MONDAY, August 10

8:00 – 8:30

Registration, Coffee

8:30 – 8:45

Welcome

Mike Norman, SDSC Director

8:45 – 9:30

Introduction, Orientation

Bob Sinkovits, Director for Scientific Computing Applications, SDSC

9:30 – 10:15

How do I launch and manage jobs on the system?

Mahidhar Tatineni, User Services Manager, SDSC

10:15 – 10:45

Break

10:45 – 12:15

Launching and Managing Jobs

Mahidhar Tatineni, User Services Manager, SDSC

12:15 – 1:30

Lunch at Café Ventanas

1:30 – 3:00

How do I manage my data on the file system?

Amit Majumdar, Division Director, Data Enabled Scientific Computing

3:00 – 3:30

Break

3:30 – 5:00

How do I know I’m making effective use of the machine?

Bob Sinkovits, Interim Director for Scientific Computing Applications, SDSC

5:30 – 8:30

Reception at Wayne Pfeiffer’s home overlooking the Pacific, Sweater or jacket recommended

Shuttle provided from SDSC driveway

TUESDAY, August 11

8:00 – 8:30

Coffee

8:30 – 10:00

How do I automate my job pipeline to ensure reproducibility?

Ilkay Altintas, SDSC’s Chief Data Science Officer, Director, Workflows for Data Science (WorDS) Center of Excellence SDSC

10:00 – 10:15

Break

10:15 – 12:15

How do I manage my software?

Andrea Zonca, HPC Applications Support Specialist, SDSC

12:15 – 1:30

Lunch at Café Ventanas

1:30 – 3:30

How do I mine and get insight from my data?

Natasha Balac, Director, Predictive Analytics Center of Excellence (PACE), SDSC

Amit Chourasia, Senior Visualization Scientist, SDSC

3:30 – 3:45

Break

3:45 – 4:15

SDSC Data Center Tour

4:15  – 5:00

Hands-on practice continues with mentors available for questions

WEDNESDAY, August 12

PARALLEL SESSIONS

8:00 – 8:30

Coffee

Track 1

Auditorium

Track 2

Synthesis Center E-B143

Session 1

8:30 – 12:00

GPU Computing and Programming

Andreas Goetz, Co-Director, CUDA Teaching Center, Co-Principal Investigator, Intel Parallel Computing Center

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 problems. 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.

Predictive Analytics

Natasha Balac, Director, Predictive Analytics Center of Excellence (PACE), SDSC

This session is designed as an introduction for attendees seeking to extract meaningful predictive information from within massive volumes of data. The session will provide an introduction to the field of predictive analytics and a variety of data analysis tools to discover patterns and relationships in data that can contribute to building valid predictions.

12:00 – 1:30

Lunch at Café Ventanas

Session 2

1:30 – 5:00

Performance Optimization

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.

Spark for Scientific Computing

Andrea Zonca, HPC Applications Specialist

Mahidhar Tatineni, User Services Manager, SDSC

Apache Spark is a cluster computing framework extensively used in Industry to process large amount of data (up to 1PB) distributed across thousands of nodes. It has been designed as a successor of Hadoop focusing on performance and usability. It provides interface in Python, Scala and Java. This session will provide an overview of the capabilities of Spark and how they can be leveraged to solve problems in Scientific Computing. Next it will feature a hands-on introduction to Spark, from batch and interactive usage on Comet to running a sample map/reduce example in Python. The final part will be devoted to two key libraries in the Spark ecosystem: Spark SQL, a general purpose query engine that can interface to SQL databases or JSON files and Spark MLlib, a scalable Machine Learning library.

THURSDAY, August 13

PARALLEL SESSIONS

8:00 – 8:30

Coffee

Track 1

Auditorium

Track 2

Synthesis Center E-B143

Session 3

8:30 – 12:00

Python for HPC

Andrea Zonca, HPC Applications Specialist

Bob Sinkovits, Director for Scientific Computing Applications, SDSC

Python is rapidly becoming more widely adopted in the High Performance Computing world. 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 not required.

(1) IPython Notebook allows users to execute code on a single compute node or cluster and export the Python web interface to the local browser for interactive data exploration and visualization. IPython Notebook supports live Python code, explanatory text, LaTeX equations and plots in the same document.

(2) IPython Parallel provides a simple, flexible and scalable way of running thousands of Python serial jobs by spawning IPython kernels (namely engines) on any HPC batch scheduler. It also allows interactive control of the engines from an IPython Notebook session along with the ability to submit more Python tasks to the engines.

(3) Numba makes it possible to run pure Python code on GPUs simply by decorating functions with the data types of the input and output arguments. Pure Python prototype code can be gradually optimized by pushing the most computationally intensive functions to the GPU without the need to implement code in CUDA or OpenCL.

(4) PyTrilinos is a Python wrapper for the Trilinos, a C++ Distributed Linear Algebra library developed by Sandia National Labs. It provides a high level interface for transparently dealing with complex MPI point-to-point communication strategies for operations involving both dense and sparse matrices and vectors whose data are distributed across an arbitrary number of nodes.

Visualization

Amit Chourasia, Senior Visualization Scientist

Visualization is largely understood and used as an excellent communication tool by researchers. This narrow view often keeps scientists from fully using and developing their visualization skillset. This tutorial will provide a “from the ground up" understanding of visualization and its utility in error diagnostic and exploration of data for scientific insight. When used effectively visualization can provide a complementary and effective toolset for data analysis, which is one of the most challenging problems in computational domains. In this tutorial we plan to bridge these gaps by providing end users with fundamental visualization concepts, execution tools, customization and usage examples. Finally, a short introduction to SeedMe.org will be provided where users will learn how to share their visualization results ubiquitously.           

12:00 – 1:30

Lunch at Café Ventanas

Session 4

1:30 – 5:00

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.

Workflow Management

Ilkay Altintas, SDSC’s Chief Data Science Officer, Director, Workflows for Data Science (WorDS) Center of Excellence SDSC

This session will start with a crash course on workflow management basics. We will then explore common computing platforms including Sun Grid Engine, NSF XSEDE high performance computing resources, the Amazon Cloud and Hadoop with an emphasis on how workflow systems can help with rapid development of distributed and parallel applications on top of any combination of these platforms. We will then discuss how to track data flow and process executions within these workflows (i.e. provenance tracking) including the intermediate results as a way to make workflow results reproducible. We will end with a lab session on using Kepler to build, package and share workflows interacting with various computing systems.

5:30 – 9:00

Beach BBQ Dinner at La Jolla Shores Hotel, sweater or jacket recommended

8110 Camino Del Oro, La Jolla, CA 92037

Shuttle provided from SDSC driveway

FRIDAY, August 14

8:00 – 8:30

Coffee

8:30 – 9:30

Emerging Technologies in HPC

Shawn Strande, Deputy Director, SDSC

9:30 – 11:00

Lightning Rounds

11:00 – 11:30

Wrap up

11:30AM

Adjourn

Thank you for attending we hope you enjoyed the week!

(To-go box lunches will be available)