SDSC Summer Institute Agenda
Agenda is subject to change. Times listed below are in PDT.
- Lesson material repository: https://github.com/sdsc/sdsc-summer-institute-2020
WEDNESDAY, July 22nd- Preparation Day
MONDAY, August 3rd
| Time | |
|---|---|
| 8:00 - 9:00 AM | 1.1. Welcome, Orientation, & Introductions (Main Room) Bob Sinkovits, Director of Scientific Computing, SDSC & Director of the Summer Institute |
| 9:00 - 10:00 AM | 1.2. Accessing and Running Jobs on Comet (Main Room) Mary Thomas, Computational Data Scientist, SDSC This session covers the basics of accessing Comet; managing the user environment; and compiling and running jobs. It is assumed that you have completed the basic steps of logging onto Comet and testing your Unix skills prior to the event. |
| 10:00 - 10:30 AM | 1.3. Comet & Expanse User Portal (Main Room) Subhashini Sivagnanam, Senior Computational and Data Science Specialist, SDSC |
| 10:30- 10:45 AM | Break |
| 10:45 - 12:45 PM | Parallel Sessions: 1.4a. Introduction to version control with git and GitHub (Main Room) Martin Kandes, Computational & Data Science Research Specialist, SDSC Introduction to git for beginners, create a repository on Github1.4b. Advanced Github (Breakout Room) Andrea Zonca, Senior Computational Scientist, SDSC You should be already familiar with creating Pull Requests, merging and rebasing branches |
| 12:45 - 1:15 PM | Lunch/Break |
| 1:15 - 2:00 PM | 1.5. Understanding Performance and Obtaining Hardware Information (Main Room) Bob Sinkovits, Director of Scientific Computing, SDSC & Director of the Summer Institute |
TUESDAY, August 4th
| Time | Track 1 (Main) | Track 2 (Breakout Room) |
|---|---|---|
| AM Session 8 AM- 10:45 AM 15 min- Break will be based on instructor |
2.1a. Python for HPC Andrea Zonca, Senior Computational Scientist, SDSC 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 Comet job. |
2.1b. A Short Introduction to Data Science and its Applications Subhasis Dasgupta, Computational and Data Researcher, SDSC Shweta Purawat, Computational and Data Researcher, SDSC Ilya Zaslavsky, Dr. Spatial Information Systems Lab, SDSC The new era of data science is here. Our lives as well as any field of science, engineering, business and society are continuously transformed by our ability to collect meaningful data in a systematic fashion and turn that into value. These needs not only push for new and innovative capabilities in composable data management and analytical methods that can scale in an anytime anywhere fashion, but also require methods to bridge the gap between applications and compose such capabilities within solution architectures. In this short overview, we will show you a plethora of applications that are enabled by data science techniques and describe the process and cyberinfrastructure used within these projects to solve questions. We will also overview SDSC’s AWESOME and SUAVE platforms for data management and visualization. Particularly, in this session, you will learn about: (1)the basis process of data science, (2) reference solution architectures for computational data science, (3) the data management challenges when deal with steaming data, (4) how social media datasets can be used within scienftific studies, (5) graph analysis at scale, (6) effectively applying basic statistical analysis and machine learning methods to survey data, (7) examples for visualization of datasets through various end user platforms |
| 10:45 – 11:15 AM | Lunch/ Break | |
| PM Session 11:15 - 2:00 PM 15 min- Break will be based on instructor |
2.2a. Performance Tuning Bob Sinkovits, Director for Scientific Computing Applications, SDSC This session is targeted at attendees who both do their own code development and need their calculations to finish as quickly as possible. We'll 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. |
2.2b. Information Visualization Amit Chourasia, Senior Visualizaton Scientist, SDSC This tutorial will provide a ground up understanding of information visualization concepts and how they can be leveraged to select and use effective visual idioms for different data types such spreadsheet data, geospatial, graph, etc.). Attendees will go through a set of hands on exercises to create designs, decode and fix problems in existing visualization. Practical guidelines for visualization will be discussed as well. |
WEDNESDAY, August 5th
