons, Product Manager, NGC
    Akhil Docca, Sr. Manager, Product Marketing, NGC
    Ryan McCormick, Solution Engineer
    Abhilash Somasamudramath,Product Manager, NGC
    Chintan Shah, Senior Product Manager
    Adam Simpson, Software Engineer

      Meet one-on-one with NVIDIA engineers and researchers to get your questions answered. We'll focus on using GPU-accelerated software from the NGC catalog for deep learning, machine learning, and HPC. Ask us about such topics as strategies for using NGC in your workflows; running NGC containers on different platforms; and using pre-trained models and industry SDKs with Docker, Singularity, and Kubernetes. Data scientists, developers, devops, and system admins supporting DL, ML, data analytics, and HPC workloads will benefit the most.



開發人員工具 – 函式庫 / 執行階段

開發人員工具 – 效能最佳化與分析

  • CWES1526
    Getting Started with Ray Tracing and NVIDIA's Ray Tracing Developer Tools
    Aurelio Reis, Director, Graphics Developer Tools
    Jeffrey Kiel, Senior Engineering Manager, Graphics Developer Tools
    Daniel Price, Engineering Manager, Graphics Developer Tools
    Dana Elifaz, Engineering Manager, Graphics Developer Tools
    JC Liang, Engineering Manager, Graphics Developer Tools

      Ray tracing is the holy grail of 3D graphics and many developers are racing to update their applications to support groundbreaking technologies like DirectX Raytracing (DXR) and Vulkan Ray Tracing. We'll go over the basics of ray tracing using these modern APIs and explain how this works under the hood. We'll be there to answer your questions on how the tools have been specifically tailored to provide optimum visibility into what developers can do to achieve peak performance and revolutionary graphics. We recommend that attendees have an intermediate-level understanding of modern 3D graphics APIs and have previously used graphics tools like NVIDIA Nsight Graphics, Microsoft PIX, or RenderDoc.
  • CWES1572
    Streamline Your Development Workflow with CUDA Profiling, Optimization, and Debugging Tools
    Jackson Marusarz, Director, Technical Product Manager
    Rafael Campana, Director of Engineering, Developer Tools
    Steve Ulrich, Software Engineering Manager
    Magnus Strengert, Senior Software Engineer- Developer Tools
    Aurelien Chartier, Senior Software Engineer

      We'll provide a brief overview of the NVIDIA Nsight family of tools and debuggers. We'll introduce the latest features for debugging, profiling, and optimizing CUDA compute applications. Several experts from the tools development and management teams will be available to talk about getting started steps, best practices, and advanced techniques for application debugging and optimization with NVIDIA developer tools. You can ask questions about anything from high-level concepts to specific tools details, and also discuss plans and suggestions for improving the tools in the future.

開發人員工具 - 程式設計語言 / 編譯器 / 偵錯工具 / 程式碼分析

  • CWES1738
    Directive-Based GPU Programming with OpenACC
    Jeff Larkin, Senior Developer Technologies Software Engineer & OpenACC Technical Committee Chair
    Julia Levites, Senior Product Manager
    Alexey Romanenko, Senior Developer Technology Engineer
    Stefan Maintz, Development Technology Manager
    Markus Wetzstein, HPC Development Technology Engineer
    Louis Stuber, Compute Developer Technology Engineer
    Andreas Hehn, Development Technology Engineer
    Dmitry Alexeev, Developer Technology Engineer

      OpenACC is a programming model designed to help scientists and developers to start with GPUs faster and be more efficient by maintaining a single code source for multiple platforms. Ask our OpenACC experts how to start accelerating your code on GPUs, continue optimizing your GPU code, start teaching OpenACC, host or participate in a hackathon, and more!
  • CWES1801
    Thrust, CUB, and Libcu++ User's Forum
    Bryce Adelstein Lelbach, HPC Programming Models Architect
    Olivier Giroux, Distinguished Engineer
    Michal Dominiak, libcu++ Engineer
    Allison Vacanti, Thrust and CUB Engineer
    Conor Hoekstra, RAPIDS Engineer
    Wesley Maxey, libcu++ Engineer

      Come join NVIDIA’s CUDA C++ Core Libraries team for a Q&A session on:
      • Thrust— The C++ parallel algorithms library. https://github.com/NVIDIA/thrust
      • CUB — Cooperative primitives for CUDA C++ kernel authors. https://github.com/NVIDIA/cub
      • libcu++ — The C++ Standard Library for your entire system. https://github.com/NVIDIA/libcudacxx
      We welcome your usage questions, feature requests, and bug reports!
  • CWES1802
    Future of Standard and CUDA C++
    Bryce Adelstein Lelbach, HPC Programming Models Architect
    Olivier Giroux, Distinguished Engineer
    Michal Dominiak, libcu++ Engineer
    David Olsen, NVC++ Engineer
    Michael Garland, Research
    Graham Lopez, HPC Product Manager

      Curious about the future of C++? Interested in learning about the C++ committee's roadmap for safety-critical, concurrent, parallel, and heterogeneous programming? Come join Bryce Adelstein Lelbach (chair of the C++ Library Design subcommittee), Olivier Giroux (chair of the C++ Concurrency and Parallelism subcommittee), and the rest NVIDIA's Standard C++ committee delegation for a Q&A session about the future of the C++ programming language.


