VSCSE - Virtual School of Computational Science and Engineering

2014 Summer School Courses

Data Intensive Summer School (June 30 - July 2, 2014)

The Data Intensive Summer School focuses on the skills needed to manage, process and gain insight from large amounts of data. It is targeted at researchers from the physical, biological, economic and social sciences that are beginning to drown in data. We will cover the nuts and bolts of data intensive computing, common tools and software, predictive analytics algorithms, data management and visualization. Given the short duration of the summer school, the emphasis will be on providing a solid foundation that the attendees can use as a starting point for advanced topics of particular relevance to their work.

Lecture 1: Dealing with Data: Choosing a Good Storage Technology for Your Application
Lecture 2: Big Data, What Does it Mean For Me?
Lecture 3: Workflow-Driven Science
Lecture 4: Globus Introduction
Lecture 5: Kepler Workflow System and Features
Lecture 6: Reproducible Science
Lecture 7: Distributed Computing In Kepler
Lecture 8: Benefit and Burden
Lecture 9: Predictive Analytics
Lecture 10: Predictive Analytics Tools
Lecture 11: Supervised Learning
Lecture 12: Strategies for Big Data
Lecture 13: R-R Studio
Lecture 14: Data Exploration

Slides Lecture 1: Dealing with Data: Choosing a Good Storage Technology for Your Application
Slides Lecture 2: Big Data, What Does it Mean For Me?
Slides Lecture 3: Workflow-Driven Science
Slides Lecture 4: Globus Introduction
Slides Lecture 5: Kepler Workflow System and Features
Slides Lecture 6: Reproducible Science
Slides Lecture 7: Distributed Computing In Kepler

Harness the Power of GPU's: Introduction to GPGPU Programming (June 16 - 20, 2014)

Harness the Power of GPUs, an Introduction to GPGPU Programming is a mixture of lectures and labs and introduces all levels of parallelism as well as common approaches for parallelization in order to achieve the following goals: Better utilization of the GPUs by enabling more scientists to use them, better understanding of the efficiency in the GPU utilization by the application developers and a higher job throughput by enabling more resources and shortening job runtimes. In addition, participants will understand and avoid the common pitfalls of parallel computing, learn CUDA and OpenACC, understand the basic principles of data parallel computing, tap into enormous computing power, even on a laptop, and speed up research.

Lecture 1: Introduction
Lecture 2: Kernels, Threads, Blocks and Grids
Lecture 3: GPU Architecture
Lecture 4: GPU Memory Hierarchies and Management
Lecture 5: Shared Memory
Lecture 6: Streams and Dynamic Parallelism
Lecture 8: Advanced Open ACC
Lecture 9: Parallelization Techniques and Optimization

Lab 1: Introduction
Lab 2: Kernels, Threads, Blocks and Grids
Lab 3: GPU Architecture
Lab 4: Matrix-Matrix Multiplication
Lab 5: Advanced Matrix-Matrix Multiplication
Lab 6: Quicksort
Lab 7: First Open ACC Examples
Lab 8: Using Data Regions
Lab 9: Body Simulation

Science Visualization Course (August 25-26, 2014)

This two-day in-person training covers all aspects of visualizing data from a broad variety of domains. The training kicks off with an introduction to visualization followed by best practices when dealing with diverse data (abstract and spatial), demonstrating a variety of methods and techniques on those data sets and demonstrating a range of freely available software. Real world problems for which visualization is needed will be demonstrated and attendees will be taken through the process of visualizing this data and gaining insight.

Lecture 1: Introduction to Data Visualization
Lecture 2: Conceptual Scientific Visualization
Lecture 3: Introduction to Informational Visualization
Lecture 4: Introduction to Scientific Visualization
Lecture 5: Scientific Visualization with Paraview
Lecture 6: Scientific Visualization with VisIt
Lecture 7: Scripting with Paraview and VisIt
Lecture 8: Parallel Visualization