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March 24-27, 2014 | San Jose, California
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HANDS-ON LAB

Presentation
Details

S4802A - Hands-on Lab: Developing GPU-Accelerated Applications with MATLAB

Dan Doherty ( Partner Manager, MathWorks )
Prior to working as Partner Manager, Dan was a Product Manager at MathWorks for over 5 years, focusing on MATLAB and core math and data analysis products. Dan received a B.S.E. and M.S.E. in Mechanical Engineering from the University of New Hampshire, where his research focused on prediction of cutting forces during CNC machining.

Learn how you can use MATLAB to develop GPU-accelerated applications without having to learn the intricacies of GPU architectures or low-level GPU computing libraries. Following a brief introduction to MATLAB you will work through exercises that show: (1) Using GPU-enabled MATLAB functions to accelerate large matrix operations; (2) Minimizing overhead associated with data transfer to the GPU; (3) Integrating CUDA kernels in MATLAB. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Beginner
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers

Day: Monday, 03/24
Time: 13:00 - 14:20
Location: Room 230B

S4868 - Hands-on Lab: Signal Processing with cuFFT

Jason Cohen ( Software Engineer, Developer Tools, NVIDIA )
Jason Cohen
Jason Cohen develops performance analysis tools for GPU programming. Currently the primary developer of the CUDA profiler in Nsight Visual Studio, he contributes to all layers of software from drivers to user interfaces, and has developed such features in the tools as the NVTX annotation library and kernel-replay profiling. Jason holds a B.S. in Computer Science and a B.S. and M.S. in Electrical and Computer Engineering from Carnegie Mellon University.

This lab will provide a guided example of developing applications using GPU-accelerated FFTs in C/C++. The process begins with prototyping an algorithm in MATLAB. Next, the algorithm is ported directly to C/C++ using CUFFTW first for convenience, and then cuFFT for production-quality performance. Finally, optimization techniques for maximizing GPU usage will be explored. Emphasis will be placed on using CUDA profiling tools to monitor GPU usage, take accurate measurements, and empirically verify all claims about performance at each step. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Intermediate
Session Type: Hands-on Lab
Tags: Numerical Algorithms & Libraries

Day: Monday, 03/24
Time: 14:30 - 15:50
Location: Room 230A

S4788 - Hands-on Lab: Rapid Multi-GPU Programming with CUDA Libraries

Nikolay Markovskiy ( Compute DevTech Engineer, NVIDIA )
Nikolay is an HPC engineer with experience in scientific research and software development focusing on computational techniques related to physics, chemistry, and biology.

Learn how to use CUDA libraries for quick, high-level programming on multiple GPUs. We will accelerate Octave, using NVBLAS to provide drop-in acceleration on the GPU. We will walk through configuration of the library to run on multiple GPUs. We will then move on to use the extended (XT) library interfaces in cuBLAS and cuFFT, specifically using large matrices support in cuBLAS-XT and single & batch transforms across multiple GPUs using cuFFT-XT. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Beginner
Session Type: Hands-on Lab
Tags: Numerical Algorithms & Libraries

Day: Tuesday, 03/25
Time: 13:00 - 14:20
Location: Room 230A

S4798 - Hands-on Lab: Getting Started with Parallel Programming

Mark Ebersole ( CUDA Educator, NVIDIA )
Highly-Rated Speaker
As CUDA Educator at NVIDIA, Mark Ebersole teaches developers the benefit of GPU computing using the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup and into production. Before joining NVIDIA, he worked at IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University.

Come and see how easy it is to get started programming for a massively parallel NVIDIA GPU. We'll explore the three main techniques; "Drop-in" accelerated libraries, directives, and CUDA-enabled languages. In addition you'll get resources and next steps on what to do next. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Beginner
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers

Day: Tuesday, 03/25
Time: 13:00 - 14:20
Location: Room 230B

S4799 - Hands-on Lab: Introduction to Python Acceleration

Mark Ebersole ( CUDA Educator, NVIDIA )
Highly-Rated Speaker
As CUDA Educator at NVIDIA, Mark Ebersole teaches developers the benefit of GPU computing using the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup and into production. Before joining NVIDIA, he worked at IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University.

