NVIDIA and High Performance Research Computing (HPRC) at Louisiana State University are pleased to be organizing a 2-day High Performance Computing and Programming event.

Why you should attend: NVIDIA GPUs are the world’s fastest and most efficient accelerators delivering world record scientific application performance. NVIDIA’s CUDA Technology is the most pervasive parallel computing model, used by over 250 scientific applications and over 150,000 developers worldwide. This Programming Workshop will focus on introducing scientific computing programming utilizing NVIDIA GPUs to accelerate applications across a diverse set of domains.

Presented by NVIDIA instructor Dr. Jonathan Bentz, the workshop will introduce programming techniques using CUDA and OpenACC paradigms as well as optimization, profiling and debugging methods for GPU programming. An introduction to Deep Learning using GPUs will also be covered.

Who it's for: Undergraduate/Graduate Students, Postdocs, Researchers, and Professors

Agenda: June 1st | Day 1
Introduction to GPU programming: 9AM - 4:30PM
• High Level Overview of GPU architecture
• OpenACC: An introduction on compiler directives to specify loops and regions of code in standard C, C++ and Fortran to be offloaded from a host CPU to an attached accelerator
• Hands-On examples to focus on data locality
• GPU-Accelerated Libraries: discussion including AmgX, cuSolver, cuBLAS and cuDNN
• Basics of GPU Programming; An introduction to the CUDA C/C++ Language
• 4 Hands-On examples will Illustrate simple kernel launches and using threads

Agenda: June 2nd | Day 2
Performance and Optimization: 9AM - 4:30PM with an Intro to Deep Learning
• Overview of Global and Shared memory usage
• Hands-On examples will illustrate a 1D Stencil and Matrix Transpose
• Using NVIDIA Profiler to identify performance bottlenecks
• Advanced Optimizations using Streams and Concurrency to overlap communication and computation
• Hands-On examples will use CUBLAS with Matrix Multiply

Intro to Deep Learning / Machine Learning
• Deep Learning and the Engine of GPUs; An Overview Deep learning
• Caffe, Torch, Theano explained; NVIDIA DIGITS Lab
Caffe Lab Examples (time permitting)

Deep learning is a rapidly growing segment born from the fields of artificial intelligence and machine learning. It is increasingly used to deliver near-human level accuracy for image classification, voice recognition, natural language processing, sentiment analysis, recommendation engines, and more. Applications areas include facial recognition, scene detection, advanced medical and pharmaceutical research, and autonomous, self-driving vehicles. This overview will focus on introducing attendees to the use of GPU accelerated deep learning frameworks utilizing NVIDIA GPUs for ideal performance and scalability.

Coffee and Lunch will be provided.

About the Instructor: Dr. Jonathan Bentz is a Solution Architect with NVIDIA, focusing on Higher Education and Reasearch customers.In this role he works as a technical resource to customers and OEMs to support and enable adoption of GPU computing. He delivers GPU training such as workshops to train users and help raise awareness of GPU computing. He also works with ISV and customer applications to assist in optimization for GPUs through the use of benchmarking and targeted code development efforts. Prior to NVIDIA, Jonathan worked for Cray as a software engineer where he developed and optimizaed high performance scientific libraries such as BLAS, LAPACK, and FFT specifically for the Cray platform. Jonathan obtained his PhD in physical chemistry and his MS in computer science from Iowa State University.
Important: Seating is limited to the first 60 people.

• Please bring your laptop to participate in hands-on exercises. A GPU in your laptop is not required. 
• No previous GPU programming experience is required. However, beginner-level C and Linux experience will be expected.
• In preparation, you may watch a few short (approx. 5 min) YouTube videos on introductory GPU programming topics or qwikLABS 
• CUDACasts can be found at: http://www.youtube.com/user/NVIDIADeveloper
• Various Labs here: https://nvidia.qwiklab.com/lab_catalogue?locale=en
• Also, please take time to register at our CUDA developer site: https://developer.nvidia.com/programs/cuda/register