  • CWES1087
    Performance Analysis and Optimization
    Peng Wang, Principal HPC Developer Technology Engineer
    Evan Weinber, Senior Compute Developer Technology Engineer
    Akshay Subramaniam, AI Developer Technology Engineer
    Ramin Mafi, Senior Compute Developer Technology Engineer
    Kamesh Arumugam, Senior Compute Developer Technology Engineer
    Sebastian Jodlowski, Senior System Software Engineer
    Lars Nyland, GPU Computing Architect

      Come talk to experts in GPU programming and code optimization, share your experience with them, and get guidance on how to achieve maximum performance on NVIDIA's platform.
  • CWES1175
    CUDA Memory Management
    Nikolay Sakharnykh, Senior Manager, Developer Technology
    Chirayu Garg, Senior AI Developer Technology Engineer
    Mark Hairgrove, CUDA SW Engineer
    Cory Perry, Senior CUDA Software Engineer
    Lars Nyland, GPU Computing Architect
    Michael Delorme, Engineering Manager, GPU Compute System Software (CUDA)
    Vishnuswaroop Ramesh, CUDA SW Engineer

      Come to discuss any topics about memory management on GPU systems, such as new CUDA APIs, profiling and optimizations for GPU memory subsystem, and tips and tricks for managing data across GPUs and CPUs. Whether you're interested in the low-level details of the GPU architecture, or software heuristics in the driver, or guidelines and best practices for applications — we have the right experts from multiple NVIDIA teams you can connect with and chat about your topic.
  • CWES1142
    The Convergence of HPC and AI
    Kamesh Arumugam, Senior Compute Developer Technology Engineer
    Akshay Subramaniam, AI Developer Technology Engineer
    Sanjay Choudhry, Senior Director, Compute Software
    Tom Gibbs, Manager, Developer Relations
    Xiang Gao, Deep Learning Framework Engineer
    David Clark, Developer Technology Engineer

      We'll discuss ways to combine deep learning and artificial intelligence with traditional HPC to accelerate the pace of scientific discovery, from high-energy physics to life sciences and health care. The traditional paradigm uses large-scale simulation at the core, where data analytics is used for pre- and post-processing of the data. More recently, AI and large-scale simulation are applied on a more cooperative basis where the strengths of each converge to form a powerful new tool for science. We can discuss both paradigms in this session.

個人化 / 推薦

  • CWES1184
    Training and Deploying Recommender Systems on the GPU: Merlin, HugeCTR, NVTabular, and Triton
    Even Oldridge, Senior Manager, Recommender Systems Framework Team
    Sirisha Rella, Senior AI Developer Technology Engineer
    Xuan Vinh Nguyen, Deep Learning Solutions Architect
    Joey Conway, Group Product Manager of AI
    Zehuan Wang, APAC Devtech Solution Team Manager
    Minseok Lee, AI Developer Technology Engineer


      We'll deep dive into how to optimally prepare, train, and deploy recommender systems on the GPU. Experts will be available to speak with you one-on-one in breakout rooms about your specific questions and problems related to recommender system ETL, training, and inference. We'll go over some of the tools and technologies that NVIDIA has been building in the recommender system space and will also touch on techniques for optimizing commonly used frameworks for recommender workflows. If you're interested in speeding up your recommender system, gaining a feature engineering advantage like the one we used to win the 2020 RecSys Challenge, or overcoming the challenges of taking trained models and deploying them into production, this session is for you.



  • CWES1082
    NVIDIA Maxine: An Accelerated Platform SDK for Developers of Video Conferencing Services
    Davide Onofrio, Technical Marketing Engineer Lead
    Abhijit Patait, Director, System Software
    Abhishek Sawarkar, Deep Learning Software Technical Marketing Engineer
    Tanay Varshney, Technical Marketing Engineer - Deep Learning
    Alex Qi, Product Manager - AI Software


      We'll answer questions on NVIDIA Maxine SDK for video conferencing services. Applications based on Maxine can reduce video bandwidth usage down to one-tenth of H.264 using AI video compression. Maxine includes APIs for the latest innovations from NVIDIA research, such as face alignment, gaze correction, face re-lighting, and real time translation, in addition to capabilities such as super-resolution, noise removal, closed captioning, and virtual assistants. These capabilities are fully accelerated on NVIDIA GPUs to run in real-time video streaming applications in the cloud. Applications built with Maxine can easily be deployed as micro-services that scale to hundreds of thousands of streams in a Kubernetes environment.


虛擬實境 / 擴增實境

  • CWES1113
    CloudXR: AR and VR over Wireless Networks
    Tom Kaye, Senior Solutions Architect
    Mitch VanDyken, Visualization Solutions Architect
    Rick Grandy, Principal Solutions Architect
    Jimmy Rotella, Senior Solutions Architect
    Randall Siggers, Senior Solutions Architect
    Doug Traill, Solutions Architect


      Learn more about NVIDIA's CloudXR platform. Pro Virtualization experts will take you on a deep dive of NVIDIA streaming XR technology. We'll cover the fundamentals of streaming, wireless 5G networks, and extended reality. With recent developments in the cloud service provider space, the CloudXR ecosystem is expanding to be a part of many platforms, including Amazon Web Services. This Connect with Experts session will walk attendees through the process of using the CloudXR SDK, as well as running CloudXR on Amazon Web Services.

  • 歐洲時區

自主機器 / 邊緣 / 視覺運算

深度學習訓練 / 推論 / 框架

  • CWES1964
    Perception Development for Autonomous Vehicles
    Adolf Hohl, Senior Solution Architect - NVIDIA
    Fabian Weise, Senior Solution Architect - NVIDIA


      Perception Development remains to be a challenging topic for autonomous vehicles. Fidelity in simulation is increasing rapidely and moving the boundaries of perception development.

  • CWES1966
    Deep Learning, Machine Learning and Data Science
    Ross Verrall, Services Operations Manager - NVIDIA
    Miguel Angel Martinez, Senior Solution Architect - NVIDIA
    Dai Yang, Senior Solutions Architect - NVIDIA
    Giuseppe Fiameni, Senior Solution Architect - NVIDIA
    Adam Grzywaczewski, Senior Deep Learning Data Scientist - NVIDIA
    Meriem Bendris, Senior Solution Architect - NVIDIA
    Carlo Nardone, Senior Solution Architect - NVIDIA


      Deep Learning, Machine Learning and Data science is evolving at an unprecedented rate. Almost every day we see new tools and algorithms emerge making impossible possible but at the same time adding layers of complexity to already challenging field.

      To support you, we’re hosting an interactive session with NVIDIA experts so that you can get your toughest questions answered.

      Join us to attend 1:1 chats or group sessions to discuss your projects and challenges with our experts. Example topics include:

      - State of the art algorithms and tools
      - Choosing and optimizing models for production with tools like TensorRT or Triton Inference server.
      - Profiling training and inference bottlenecks in model implementation.
      - GPU acceleration of traditional data science and machine learning workloads with RAPIDS. cuDF (GPU accelerated equivalent of Pandas), cuML (SciKit Learn and XGBoost), cuGraph (NetworkX) and their multi-GPU / multi node implementation with Dask.



繪圖 / 設計 / 虛擬實境

  • CWES1971
    Professional Rendering and Virtualization
    Frank Pudlowsky, Senior Solution Architect - NVIDIA
    Mahmoud El Ghomari, Senior Solution Architect - NVIDIA
    Jits Langedijik, Senior Solutions Architect Professional Visualization - NVIDIA
    Antony Pinto, Senior Solutions Architect - NVIDIA
    Chris Mcleod, Senior Solutions Architect - NVIDIA
    Simon Schaber, Senior Solution Architect - NVIDIA
    Lee Bushen, Senior Solution Architect - NVIDIA


      Key Takeaways:

      - Explore Omniverse, the powerful new collaboration platform for 3D production pipelines
      Learn how to overcome the biggest challenges of virtualization
      Learn how NVIDIA RTX Server™ powers mixed workloads for the most intense graphics and compute workflows for virtualization, rendering, data science, simulation, scientific visualization and augmented/virtual reality
      Learn how CloudXR supports enterprises to integrate AR and VR into their workflows and how it operates across 5G and WiFi networks.



  • CWES1968
    GPUs for healthcare and life science
    Mike Vella, Senior Solution Architect - NVIDIA
    Jonny Hancox, Senior Solution Architect - NVIDIA
    Nicola Rieke, Senior Solution Architect - NVIDIA


      GPUs have the ability to transofrm Healthcare and life sciences, through artificial intelligence for assisting physicians as well as and accelerating existing workloads for digital signal processing and image reconstruction.