Python is one, if not the, fastest growing language today. There is great community support and many tools available. The ability to quickly iterate on algorithms has made it very popular in the scientific community. In this hands-on tutorial, we'll see how we can get the performance of a compiled language by using Continuum Analytics NumbaPro compiler to accelerate Python code on the GPU. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Beginner
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers

Day: Tuesday, 03/25
Time: 14:30 - 15:50
Location: Room 230B

S4933 - Hands-on Lab: CUDA Application Development Life Cycle with NVIDIA® Nsight™ Eclipse Edition

Satish Salian ( Sr. Mgr. CUDA Tools and Developer Experience, NVIDIA )
Satish Salian is a Senior Software Engineering Manager responsible for CUDA developer tools, GPU system tools and CUDA developer experience at NVIDIA. He leads the overall strategy, direction and development of the CUDA tools ecosystem and engineering support for CUDA developers. Satish has been part of the NVIDIA team since 2001 and has also been involved in the development of NVIDIA's Graphics and display tools and related NVAPI SDK. Satish received his Bachelor's degree in Computer Engineering from University of Pune, India.

CUDA application development made easy with NVIDIA's Integrated Development Environment on Linux and MAC. Here's your opportunity to go through a step-by-step, hands-on exercise on editing, compiling, debugging and profiling a CUDA application using Nsight™ Eclipse Edition. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Beginner
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers; Debugging Tools & Techniques

Day: Tuesday, 03/25
Time: 14:30 - 15:50
Location: Room 230A

S4790 - Hands-on Lab: Numerical Integration in CUDA

Carl Ponder ( DevTech Engineer, NVIDIA )
Highly-Rated Speaker
Carl Ponder
Carl is a DevTech Engineer at NVIDIA where he focuses on CUDA application tuning and performance. Carl received his Ph.D. in Computer Science from the University of California, Berkley.

Evaluating integrals is an important part of modelling physical systems. For sufficiently complex systems, integrals as closed-form expressions are difficult to derive or do not exist, so numerical approximation is the method of choice. In this session we will survey methods of Numerical Integration -- Tiling, Monto Carlo and transforms -- and discuss their efficiencies and the characteristics of their approximation error. We will work through some simple hands-on exercises of integrating the Gaussian function, estimating Pi, and measuring the volume of a multidimensional polytope. You will gain some practice writing simple CUDA code and using the cuRand library to generate high-quality random numbers in parallel, which are also applicable to other areas such as randomized simulation. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Intermediate
Session Type: Hands-on Lab
Tags: Numerical Algorithms & Libraries; Finance

Day: Tuesday, 03/25
Time: 16:00 - 17:20
Location: Room 230A

S4800 - Hands-on Lab: CUDA Fortran: Getting Started

Mathew Colgrove ( Dev Tech Software Engineer, NVIDIA )
Mathew Colgrove is a Dev Tech Software Engineer with NVIDIA's Portland Group team. Mat's primary role is to help users in porting code to accelerators using OpenACC and CUDA Fortran as well as assisting with general programming questions. Prior to his current position, he was Quality Assurance manager responsible for both building and maintaining PGI's proprietary automated testing environments. Mat is also NVIDIA's SPEC representative www.spec.org on the CPU and HPG committees.

This tutorial will cover various aspects of writing code in CUDA Fortran, which is the Fortran interface to the CUDA architecture. Topics covered will include a basic introduction to parallel programming concepts using CUDA, performance measurements and metrics, and some basic optimization techniques. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Beginner
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers

Day: Tuesday, 03/25
Time: 16:00 - 17:20
Location: Room 230B

S4793 - Hands-on Lab: Image Processing Using NPP

Yang Song ( Senior Software Engineer, NVIDIA )
Yang Song
Yang Song is the technical lead for NVIDIA's NPP library. As technical lead, he is responsible for NPP's overall design and schedule, and he is currently focused on high performance implementations of image codecs. He joined the NPP team originally as an intern in 2010, and returned full-time in 2011. Yang received his Ph.D in Electrical Engineering from University of Arizona in 2011, with a dissertation focused on hardware implementation of an H.264 codec. As a graduate student, he received a Chinese Government Award for Outstanding Student Abroad, and published a number of journal articles leading to technology disclosures through the University of Arizona. He received his MS and BS degrees from Nanjing University of Science and Technology, China.

