Pacific time

Main Room Session

Breakout Room Session

8:00 AM – 8:30 AM

Check-in & Light Breakfast

8:30 AM – 10:00 AM

4.1a Intro to Git & GitHub
Elham Khoda, Computational and Data Science Research Specialist
This session will provide an overview of GitHub and an introduce version control with Git/GitHub for beginners. Participants will learn to create a repository on Github and manage files, use pull requests, merge changes, rebase branches, etc.

4.1b Advanced Git & GitHub
Fernando Garzon, Computational and Data Science Research Specialist

In today's fast-paced software development world, mastering GitHub and Git is a game-changer. This session will enhance your understanding beyond the basics, introducing advanced techniques to streamline workflows, manage complex projects, and automate tasks. You'll discover how to maximize productivity without compromising quality. By the end of this talk, you'll have a deeper grasp of GitHub's potential and a curiosity to explore tools like GitHub Actions, documentation, and automation features. Join us to elevate your development experience and skills.

10:00 AM – 10:15 AM

Break

10:15 AM – 12:30 PM

4.2a Python for HPC
Andrea Zonca, Senior Computational Scientist

In this session we will introduce four key technologies in the Python ecosystem that provide significant benefits for scientific applications run in supercomputing environments. Previous Python experience is recommended but not required.
(1) First, we will learn how to speed up Python code compiling it on-the-fly with numba (2) Then we will introduce the threads, processes and the Global Interpreter lock and we will leverage first numba then dask to use all available cores on a machine (3) Finally we will distribute computations across multiple nodes launching dask workers on a separate Expanse job.

4.2b Information Visualization Concepts
Isaac Nealey, Data Visualization Research Specialist 
This session will provide an introductory understanding of information visualization concepts and how they can be leveraged to select and use effective visual idioms for different data types such as tabular, geospatial, and graph data. We will discuss and critique some example visualization designs, and cover practical rules of thumbs for canonical information visualization as well as address the challenges facing practitioners of scientific data visualization.

2:00 PM – 4:30 PM

4.3a Conducting Scientific Visualization with VTK and Unreal Engine 5
Isaac Nealey, Data Visualization Research Specialist 
This tutorial will provide a high-level overview of scientific visualization techniques with two open-source software suites. We will primarily cover structured mesh-based data, but we will address other commonly encountered datasets such as point clouds and geographic data as well. Attendees will employ a state-of-the-art game engine to render 3D scenes containing scientific data. This is a hands-on workshop that will require an adequate computer and the installation of large software packages.

4.3b Scalable Machine Learning 
Mai Nguyen, Lead for Data Analytics
Paul Rodriguez, Research Analyst
Machine learning is an integral part of knowledge discovery in a wide variety of applications. From scientific domains to social media analytics, the data that needs to be analyzed has become massive and complex.

This session introduces approaches that can be used to perform machine learning at scale. Tools and procedures for executing machine learning techniques on HPC will be presented.  Spark will also be covered for scalable data analytics and machine learning. Please note: Knowledge of fundamental machine learning algorithms and techniques is required.

Pacific time

Main Room Session

Breakout Room Session

8:00 AM – 8:30 AM

Check-in & Light Breakfast

5.1 Scaling up Interactive Data Analysis in Jupyter Lab: From Laptop to HPC
Peter Rose, Director of Structural Bioinformatics Laboratory
In this session we will demonstrate scaling up data analysis to larger than memory (out-of-core) datasets and processing them in parallel on CPU and GPU nodes. In the hands-on exercise we will compare Pandas, Dask, Spark, cuDF, and Dask-cuDF dataframe libraries for handling large datasets. We also cover setting up reproducible and transferable software environments for data analysis.

9:45 AM – 12:15 PM

5.2a Performance Tuning
Bob Sinkovits, Director of Education and Training 
This session is targeted at attendees who both do their own code development and need their calculations to finish as quickly as possible. We will cover the effective use of cache, loop-level optimizations, force reductions, optimizing compilers and their limitations, short-circuiting, time-space tradeoffs and more. Exercises will be done mostly in C, but emphasis will be on general techniques that can be applied in any language.

