Part I of this tutorial will provide an overview of parallel I/O systems and summarize the key techniques for obtaining high performance I/O on high-performance computing (HPC) resources at scale. Part II introduces the ADIOS library, delving through their usage models and examples, showing how to achieve high performance scalable I/O. Part III explains data compression. Part IV covers techniques for creating in situ analytics and teaches how to generate visualization services. Over one half of this tutorial will be hands-on sessions, where we provide access to the software and go through live examples using a Virtualbox VM. We will have users work with real examples including the heat equation, a brusselator example, and the Gray Scott Model of Reaction Diffusion.

Norbert Podhorszki is a senior scientist and team leader in the Scientific Data Group in the Computer Science and Mathematics Division at the Oak Ridge National Laboratory. He is one of the key developers of ADIOS that won an R&D 100 award in 2013. His main research interest is in creating I/O and staging solutions for in situ processing of data on leadership class computing systems. He received his Ph.D. in Information Technology from the Eötvös Loránd University of Budapest. He has worked in the field of logic programming, performance monitoring and analysis of message-passing programs, application monitoring in Grid environments and application development in Desktop Grids, scientific workflow technologies in supercomputing projects, and high performance I/O. He is author and co-author of more than 100 scientific papers and book chapters. He has given many tutorials on ADIOS, including several times at the Supercomputing and the International Supercomputing conferences.

1. Please download Julia from https://julialang.org/downloads/ and follow the installation instructions presented at https://julialang.org/downloads/platform.html
2. Install Julia packages that will be used throughout the course. In the command line (console) type julia and paste the following Julia code:
   

   using Pkg
   Pkg.add(“DataFrames”)
   Pkg.add(“PyCall”)
   Pkg.add(“Conda”)
   Pkg.add(“PyPlot”)
   Pkg.add(“Plots”)
   Pkg.add(“CSV”)
   Pkg.add(“LightGraphs”)
   Pkg.add(“IJulia”) # optional for participants wanting to use Jupyter Notebooks

3. A convenient programming environment for the Julia language is Atom plugin named Juno. In order to install Atom with Juno please follow the steps below:
   1. Download and install Atom (available at https://atom.io/).
   2. Start Atom and press Ctrl + , ( Ctrl key + comma key ) to open the Atom settings screen.
   3. Select the Install tab.
   4. In the Search packages field, type uber-juno and press Enter.
   5. You will see the uber-juno package developed by JunoLab—click Install to install the package.
4. Optionally, it is possible to use Julia within Jupyter notebook.
   In order to try Julia inside a Jupyter notebook start the Julia console and run the two following commands:

   using IJulia
   notebook(dir=pwd()) # a new web browser tab should open with Jupyter Notebook
   # showing current Julia working directory

Przemysław Szufel is an Assistant Professor in Decision Support and Analysis Unit at Warsaw School of Economics. He is a member of the Management Committee of European Social Simulation Association (ESSA). His current research focuses on methods for execution of large-scale simulations for numerical experiments and optimization. He is an author or a co-author of several Open Source tools for high performance and numerical simulation (such as KissCluster, D-MASON, Isislab SOF, SilverDecisions, PyCX), and actively participates in their development and a co-author of various algorithms for distributed simulation-optimization models (such as AKG, AOCBA).

1. Introduction to Scientific Visualization and Visual Analysis.
2. Visualization systems and paradigms
3. Generic data structures
4. Introduction to VisNow
5. Hands-on Session #1 – 2D data visualization
6. Hands-on Session #2 – 3D data visualization
7. Hands-on Session #3 – Vector data visualization
8. Hands-on Session #4 – Unstructured data visualization.