Learn how to use the NVIDIA Performance Primitives (NPP) Library to solve image and signal processing problems. The workshop covers a simple but complete example for automatic contrast adjustment of an image. Topics covered include the specification of input and output data formats, the data alignment and memory management for high performance, and the flexibility of regions-of-interest for processing. Users will experience the simplicity to instantiate NPP primitives and the efficiency to leverage the GPU power for image processing. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Beginner
Session Type: Hands-on Lab
Tags: Video & Image Processing

Day: Wednesday, 03/26
Time: 09:00 - 10:20
Location: Room 230A

S4801 - Hands-on Lab: Using Unified Memory in CUDA 6

Mark Ebersole ( CUDA Educator, NVIDIA )
Highly-Rated Speaker
As CUDA Educator at NVIDIA, Mark Ebersole teaches developers the benefit of GPU computing using the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup and into production. Before joining NVIDIA, he worked at IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University.

Prior to the release of CUDA 6, programmers accelerating C or C++ code on an NVIDIA GPU had to manually deal with memory allocation and synchronization between the CPU and GPU memory spaces. This requirement meant it took longer to get code accelerated on the GPU, and in some cases of complex data structures, made it nearly impossible to do manually. With Unified Memory, the task of memory management can be left to the underlying driver and software - leaving the programmer to concentrate on writing kernels and optimizing code. In this hands-on lab, we'll explore different use cases of Unified Memory and it's benefits. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Intermediate
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers

Day: Wednesday, 03/26
Time: 09:00 - 10:20
Location: Room 230B

S4791 - Hands-on Lab: Building a Sparse Linear Solver using CUDA Libraries

Sharan Chetlur ( CUDA Software Engineer, NVIDIA )

In this hand-on session, we will construct a Sparse Iterative Solver using CUDA library routines. We will use the standard CUBLAS and CUSPARSE libraries to construct a simple, yet performant Solver without writing any custom CUDA kernels. We will walk through an example of how to set up and use various CUBLAS and CUSPARSE APIs to implement the SSOR (Symmetric Successive Over-Relaxation) algorithm. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Intermediate
Session Type: Hands-on Lab
Tags: Computational Fluid Dynamics; Computational Physics; Numerical Algorithms & Libraries; Manufacturing

Day: Wednesday, 03/26
Time: 14:00 - 15:20
Location: Room 230A

S4802B - Hands-on Lab: Developing GPU-Accelerated Applications with MATLAB

Dan Doherty ( Partner Manager, MathWorks )
Prior to working as Partner Manager, Dan was a Product Manager at MathWorks for over 5 years, focusing on MATLAB and core math and data analysis products. Dan received a B.S.E. and M.S.E. in Mechanical Engineering from the University of New Hampshire, where his research focused on prediction of cutting forces during CNC machining.

Learn how you can use MATLAB to develop GPU-accelerated applications without having to learn the intricacies of GPU architectures or low-level GPU computing libraries. Following a brief introduction to MATLAB you will work through exercises that show: (1) Using GPU-enabled MATLAB functions to accelerate large matrix operations; (2) Minimizing overhead associated with data transfer to the GPU; (3) Integrating CUDA kernels in MATLAB. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Beginner
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers

Day: Wednesday, 03/26
Time: 14:00 - 15:20
Location: Room 230B

S4792 - Hands-on Lab: Leveraging Accelerated Core Agorithms Using NVIDIA AmgX

Marat Arsaev ( Systems Software Engineer, NVIDIA )
Marat's expertise include image & video processing and software optimization and acceleration using GPUs. Prior to joining NVIDIA, Marat was a Software Developer at MSU Graphics & Media Lab.Marat received his degree in Computer Science from Moscow State University.

