[LA-SiGMA] Announcement: 5th Annual LONI Parallel Programming Workshop and NVidia GPU Workshop

[Feng Chen]

Dear All,

The LSU Information Technology Services, LSU Center for Computation & Technology, Louisiana Optical Network Initiative (LONI) are pleased to announce the following two workshops:


  *   5th Annual LONI Parallel Programming Workshop from May 30 - 31, 2016.
  *   NVidia GPU Workshop from June 1 - 2, 2016.

Sponsors

Special thanks to the following for their generous support of the LONI Programming Workshop.


  *   Louisiana Optical Network Initiative (LONI)
  *   Information Technology Services, Louisiana State University
  *   Center for Computation & Technology, Louisiana State University

REGISTER NOW if you plan on attending the following events:

Note:


  *   The two workshops need to be registered *separately*
  *   The LONI programming workshop will  be broadcasted through WebEx for remote attendants.
  *   Coffee and lunch will be provided for in-person attendees.

*5th Annual LONI Parallel Programming Workshop
Date: May 30 - 31, 2016
Time: 8:30 AM - 5:00 PM
Venue: BEC, ROOM 1700 (tentative)
Website: http://www.hpc.lsu.edu/training/workshop.php
Registration Fee: None
Registration opens: March 16, 2016
Registration deadline: May 23, 2016

Description:
This workshop will first introduce users to the concepts of Parallel Programming, then cover how those concepts apply to solving problems frequently encountered in numerical studies such as matrix-matrix multiplication, using the Message Passing Interface (MPI) parallel programming technique. This workshop will be hands-on oriented, so the attendants should have prior programming experience in C/C++ and/or Fortran.

Schedule (Note: This tentative Schedule is subject to change.)

June 1: Parallel Programming Concepts and Introduction to MPI, Part 1
June 2: Introduction to MPI, Part 2 and Understanding MPI Applications

*NVidia GPU Workshop
Date: June 1 - 2, 2016
Time: 8:30 AM - 4:30 PM
Venue: BEC, ROOM 1700 (tentative)
Website: http://www.hpc.lsu.edu/training/workshop.php
Registration Fee: None
Registration opens: March 16, 2016
Registration deadline: May 31, 2015

Description:
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.

Schedule (Note: This tentative Schedule is subject to change.):

Agenda:  June 1st | Day 1
Registration: 8:30AM - 9:00AM
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
* 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
* Conclude with a Deep Learning Overview
Intro to Deep Learning / Machine Learning
* Overview of Global and Shared memory usage (Caffe, Torch, Theano)
* Deep Learning with GPUs and NVIDIA DIGITS<https://developer.nvidia.com/digits>
* Live demo using NVIDIA DIGITS
Caffe Lab Examples (time permitting)
Deep learning<https://developer.nvidia.com/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.

Please distribute to faculty, staff and students that might be interested.

Thanks,

Feng Chen, PhD
IT Consultant
High Performance Computing
Louisiana State University
329 Frey Computing Services Center, Baton Rouge, LA  70803
office 225-578-2924
fchen14 at lsu.edu<mailto:fchen14 at lsu.edu>





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