5.2b Deep Learning - Part 1
Mai Nguyen, Lead for Data Analytics
Paul Rodriguez, Computational Data Scientist
Deep learning, a subfield of machine learning, has

seen tremendous growth and success in the past

few years. Deep learning approaches have achieved

state-of-the-art performance across many domains,

including image classification, speech recognition, and

biomedical applications. This session provides an

introduction to neural networks and deep learning concepts

and approaches. Examples utilizing deep learning

will be presented, and hands-on exercises will be

covered using Keras.  Please note: Knowledge of

fundamental machine learning concepts and techniques

is required. 

1:45 PM – 4:30 PM

5.3a GPU Computing and Programming
Andreas Goetz, Research Scientist and Principal Investigator

This session introduces massively parallel computing with graphics processing units (GPUs). The use of GPUs is popular across all scientific domains since GPUs can significantly accelerate time to solution for many computational tasks. Participants will be introduced to essential background of the GPU chip architecture and will learn how to program GPUs via the use of libraries, OpenACC compiler directives, and CUDA programming. The session will incorporate hands-on exercises for participants to acquire the basic skills to use and develop GPU aware applications.

5.3b Deep Learning – Part 2
Mai Nguyen, Lead for Data Analytics
Paul Rodriguez, Computational Data Scientist
This session continues and extends Deep Learning -

Part 1 by going into more advanced examples.  Concepts

regarding architecture, layers, and applications will

be presented.  Additionally, more advanced tutorials

and hands-on exercises with larger deep convolutional

networks and transfer learning will be executed on

GPUs.  There will also be a chance to learn Keras more

in depth and become familiar with building more flexible models.

Q&A + Wrap-up

Pacific time

Main Room Session

8:00 AM – 8:30 AM

Check-in & Light Breakfast

8:30 AM – 11:30 AM

6.1a Parallel Computing using MPI & Open MP 
Mahidhar Tatineni, Director of User Services
This session is targeted at attendees who are looking for a hands-on introduction to parallel computing using MPI and Open MP programming. The session will start with an introduction and basic information for getting started with MPI. An overview of the common MPI routines that are useful for beginner MPI programmers, including MPI environment set up, point-to-point communications, and collective communications routines will be provided. Simple examples illustrating distributed memory computing, with the use of common MPI routines, will be covered. The OpenMP section will provide an overview of constructs and directives for specifying parallel regions, work sharing, synchronization and data scope. Simple examples will be used to illustrate the use of OpenMP shared-memory programming model, and important run time environment variables Hands on exercises for both MPI and OpenMP will be done in C and FORTRAN. 
 

6.1b Knowledge Management and Knowledge Graph
Subhasis Dasgupta, Computational and Data Researcher
Jon Stephen, Computational and Data Researcher

In this session, we have three connected sections. The first section will help participants understand knowledge management and how to implement it, specifically within the scientific community. It will also highlight the fundamental shift in the machine learning paradigm and how to incorporate knowledge management into daily processes. This section will cover the basic concepts of knowledge management, from ontology development to document management. In the next part, we will cover two fundamental knowledge management techniques that can help users design their knowledge pipelines and improve their daily processes. The first technique focuses on how to use large language models (LLMs) beyond traditional engineering methods and other related technologies.

Finally, we are going to delve into the fascinating topic of knowledge graph technology. In this section, we'll explore how to effectively build and use knowledge graphs to manage knowledge pipelines. We'll also discuss how to use LLMs to construct and navigate knowledge management systems.

11:30 AM – 11:45 AM

6.2 Overview of Voyager 
Amit Majumdar, Division Director of Data-Enabled Scientific Computing
Voyager provides an innovative system architecture uniquely optimized for deep learning operations using well-established frameworks such as PyTorch and TensorFlow. Voyager comprises 42 training nodes of Supermicro X12 Habana Gaudi Training Servers; each training node contains 8 GAUDI HL-205 training processor cards which have 100 GbE non-blocking, all-to-all connections among the 8 cards within a node; the 42 Training nodes are connected via a high-performance, low latency 400 GbE switch interconnect. Voyager’s architecture has already shown highly scalable AI application performance in various areas such as LLMs (with billions of parameters such as for GPT2-XL and GPT3-XL), convolutional neural network-based image processing, and graph neural network based high-energy particle physics. 

11:45 AM - 12:00 PM

6.3 Overview of COSMOS

Mahidhar Tatineni, Director of User Services

12:15 PM – 12:30 PM

Closing Remarks

Robert Sinkovits, Director of Education and Training

Lunch boxes will be provided*