AMGX is a new flexible and easy-to-use NVIDIA GPU-accelerated high performance sparse linear solver library. It features variety of popular solvers as well as user-defined solver configurations like nested solvers or preconditioners. Come and learn how easy is it to use the library in your application, configure the solver and get maximum performance of it. You will also learn how to solve your linear system using multiple GPUs using our library. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Intermediate
Session Type: Hands-on Lab
Tags: Computational Structural Mechanics; Computational Fluid Dynamics; Computational Physics; Programming Languages & Compilers

Day: Wednesday, 03/26
Time: 15:30 - 16:50
Location: Room 230A

S4794 - Hands-on Lab: Optimizing CUDA Application Performance with Visual Profiler

Sandarbh Jain ( Software Engineer, NVIDIA )
Sandarbh Jain
Sandarbh Jain is an Engineer in the CUDA Developer Tools group at NVIDIA. He is primarily responsible for CUDA performance analysis tools. Sandarbh received his Bachelor's degree in Computer Engineering from Jamia Millia Islamia, India.

This hand-on session takes you through the various steps involved in optimizing your CUDA application. NVIDIA's CUDA Visual profiler, a cross-platform performance profiling tool that delivers developers vital feedback for optimizing CUDA C/C++ applications, will be used on sample application code to dig out the various performance limiters and assist in the fine tuning of the code. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Intermediate
Session Type: Hands-on Lab
Tags: Performance Optimization

Day: Wednesday, 03/26
Time: 17:00 - 18:20
Location: Room 230A

S4803 - Hands-on Lab: Getting Started with OpenACC

Michael Wolfe ( Compiler Engineer, NVIDIA )
Highly-Rated Speaker
Michael Wolfe has been a compiler engineer at The Portland Group since joining in 1996, where his responsibilities and interests have included deep compiler analysis and optimizations ranging from improving power consumption for embedded microcores to improving the efficiency of Fortran on parallel clusters. He was an associate professor at the Oregon Graduate Institute from 1988 until 1996, and was a co-founder and lead compiler engineer at Kuck and Associates, Inc., prior to that. He earned a PhD in Computer Science from the University of Illinois, and has published one textbook, "High Performance Compilers for Parallel Computing", a monograph, "Optimizing Supercompilers for Supercomputers", and many technical papers.

Learn how to use OpenACC directives to quickly start accelerating your applications. You will learn how to identify your GPU, what language features you can use, the most common directives to insert, how to build your program, and how to run your program. Small sample programs and self-guided exercises will be provided. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Beginner
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers

Day: Wednesday, 03/26
Time: 17:00 - 18:20
Location: Room 230B

S4795 - Hands-on Lab: Doing Great Things with OpenCV

Kirill Kornyakov ( Senior Software Engineer, Itseez )
Kirill Kornyakov has been a member of the core OpenCV development team for the last four years. He works at Itseez (Nizhny Novgorod, Russia), where he leads the development of the OpenCV library for the Android operating system, with a focus on performance optimization for the NVIDIA Tegra platform. He also works on the implementation of real-time computer vision algorithms, mainly Computational Photography and Advanced Driver Assistance Systems (ADAS). Kirill has B.Sc. and M.Sc. degrees from Nizhny Novgorod State University, Russia.

Computer Vision is developing fast, and finding new applications in such areas as driver assistance, computational photography, augmented reality and many others. OpenCV library (http://opencv.org) allows to rapidly prototype new algorithms, but the real-time performance still remains one of the key challenges. And that's why acceleration technologies like CUDA are becoming crucial, especially on embedded and mobile devices. In this tutorial we will study how Computer Vision applications can be optimized using the CUDA technology. We will consider several examples, and make them work faster using CUDA-optimized primitives. Also, some performance tips for developers will be given. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Beginner
Session Type: Hands-on Lab
Tags: Machine Learning & AI

Day: Thursday, 03/27
Time: 09:00 - 10:20
Location: Room 230A

S4870 - Hands-on Lab: Getting More Parallelism Out of Multiple GPUs

Justin Luitjens ( Developer Technologies Engineer, NVIDIA )
Highly-Rated Speaker
Justin has been with NVIDIA for 3 years where he has focused on accelerating customer applications.

Multi-GPU systems provide higher performance per dollar than single-GPU systems.  This has led to a large increase in multi-GPU systems. This workshop will teach you the basics of multi-GPU applications. We will start with an application that utilizes a single GPU. Together we will extend this application to work on multiple GPUs efficiently. Topics covered will include dispatching work, communicating between GPUs, avoiding race conditions efficiently, along with other best practices. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Intermediate
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers

Day: Thursday, 03/27
Time: 09:00 - 10:20
Location: Room 230B

S4796 - Hands-on Lab: Parallel Programming: OpenACC Profiling

Michael Wolfe ( Compiler Engineer, NVIDIA )
Highly-Rated Speaker
Michael Wolfe has been a compiler engineer at The Portland Group since joining in 1996, where his responsibilities and interests have included deep compiler analysis and optimizations ranging from improving power consumption for embedded microcores to improving the efficiency of Fortran on parallel clusters. He was an associate professor at the Oregon Graduate Institute from 1988 until 1996, and was a co-founder and lead compiler engineer at Kuck and Associates, Inc., prior to that. He earned a PhD in Computer Science from the University of Illinois, and has published one textbook, "High Performance Compilers for Parallel Computing", a monograph, "Optimizing Supercompilers for Supercomputers", and many technical papers.

Profile your OpenACC applications using the PGI pgprof profiler, the NVIDIA Visual Profiler, and the NVIDIA compute profiler. Learn how to correlate events in the profile to constructs in your program, to allow you to optimize for better performance. Small sample programs and self-guided exercises will be provided. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Intermediate
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers

Day: Thursday, 03/27
Time: 14:00 - 15:20
Location: Room 230A

S4871A - Hands-on Lab: Using Logan: Mobile Super Computing

Mark Ebersole ( CUDA Educator, NVIDIA )
Highly-Rated Speaker
As CUDA Educator at NVIDIA, Mark Ebersole teaches developers the benefit of GPU computing using the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup and into production. Before joining NVIDIA, he worked at IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University.

The Tegra K1 processor brings the main power unit of top supercomputers to the mobile space; a Kepler-based GPU. The ability to program this GPU using the CUDA platform is going to revolutionize the amazing space of mobile processing applications; from face recognition to machine learning in autonomous robots. In this hands-on lab, we'll learn how to access the the developer board with TK1, as well as use the OpenCV library and CUDA-enabled C/C++ to accelerate a computer vision task. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Intermediate
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers; Mobile Applications

Day: Thursday, 03/27
Time: 14:00 - 15:20
Location: Room 230B

S4797 - Hands-on Lab: Accelerate Your C++ Code with Thrust Library

Maxim Milakov ( Senior DevTech Engineer, NVIDIA )
Maxim spends his time, driving GPU adoption with key application developers, providing support of NVIDIA solutions and technologies (CUDA, OpenACC), ensuring best possible performance of GPU computing applications on current and next-generation architectures; collaborating with the architecture and software teams at NVIDIA to influence the design of next-generation architectures and educating wide range of developers on parallel computing with NVIDIA accelerators. Maxim received his Bachelor Degree from Lomonosov Moscow State University.

Building parallel programs is easy with Thrust's power tools like parallel map, sort, and reduce. This session is a beginner-level tutorial; you will use Thrust's containers and algorithms to create a set of points on a 2D plane and classify them into quadrants. Familiarity with STL containers and algorithms is helpful but not required. By the end of the session you will be able to use basic Thrust algorithms to accelerate your C++ code on GPU, and you will have a solid foundation from which you can learn more advanced techniques. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Beginner
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers

Day: Thursday, 03/27
Time: 15:30 - 16:50
Location: Room 230A

S4871B - Hands-on Lab: Using Logan: Mobile Super Computing

Mark Ebersole ( CUDA Educator, NVIDIA )
Highly-Rated Speaker
As CUDA Educator at NVIDIA, Mark Ebersole teaches developers the benefit of GPU computing using the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup and into production. Before joining NVIDIA, he worked at IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University.

The Tegra K1 processor brings the main power unit of top supercomputers to the mobile space; a Kepler-based GPU. The ability to program this GPU using the CUDA platform is going to revolutionize the amazing space of mobile processing applications; from face recognition to machine learning in autonomous robots. In this hands-on lab, we'll learn how to access the the developer board with TK1, as well as use the OpenCV library and CUDA-enabled C/C++ to accelerate a computer vision task. Be prepared for this hands-on lab by installing the suggested software at bit.ly/gtc14labs on your system.

Session Level: Intermediate
Session Type: Hands-on Lab
Tags: Programming Languages & Compilers; Mobile Applications

Day: Thursday, 03/27
Time: 15:30 - 16:50
Location: Room 230B

Hands-on lab