| Time | Track 1 (Main) | Track 2 (Breakout Room) |
|---|---|---|
| AM Session 8 AM- 10:45 AM 15 min- Break will be based on instructor |
3.1a. Scientific Visualization with Visit Amit Chourasia, Senior Visualization Scientist, SDSC This tutorial will provide a high level overview of scientific visualization techniques and their applicability for structured mesh based data (such as rectilinear grids). Attendees will follow along exercises in a hands-on manner to employ different types of techniques using VisIt software and also perform remote visualization on Comet. |
3.1b. Machine Learning Overview Mai Nguyen, Lead for Data Analytics, SDSC Paul Rodriguez, Research Analyst, SDSC SDSC Machine learning is an interdisciplinary field focused on the study and construction of computer systems that can learn from data without being explicitly programmed. Machine learning techniques can be used to uncover patterns in your data and gain insights into your problem. This session provides an overview of the fundamental machine learning algorithms and techniques used to explore, analyze, and leverage data to construct data-driven solutions applicable to any domain. Topics covered include the machine learning process, data exploration, data preparation, classification, and cluster analysis. Concepts and algorithms will be introduced, followed by exercises to allow hands-on experience using R and RStudio. |
| 10:45 – 11:15 AM | Lunch/ Break | |
| PM Session 11:15 - 2:00 PM 15 min- Break will be based on instructor |
Group photo 3.2. Lightning Rounds |
THURSDAY, August 6th
| Time | Track 1 (Main) | Track 2 (Breakout Room) |
|---|---|---|
| AM Session 8 AM- 10:45 AM 15 min- Break will be based on instructor |
4.1a. GPU Computing and Programming Andreas Goetz, Research Scientist and Principal Investigator, SDSC This session provides an introduction to massively parallel computing with graphics processing units (GPUs). The use of GPUs is becoming increasingly 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 skills to use and develop GPU aware applications. |
4.1b. Scalable Machine Learning Mai Nguyen, Lead for Data Analytics, SDSC Paul Rodriguez, Research Analyst, SDSC 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 provides an introduction to 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. In particular, we will use Spark’s machine learning library, MLlib, to demonstrate how distributed computing can be used to provide scalable machine learning. Please note: Knowledge of fundamental machine learning algorithms and techniques is required. (See description for Machine Learning Overview.) |
| 10:45 – 11:15 AM | Lunch/ Break | |
| PM Session 11:15 - 2:00 PM 15 min- Break will be based on instructor |
4.2a. Parallel Computing using MPI & Open MP Mahidhar Tatineni, User Services Manager, SDSC 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. |
4.2b. Deep Learning Mai Nguyen, Lead for Data Analytics, SDSC Paul Rodriguez, Research Analyst, SDSC 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. Deep learning makes use of models that are composed of many layers of interconnected processing units. The many layers allow for a deep network to learn representations of data at multiple and increasingly complex and task-specific levels of abstraction, leading to automatic feature learning and excellent prediction performance. This session provides an introduction to deep learning concepts and approaches. Case studies 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. |
FRIDAY, August 7th
| Time | Main Room |
|---|---|
| 8:30 - 9:00 AM | 5.1. An Introduction to Singularity: Containers for Scientific and High-Performance Computing Martin Kandes, Computational & Data Science Research Specialist, SDSC |
| 9:00 - 9:30 AM | 5.2. SeedMeLab Amit Chourasia, Senior Visualization Scientist, SDSC |
| 9:30 - 10:00 AM | 5.3 Jupyter Notebooks, Reverse Proxy Mary Thomas, Computational Scientist, SDSC |
| 10:00 - 10:15 AM | Break |
| 10:15 - 11:45 AM | Introduction to new projects: - 5.4. Voyager, Amit Majumdar, Director for Data Enabled Scientific Computing (DESC), SDSC - 5.5. Cloud Bank, Shava Smallen, Research Programmer, SDSC - 5.6. Expanse, Shawn Strande, Deputy Director, SDSC |
| 11:45 - 12:00 PM | Adjourn- Wrap-up, Thank you for joining us! Bob Sinkovits, Director of Scientific Computing, SDSC & Director of the Summer Institute |