Sep 28, 2015 · 8:30 a.m.– 5:00 p.m. · Rm. 130, 701 Carnegie Center
Netapp will provide 3 days training on their storage appliance. This will be an all-day event for those three days. The training will take place in 701 Carnegie Center, Room 130. Windows should already be there. Macs are welcomed. Linux simply doesn't work with the Java based application.
There are two components to the training:
- 3-day admin
- 2-day performance
These will be condensed into 3 days.There are seats for 16 people.
NOTE: Open only to internal Princeton HPC technical staff/system administrators.
Detailed Course Outline - Clustered ONTAP v8.3 Outline
Module 1: Exploring Data ONTAP Storage Fundamentals
• Overview of clustered Data ONTAP and Data ONTAP 8.3 enhancements
• Scaling Methods
• Administrative User Interfaces
Module 2: Hardware and Initial Setup
• FAS Hardware
• Setting up the Cluster
Module 4: Storage Management
• Data ONTAP Storage Architecture
• Data ONTAP File System
• Virtual Storage Tier
• Data ONTAP Physical Storage Configuration
• Data ONTAP FlexVol Configuration
Module 5: Network Management
• Network Ports
• Network Interfaces
• Non-disruptive LIF Configuration
• Network Management
• Network Load Balancing
Module 6: Implementing NAS Protocols
• File System Structure
• Deploying NFS
• Windows File Services
Module 9: Managing Storage Space
• Thin Provisioning
• Deduplication and Compression
• FlexClone Volumes
• Volume Moves in Clustered Data ONTAP
• Growing Aggregates
• Automatic Space Management
Module 10: Data Protection
• Storage Failover Management
• Netapp Data Protection Interfaces
Module 13: Bonus Material (Optional)
• Infinite Volumes
• Engaging NetApp Support
• OnCommand Insight Walkthrough
• Physical Storage Maintenance
Detailed Course Outline – Performance Analysis of Clustered ONTAP
Module 1: How a NetApp Storage System Works
• Describe the layers within the Data ONTAP architecture
• List the advantages that are provided by the ability of WAFL to optimize writes
• Explain the purpose of NVRAM
• Diagram the flow of read and write requests through the network and protocol layers of Data ONTAP
• Describe the benefits that RAID provides
Module 2: Performance Overview*
• Define performance-related terms, such as “baseline,” “bottleneck,” “Little’s law,” and “latency”
• Describe baseline performance guidelines and methodologies as they relate to NetApp storage systems
Module 3: Clustered Storage System Workloads and Bottlenecks
• Gather information about the workload of an existing storage system
• Identify the storage system components that can affect performance—become bottlenecks
Module 4: Cluster Performance Monitoring and Analysis
• Describe the performance analysis tools and commands that are commonly used for cluster health checks
• Identify the key performance commands and describe the command output that they produce
• Explain how to use NetApp tools for performance measurement
• Describe the benefits of using the AutoSupport support tool for performance analysis
Module 5: OnCommand Management Tools
• List the three categories of performance tools
• Explain the features and functions of Insight Perform
• