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2018 GTC San Jose
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Browse & Search for sessions, and click "Add to Schedule" to save sessions to your agenda.

Note sessions are first come, first serve on the day of the conference. Arrive early to the room for high priority sessions.

Sign-up is required for Conference + Training pass holders to reserve seats in Instructor-Led Labs.

TDLIW01 - Pre-GTC DLI Workshop: Fundamentals of Deep Learning for Computer Vision

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.

In this hands-on course, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to:

  • Implement common deep learning workflows, such as image classification and object detection.
  • Experiment with data, training parameters, network structure, and other strategies to increase performance and capability.
  • Deploy your neural networks to start solving real-world problems.

Upon completion, you’ll be able to start solving problems on your own with deep learning. You will need to purchase a special pass to attend this full-day workshop.

See GTC Pricing for more information.

8 hours Pre-GTC DLI Workshops Mike Mendelson - Deep Learning Institute Curriculum Developer, NVIDIA
TDLIW02 - Pre-GTC DLI Workshop: Fundamentals of Natural Language Processing

Pre-requisite: ‘Fundamentals of Deep Learning for Computer Vision’ or similar deep learning experience

In this course, you will receive hands-on training on the latest techniques for understanding textual input using Natural Language Processing. You’ll learn how to:

  • Classify words to accurately understand their meaning
  • Handle factual queries and their semantic meaning
  • Train Machine Translators from one language to another Upon completion of this course, you’ll be proficient in Natural Language Processing using neural networks in any application.

You will need to purchase a special pass to attend this full-day workshop.

See GTC Pricing for more information.

8 hours Pre-GTC DLI Workshops Charles Killam - Certified Instructor, NVIDIA
TDLIW03 - Pre-GTC DLI Workshop: Perception for Autonomous Vehicles

Pre-requisite: ‘Fundamentals of Deep Learning for Computer Vision’ or similar deep learning experience

In this course, you’ll learn how to design, train, and deploy deep neural networks for autonomous vehicles using the NVIDIA DRIVE™ PX2 development platform. Learn how to:

  • Integrate sensor input using the DriveWorks software stack
  • Train a semantic segmentation neural network
  • Optimize, validate, and deploy a trained neural network using TensorRT Upon completion of this course, students will be able to create and optimize perception components for autonomous vehicles using DRIVE PX2.

You will need to purchase a special pass to attend this full-day workshop. See GTC Pricing for more information.

8 hours Pre-GTC DLI Workshops Aaraadhya Narra - Solutions Architect, DLI Certified Instructor, NVIDIA
TDLIW04 - Pre-GTC DLI Workshop: Fundamentals of Accelerated Computing with CUDA C/C++

Pre-requisite: None

Duration: 8 hours

Format: Self-paced online or instructor-led

Languages: English

The CUDA computing platform enables the acceleration of CPU-only applications to run on the world's fastest massively parallel GPUs. Experience C/C++ application acceleration by:

  • Accelerating CPU-only applications to run their latent parallelism on GPUs
  • Utilizing essential CUDA memory management techniques to optimize accelerated applications
  • Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
  • Leveraging command line and visual profiling to guide and check your work

Upon completion of this workshop, you'll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

See GTC Pricing for more information.

8 hours Pre-GTC DLI Workshops Joshua Wyatt - Content Developer, NVIDIA Deep Learning Institute, NVIDIA
SE0000 - Welcome Reception

At this reception, meet NVIDIA staff and other GTC alumni to get tips, especially if you're a first-timer.

Special Event - 2 h Special Event
SE0002 - Dinner with Strangers (Sun)

Join a random group of GTC attendees for enlightening conversations over a self-hosted dinner in great restaurants nearby. Less creepy than it sounds, this is one of the more popular programs at GTC.

Sign up in Main Lobby.

Special Event - 2 h Special Event
CE8164 - Connect with the Experts: CUDA-based Raytracing and Rendering

We will answer your questions on the design and implementation of renderers based on raytracing using CUDA, and discuss how to get the best performance out of NVIDIA hardware in your renderer.

Connect with Experts are informal sessions where you can ask experts from NVIDIA and other organizations your burning questions about a specific subject.

1 Hour Connect with the Experts Carsten Waechter - Ray Tracing Software Architect, NVIDIA
Pascal Gautron - Senior Developer Technology Engineer, NVIDIA
L8111A - Jetson Developer Tools Training Labs

This lab is focused on teaching you how to maximize the productivity when developing software for the Jetson platform. You will experience first hand how to manage source code on the host PC to cross-compile the software, initiate remote debugging sessions to debug CPU C/C++ and CUDA C code. Through a comprehensive set of exercises, you will also learn how to use the CUDA Visual Profiler for optimizing CUDA kernels, use the Tegra System Profiler for optimizing CPU code and tracing multi-process system-wide activities, and use Tegra Graphics Debugger for debugging and profiling 3D graphics applications. Prerequisites: Basic CUDA-C and C++ coding skills.

120 Minutes Instructor-Led Lab Sebastien Domine - VP SW Eng. Developer Tools, NVIDIA
L8119 - Programming GPU-Accelerated OpenPOWER Systems with OpenACC

In this tutorial you will learn how to handle the massive computing performance offered by POWER systems with NVLink-attached GPUs – the technology also powering Sierra and Summit, two of the fastest supercomputers in the US. We will present the POWER architecture and highlight the available software stack, before we dive into programming the attached GPUs with OpenACC. By using real-world examples we will get to know the hardware architectures of both CPU and GPU and learn the most important OpenACC directives on the way. The resulting GPU-accelerated program can easily be used on other GPU-equipped machines and architectures, by nature of OpenACC's portable approach. The lab requires the attendees to bring their own laptop. We will work on IBM Minsky servers (POWER8 CPUs with P100 GPUs).

120 Minutes Instructor-Led Lab Andreas Herten - Post-Doctoral Researcher GPUs in HPC, Jülich Supercomputing Centre
L8143 - Image Segmentation with TensorFlow

Prerequisites: Image Classification with DIGITS

Duration: 2 hours

Framework: Caffe with DIGITS and TensorRT

Image (or semantic) segmentation is the task of placing each pixel of an image into a specific class. In this lab, you'll segment MRI images to measure parts of the heart by:

• Comparing image segmentation with other computer vision problems

• Experimenting with TensorFlow tools such as TensorBoard and the TensorFlow Python API

• Learning to implement effective metrics for assessing model performance

Upon completion of this lab, you'll be able to set up most computer vision workflows using deep learning.

Presented by the NVIDIA Deep Learning Institute (DLI).

120 Minutes Instructor-Led Lab Mike Mendelson - Deep Learning Institute Curriculum Developer, NVIDIA
L8152 - Medical Image Segmentation with DIGITS

Prerequisites: 'Fundamentals of Deep Learning with Computer Vision' or similar experience

Duration: 2 hours

Framework: Caffe

Image (or semantic) segmentation is the task of placing each pixel of an image into a specific class. In this lab, you'll segment MRI images to measure parts of the heart by:

• Extending Caffe with custom Python layers

• Implementing the process of transfer learning

• Creating fully convolutional neural networks from popular image classification networks

Upon completion, you'll be able to set up most computer vision workflows using deep learning.

Presented by the NVIDIA Deep Learning Institute (DLI).

120 Minutes Instructor-Led Lab Steven Steinke - Curriculum Developer, NVIDIA
L8167 - Image Creation using Generative Adversarial Networks using TensorFlow and DIGITS

This lab will guide you through the process of training a Generative Adversarial Network (GAN) to generate image contents in DIGITS. You'll learn how to: • Use Generative Adversarial Networks (GANs) to create handwritten numbers • Visualize the feature space and use attribute vector to generate image analogies • Train a GAN to generate images with set attributes Upon completion, you'll be able to use GANs to generate images by manipulating feature space. Prerequisites: Fundamentals of Deep Learning with Computer Vision or similar experience

120 Minutes Instructor-Led Lab Jonathan Bentz, NVIDIA
S8225 - Sharing Physically Based Materials Between Renderers with MDL We'll discuss the basics of NVIDIA's Material Definition Language, showing how a single material can be used to define matching appearances between different renderers and rendering techniques. End users will learn how physically based definitions can be defined, while developers will learn what's entailed in supporting MDL within their own products or renderers. 50-minute Talk Jan Jordan - Software Product Manager MDL, NVIDIA
Lutz Kettner - Director, Rendering Software and Material Definition, NVIDIA
S8236 - Singularity: Reproducible, Trusted Containers for Scientific Computing

Singularity is a container technology which is widely supported by HPC centers and service providers because it facilitates extreme mobility of compute via verifiable, trusted containers. This talk will cover a high level view of container computing and an introduction to Singularity, description of the Singularity Image Format (SIF), as well as technical recipes and usage examples with GPUs. After attending this talk, you will have a strong understanding of containerization and how to leverage this technology to create extremely reproducible workflows.

50-minute Talk Gregory Kurtzer - CEO, SyLabs
S8250 - Maximizing The Power of GPU For Diverse Workloads of Enterprise Digital Workspaces On VMware vSphere

Enterprise Digital Workspaces support diverse workloads including virtual desktops, deep learning, big data. Nvidia GPUs bring high performance computing (HPC) for graphics, GPGPU, especially machine learning workloads. They also provide HW encode and decode to accelerate the processing of video contents. In this session, we will explore performance and resource utilization of various workloads that leverage different capabilities of GPU like graphics, compute and H.264 HW encode / decode. Nvidia virtualized GPUs and VMware vSphere brings in tremendous combined benefits for both GPU-based workloads and data center management via virtualization. We will present results of our research on running diverse workloads on vSphere platform using Nvidia GRID GPUs. We explore vSphere features of Suspend/Resume and vMotioning of vGPU based virtual machines. We will quantify benefits of vGPU for data center management using VMware vSphere and describe techniques for efficient management of workloads and datacenter resources.

50-minute Talk Uday Kurkure - Staff Engineer, VMware
Hari Sivaraman - Staff Engineer, VMware
S8286 - Quick and Easy DL Workflow Proof of Concept Spin up a deep learning (DL) proof-of-concept on a budget. We'll walk you through a DL workflow in the cloud leveraging DIGITS, then download a trained model, and run inference on a Jetson TX2. This session considers multiple options such as Nimbix, AMI, and NGC on Tesla P100, Tesla V100, and NVIDIA DGX-1 servers. This tutorial will be a combination of lecture, live demos, and detailed instructions. 50-minute Talk Jeffrey Weiss - Director, Solution Architects, NVIDIA
Alec Gunny - Solutions Architect, NVIDIA
Kenneth Hester - Solution Architect, NVIDIA
S8382 - Zero to GPU Hero with OpenACC GPUs are often the fastest way to obtain your scientific results, but many students and domain scientists don't know how to get started. In this tutorial we will take an application from simple, serial loops to a fully GPU-enabled application. Students will learn a profile-guided approach to accelerating applications, including how to find hotspots, how to use OpenACC to accelerated important regions of code, and how to get the best performance they can on GPUs. No prior experience in GPU-programming or OpenACC is required, but experience with C, C++, or Fortran is a must. Several books will be given away to attendees who complete this tutorial. 80 Minutes Tutorial Jeffrey Larkin - Senior DevTech Software Engineer, NVIDIA
S8391 - Investigating Data Augmentation Strategies for Advancing Deep Learning Training We saw the huge success of the deep learning paradigm and the superhuman capability in numerous benchmarks in image, video, audio, or text. However, it poses huge challenges as adopting the methods in industrial applications (mainly due to the lack of quality tracking data) as the neural networks consume enormous parameters and require relatively huge quality training data. We'll aim for investigating the "data augmentation" strategies – increasing quality training data for robust inference – across different learning problems mainly in image, video, 3D, and IoT data streams. We'll first quantify the importance of training data for deep neural networks then review numerous strategies, such as crawling from the web, utilizing generative models, 3D computer graphics, augmented reality, engagement in social media, gaming, etc. We'll compare the effectiveness among the diverse strategies. As generally taking the data from other domains, we also need to deal with the cross-domain learning problem. We'll provide detailed insights from our recent work published in top conferences (e.g., CVPR, ICCV, AAAI, etc.) and those cases in industrial applications. 50-minute Talk Winston Hsu - Professor, National Taiwan University
S8467 - Playing FPS Games with Deep Reinforcement Learning Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in 2D environments that are fully observable to the agent. We present the first architecture to tackle 3D environments in first-person shooter games that involve partially observable states. Typically, deep reinforcement learning methods only utilize visual input for training. We present a method to augment these models to exploit game feature information, such as the presence of enemies or items, during the training phase. Our model is trained to simultaneously learn these features along with minimizing a Q-learning objective, which is shown to dramatically improve the training speed and performance of our agent. Our architecture is also modularized to allow different models to be independently trained for different phases of the game. We show that the proposed architecture substantially outperforms built-in AI agents of the game as well as average humans in deathmatch scenarios. 25-minute Talk Devendra Singh Chaplot - Ph.D. student, Carnegie Mellon University
S8483 - Empowering CUDA Developers with Virtual Desktops You've just been tasked with deploying the NVIDIA CUDA Toolkit to a group of developers. Wouldn't it be great if you could save time deploying it, protect the developers work, reduce the amount of unique workstation hardware needed, & get more out of your hardware investment? This session will show how this can be done with VMware Horizon Virtual Desktops leveraging vGPUs and the CUDA Toolkit. The CUDA Toolkit is a core component of most developer's desktops and provides the underpinnings for many development operations that take advantage of GPU technology. It can, and often is, difficult to install on Virtual Machines. We will walk through its deployment on Linux virtual machines, insuring requirements for both the CUDA Toolkit & VMware Horizon with vGPU are met. 50-minute Talk Tony Foster - Sr. Advisor, Technical Marketing Ready Bundles for HPC, Dell EMC
S8512 - Accelerating Generative Design by Leveraging GPUs on the Cloud We'll walk through the use of GPU accelerated voxel-based stress solver in the level set topology optimization engine used for Autodesk Generative Design. We'll discuss how the solver benefits from executing on the GPU over our CPU implementation and why this is important from both a costing and efficiency standpoint. Autodesk has partnered closely with Amazon to deliver cloud-based simulation on their platform and we will talk about how we are driving GPU usage on the cloud and how we have used the nvidia-docker plugin for PCIe passthrough to run on Amazon's GPU compute systems. 50-minute Talk Jerran Schmidt - Design Engineer, Autodesk
Christopher Hebert - Developer Technology Engineer, NVIDIA
S8586 - Writing Graph Primitives with Gunrock Learn how to use Gunrock, a state-of-the-art CUDA-based graph-processing library specifically designed for the GPU, to develop fast, efficient, and complex graph primitives. Gunrock achieves a balance between performance and expressiveness by coupling high-performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. Gunrock is a stable, powerful, and forward-looking substrate for GPU-based, graph-centric research and development. Like many graph frameworks, it leverages a bulk-synchronous programming model and targets iterative convergent graph computations. We believe that Gunrock offers both the best performance on GPU graph analytics as well as the widest range of primitives. 80 Minutes Tutorial Muhammad Osama - Graduate Researcher, University of California Davis
S8587 - Recent Progress in Accelerating Monte Carlo Simulation on GPU for Pricing and Risk Management of Financial Instruments

Learn about recent progress in accelerating Monte Carlo simulation on the GPU in applications for pricing financial instruments and risk management. We'll focus on the forward Monte Carlo simulation, which allows for a natural parallelization across CUDA cores, and present a recent extension of our implementation to a broad selection of industry standard valuation models for different asset classes, including hybrid models that can be used to price multi-currency and multi-asset portfolios. Even with increasing complexity and dimensionality of valuation models, our benchmarks show stable GPU speedup factors in the ranges of 20x and 30x for calculations with floating point double precision FP64 and single precision FP32, respectively. We also briefly summarize a most recent research project on a more complex backward (/American / Least Squares) Monte Carlo simulation method, based on regression algorithms used to price general financial instruments with optionality. The latter method heavily relies on matrix calculations and benefits from using GPU- accelerated libraries, cuBLAS for linear algebra and cuSOLVER for solvers.

25-minute Talk Serguei Issakov - Global Head of Quantitative Research and Development, Senior Vice Pres, Numerix
S8596 - Overcoming Missing Modalities in Remote Sensing

Recent advances in earth observation are opening up a new exciting area for exploration of satellite image data. We'll teach you how to analyse this new data source with deep neural networks. Focusing on emergency response, you will learn how to apply deep neural networks for semantic segmentation on satellite imagery. We will specifically focus on multimodal segmentation and the challenge of overcoming missing modality information during inference time. It is assumed that registrants are already familiar with fundamentals of deep neural networks.

25-minute Talk Damian Borth - Director, German Research Center for Artificial Intelligence (DFKI)
Benjamin Bischke - PhD Candidate, German Research Center for Artificial Intelligence (DFKI)
S8660 - A Deep Neural Network for Estimating Depth from Stereo We present a deep neural network architecture for estimating 3D depth from stereo images. The network is modeled after computer vision stereo matching pipelines to simplify training process. Our loss function consists of a photometric loss term and Lidar based loss terms. This combination makes it possible to train our DNN in a supervised, semi-supervised and completely unsupervised way. Our DNN produces depth maps that have accuracy similar to Lidar based depth. We also compare our stereo DNN architecture to other stereo architectures as well as to a monocular depth DNN architecture. We demonstrate qualitative and quantitative test results. 50-minute Talk Nikolai Smolyanskiy - Principal Deep Learning and Computer Vision Engineer, NVIDIA
Alexey Kamenev - Senior Deep Learning and Computer Vision Engineer, NVIDIA
S8666 - Deploying Autonomous Vehicles with NVIDIA DRIVE

DRIVE PX is an open platform for Autonomous Driving Ecosystem. It’s been adopted by over 300 partners in the automotive ecosystem to develop solutions for vehicles that are intelligent and autonomous. This talk will outline the technical challenges facing development of autonomous intelligent vehicles and provide details of how the next generation of DRIVE AI car computer i.e. DRIVE Xavier and DRIVE Pegasus address these challenges.

50-minute Talk Srikanth Sundaram - Senior Product Manager DRIVE PX 2, NVIDIA
S8704 - NVIDIA IndeX - Advanced Large-Scale Data Visualizations on the NVIDIA GPU Cloud (NGC)

NVIDIA IndeX incorporates NVIDIA's hardware and software technology to enable interactive high-quality 3D visual exploration and real time evaluation of computed and simulated large data for a wide range of scientific fields: NVIDIA IndeX is deployed for DGX technology and can be made available as a container on the cloud, such as AWS or NGC. With NVIDIA IndeX scientists gain unique insights into unlimited size and complexity of 3D data and NV-IndeX's in-situ solution allows scientists envisioning remarkable new data simulation and visualization workflows. We present NVIDIA IndeX's CUDA programming interface for implementing novel visualization techniques, illustrates CUDA programs that produce various high-fidelity visualizations and demonstrates large-scale data visualization on the NVIDIA GPU Cloud based on custom visualization techniques.

25-minute Talk Marc Nienhaus - Sr. Manager Software Engineering, NVIDIA IndeX, NVIDIA
Alexander Kuhn - Senior Software Engineer, NVIDIA
Henning Lux - Senior Software Engineer, NVIDIA
S8727 - Improving NAMD Performance on Volta GPUs In 2007, NAMD was the first full-featured production molecular dynamics software to use CUDA for accelerating its costliest computations. We'll describe our latest efforts, techniques, and results in our quest to optimize NAMD to make best use of the tremendous computational capabilities of state-of-the-art Volta GPUs, particularly in new dense node configurations such as the NVIDIA DGX and ORNL Summit systems that feature NVLink-connected GPUs. In existence now for over 20 years, NAMD is a sophisticated parallel molecular dynamics program. NAMD development has emphasized parallel scalability to support large-size and long-timescale biomolecular simulations running on petascale supercomputers. As GPU technology has evolved, NAMD has benefited from moving greater amounts of work to the GPU. NVIDIA's release of Volta has now shifted the balance almost entirely to the GPU, with the small remaining CPU calculations often posing bottlenecks to NAMD's performance. Attendees will learn optimization strategies and pitfalls for achieving higher performance as Amdahl's Law poses an ever increasing challenge for mature GPU-accelerated codes like NAMD. 50-minute Talk David Hardy - Research Programmer, University of Illinois at Urbana-Champaign
Ke Li - HPC Developer Technology Engineer, NVIDIA
John Stone - Senior Research Programmer, University of Illinois at Urbana Champaign
S8782 - A Cross-Field VR Case Study to Treat Children with Autism Spectrum Disorder We build a contextualized learning system with realistic interaction for medical education. This system is intended to integrate Virtual Reality (VR) with the knowledge of occupational therapy, especially for autistic children. Our system supports a variety of scenes to facilitate training for children's confidence, adaptability and social ability. Adopting our system, the training content is no longer limited to the traditional treatment room. Clearly, therapist and children are able to save their arranging time and focus on immersive training. 25-minute Talk Huai-Sheng Huang - Assistant Professor, Fu Jen Catholic University - Department of Information Management
S8823 - Latest Tools and Techniques for Training and Deploying Deep Neural Networks in Educational Environments

Craig Morioka, UCLA Adjunct Associate Professor of Radiological Sciences, and Dima Lituiev, Postdoctoral Scholar at the University of California San Francisco, Institute for Computational Health Sciences, will discuss how they empower their fellow faculty, staff, and students with the latest techniques in training and deploying deep neural networks through NVIDIA’s Deep Learning Institute (DLI) University Ambassador Program - a new AI and Deep Learning education enablement program for universities. This will include a dive into the benefits of an online learning platform, which uses GPUs in the cloud, by stepping through the DLI’s online Image Segmentation and Radiomics labs. The Image Segmentation lab leverages an example from medical image analysis where it is often important to separate pixels corresponding to different types of tissue or cells for the purposes of diagnostics and treatment planning. Dima uses image segmentation in his research to facilitate diagnostics of kidney rejection by analyzing histological slides from patients with kidney transplants. We will explore how the Tensorflow code is structured and how the Tensorboard tool can be used to visualize structure and training dynamics of segmentation models. The focus of the Radiomics lab is detection of the 1p19q co-deletion biomarker using deep learning - specifically convolutional neural networks – using the Keras and TensorFlow computing frameworks. Attendees will also learn how they can apply to become a DLI University Ambassador and bring the latest in Deep Learning and AI education to their academic communities.

50-minute Talk Joseph Bungo - Deep Learning Institute (DLI) Program Manager, NVIDIA
Dmytro Lituiev - Postdoctoral Research Fellow, UC Berkeley and UCSF
Craig Morioka - Adjunct Associate Professor of Radiological Sciences, UCLA
S8873 - GBM Inferencing on GPU We'll present a novel GPU implementation for batched GBM inferencing. We'll also present detailed performance comparison of our implementation against the state-of-the-art libraries such as XGBoost and Treelite. We'll then compare inference performance on various real-world datasets. 50-minute Talk Shankara Rao Thejasw Nanditale - Compute Devtech Engineer, NVIDIA
Vinay Deshpande - Compute DevTech Engineer, NVIDIA
S8891 - Computer-Augmented Healthcare: Opportunities and Challenges

The Role of Data in Achieving Precision and Value in Healthcare The goal of healthcare is to provide the most effective treatment to every patient in the most efficient way. Data plays a key role in every aspect of this process — from decision support systems that provide a clinician with the right information at the right time, to scheduling algorithms that predict patient flow and schedule accordingly, to analytics to coach and support patients in achieving or maintaining a healthy lifestyle. Achieving the vision of a data-informed healthcare system will require fundamental advances in many areas including causal inference, inference on complex, high-dimensional and heterogeneous data, missing data, process modeling, bias reduction, statistical validation, and model adaptation, to name a few. In this talk, I will illustrate some of these challenges through concrete examples within the Malone Center.

25-minute Talk Gregory Hager - Professor and Director, The Malone Center for Engineering in Healthcare, Johns Hopkins University
S8964 - Sensing Technologies for an Autonomous Tomorrow (Presented by Analog Devices)

The future of autonomous transport is upon us. In order to provide safe, reliable transport for all, it is essential to have the most accurate, real time 3D map around the vehicle. The 360 degree safety shield created using radar, LIDAR, cameras, and IMUs make up the perception sensor suite is the foundation to making this a reality. Data from high performance imaging radar, LIDAR, and cameras are fused together giving the vehicle it's sense of sight, whereas the IMU gives the vehicle is sense of feeling, while also ensuring it maintains its heading. The large amount of data generated from Analog Devices' Drive360 sensors will require high performance AI computers in the vehicle such as NVIDIA's Drive Pegasus to generate the real time 3D map. Together, Analog Devices & NVIDIA can enable safe, reliable autonomous transportation for all.

25-minute Talk Chris Jacobs - VP, Autonomous Transportation & Automotive Safety, Analog Devices
S8979 - An Introduction to CUDA Programming Session 1 of 4 (Presented by Acceleware)

Join us for an informative introduction to CUDA programming. The tutorial will begin with a brief overview of CUDA and data-parallelism before focusing on the GPU programming model. We will explore the fundamentals of GPU kernels, host and device responsibilities, CUDA syntax and thread hierarchy. A programming demonstration of a simple CUDA kernel will be delivered. Printed copies of the material will be provided to all attendees for each session - collect all four!

80 Minutes Tutorial Dan Cyca - Chief Technology Officer, Acceleware
Chris Mason - Technical Product Manager, Acceleware
S81028 - Earth Observation From Space: Deep Learning based Satellite Image Analysis

Learn how recent advances in Earth observation are opening up a new exciting area for exploration of satellite image data with deep learning. Focusing on real-world scenarios, we will teach you how to analyze this exciting remote sensing data source with deep neural networks. An automated satellite image understanding is of high interest for various research fields and industry sectors such as the insurance, agriculture or investing industry. You will learn how to apply deep neural networks in natural disaster situations and for the classification of land-use, land-cover and building types.

25-minute Talk Patrick Helber - PhD candidate, German Research Center for Artificial Intelligence
S8122 - Dissecting the Volta GPU Architecture through Microbenchmarking

We'll present the architectural details of the Volta GPU discovered via our micro-benchmarks and reveal the geometry and latency of Volta's complex memory hierarchy, the format of its encoded instructions, and the latency of commonly used instructions. The knowledge being shared enables developers to craft better optimized code than what is currently possible through publicly available information and tool chains.

25-minute Talk Zhe Jia - R&D Engineer, Citadel Securities
S8743 - Deep Learning for Locomotion Animation We'll examine tools and technologies that NVIDIA's GameWorks team is building to leverage the power of deep learning for content creation, demonstrating recent research in ways that neural networks can be used to generate realistic looking human animation. We'll talk about how to apply GPUs for high-performance runtime inferencing of these networks for use in games or real-time VFX scenarios. 25-minute Talk Gavriel State - Senior Director, System Software, NVIDIA
S8934 - Stitching 8K Video in the Cloud with Pixvana SPIN Studio and VRWorks At Pixvana, we are building a video creation and delivery platform for the emerging mediums of virtual, and mixed reality (XR). Pixvana SPIN Studio is built on a cloud media processing system using AWS and Azure GPU instances to create and deliver high-quality VR video. In this talk, Sean Safreed and Paul Barsic will discuss the new cloud-based stitching module built on Nvidia VR Works and running on CUDA/Linux. The talk will introduce the architecture for cloud stitching, interaction with AWS and Azure and dive into the end-to-end functions used to go from camera source to final 360 VR media. 25-minute Talk Sean Safreed - Co Founder and CPO, Pixvana
S8956 - Interactive Visualization of Massive Geoscience Datasets DecisionSpace® Geosciences (DSG) delivers a collaborative geoscience interpretation environment with integration across multi-domain workflows and data types. In this session, we will discuss our approach to integrating NVIDIA IndeX with DSG. This integration provides a scalable visualization solution capable of interactively rendering and manipulating massive geoscience datasets such as terabyte size seismic volumes and horizons. We will also discuss our usage of the latest features of IndeX to provide advanced visualization capabilities. 25-minute Talk Venkat Viswanathan - Development Manager, Platform & Visualization, Halliburton-Landmark
S8963 - How Will Machine Learning and Artificial Intelligence Change the Practice of Healthcare

This session will give an overview of new methods that leverage machine learning and causal inference to enable reliable individualized decision-making. We will present applications in different areas of healthcare where real-time inference is changing the practice of medicine. The latter also gives rise to new challenges in developing human-machine collaborative systems.

25-minute Talk Suchi Saria - John C. Malone Assistant Professor, Johns Hopkins University
CE8120 - Connect with the Experts: Data Analytics and Machine Learning

Join us in the hangout area to get your technical questions about optimizing data analytics pipelines and machine learning algorithms answered by NVIDIA experts. Learn about the latest capabilities to accelerate entire data analytics pipelines from databases to analytic algorithms, machine learning, and graph analytics. How can GPUs excel at data intensive workloads like complex data analytics tasks? By example, we will demonstrate how to accelerate critical components, covering benchmarks, tools, frameworks, etc. Related presentations: S8289 - How to Get the Most out of GPU Accelerated Database Operators S8417 - Breaking the Speed of Interconnect with Compression for Database Applications S8502 - GOAI One Year Later

Connect with Experts are informal sessions where you can ask experts from NVIDIA and other organizations your burning questions about a specific subject.

1 Hour Connect with the Experts Michael Wendt - Manager, Applied Engineering Solutions, NVIDIA
Keith Kraus - Senior Engineer, NVIDIA
Andrey Adinets - Developer, NVIDIA
Levs Dolgovs - Developer, NVIDIA
Nikolay Sakharnykh - Sr. Developer Technology Engineer, NVIDIA
CE8126 - Connect with the Experts: Deep Learning Basics

Attend this session to get your questions on deep learning basics and concepts answered. NVIDIA experts can help you with the fundamentals and provide guidance on how and when to apply Deep Learning and GPUs to your work. No question is too basic to ask.

Connect with Experts are informal sessions where you can ask experts from NVIDIA and other organizations your burning questions about a specific subject.

1 Hour Connect with the Experts Rajan Arora - Solution Architect, NVIDIA
Xuan Vinh Nguyen, NVIDIA
Robert Crovella - SA Mgr., NVIDIA
CE8135 - Connect with the Experts: CUDA Libraries

CUDA libraries accelerate AI and HPC applications, and span across deep learning, linear algebra, signal processing and core math. Stop by to chat with NVIDIA experts whether you are a beginner with "how-to" questions or a CUDA ninja and want to dive deep into strategies to speed up your applications.

1 Hour Connect with the Experts Lung-Sheng Chien - Software Engineer, NVIDIA
Lukasz Ligowski, NVIDIA
Harun Bayraktar, NVIDIA
Steven Rennich, NVIDIA
Murat Guney - AI devtech engineer, NVIDIA
S81014 - Advancing State-of-the-Art of Autonomous Vehicles and Robotics Research using AWS GPU Instances (Presented by Amazon Web Services)

Toyota Research Institute's (TRI) mission is to improve the quality of human life through advances in artificial intelligence, automated driving, and robotics. Learn more about their research and how they are using AWS EC2 P3 instances, industry's most powerful GPUs instances, in combination with other AWS services to enable autonomous vehicles and robots at scale.

50-minute Talk Chetan Kapoor - Senior Product Manager - EC2, Amazon Web Services
Adrien Gaidon - Machine Learning Lead, Toyota Research Institute
Mike Garrison - Senior Infrastructure Engineer, Toyota Research Institute
S8200 - Domain Adaptation Using Adversarial Training for Semantic Segmentation and Caption Style Transfer We'll introduce the basic concept of domain adaptation and how to use adversarial training to achieve unsupervised domain adaptation. We'll then describe how the technique is used in two tasks: improving semantic segmentation across cities, and transferring language style for image captioning. In particular, we combine domain adaptation with policy gradient-based reinforcement learning approach to transfer language style. The details and results of both tasks are published in ICCV 2017. 25-minute Talk Min Sun - Assistant Professor, National Tsing Hua University
S8216 - Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image Learn how to predict a dense depth image from a sparse set of depth measurements and a single RGB image. This approach can be applied to serve as a plug-in module in simultaneous localization and mapping to convert sparse maps to dense maps, and as a super-resolution of LiDAR depth data. We'll describe the performance of our prediction method, explain how to train the depth prediction network, and showcase examples of its applications. Codes and video demonstration are also publicly available. This session is for registrants who are already familiar with basic machine learning techniques. 25-minute Talk Fangchang Ma - Ph.D. Candidate, Massachusetts Institute of Technology
S8227 - Integrating the NVIDIA Material Definition Language MDL in Your Application The NVIDIA MDL SDK provides a rich toolset to integrate MDL in a wide range of renderers, from physically based ray tracing to real-time applications. In this tutorial-like session, we'll show how MDL materials and texturing functions can be compiled for OptiX/CUDA, x86, and OpenGL target platforms. We'll present how the MDL Distiller can be used to simplify MDL materials for use with real-time rendering solutions. Developers will learn about the available APIs and example code. 50-minute Talk Sandra Pappenguth - Senior Software Engineer, NVIDIA
Matthias Raab - Senior Graphics Software Engineer, NVIDIA
S8264 - Practical Applications of Virtual Reality in Architecture We'll provide an overview of various VR delivery methods, including software, like Enscape and Fuzor, as well as hardware like Oculus Rift and HTC Vive. Perhaps more important than the software and hardware, we'll discuss the dynamic experienced within multi-discipline design teams and with clients. We'll talk about public meetings and client feedback. All this is made possible using NVIDIA Quadro P5000 and P6000 graphics cards. 50-minute Talk Daniel Stine - VDC/BIM Administrator, LHB
S8343 - Detection of Financial Statement Fraud using Deep Autoencoder Networks

Explore how auditors are applying deep learning to detect "anomalous" records in large volumes of accounting data. The Association of Certified Fraud Examiners estimates in its Global Fraud Study 2016 that the typical organization loses 5% of its annual revenues due to fraud. At the same time, organizations accelerate the digitization of business processes affecting Enterprise Resource Planning (ERP) systems. These systems collect vast quantities of electronic journal entry data in general- and sub-ledger accounts at an almost atomic level. To conduct fraud, perpetrators need to deviate from regular system usage or posting pattern. This deviation will be weakly recorded and reflected accordingly by a very limited number of "anomalous" journal entries of an organization. To anomalous journal entries, several deep auto-encoder networks are trained using NVIDIA's DGX-1 system. The empirical evaluation on two real-world accounting datasets underpinned the effectiveness of the trained network in capturing journal entries highly relevant for a detailed audit while outperforming several baseline methods.

25-minute Talk Marco Schreyer - Researcher, German Research Center for Artificial Intelligence
Timur Sattarov - Forensic Data Analyst, PricewaterhouseCoopers GmbH WPG
S8380 - Image Data Augmentation on GPU: One Method That Does It All Data augmentation is an effective method to boost your deep-learning training performance. There are many ways of doing this augmentation, and the ways to do so are not well established, and not all deep learning frameworks support augmentation natively. We present a method of doing data augmentation that is based on transformation matrices to perturb both space and color, in a way that is easy to use and understand, framework-agnostic, and fast (runs on GPU). This method works especially well for augmentations that need to be applied to both images and labels, typical in object detection and segmentation tasks. Image augmentation is a job that GPU's excel at, and it will significantly reduce the load, and need, for a fast CPU. 25-minute Talk Tim Zaman - Software Engineer, NVIDIA
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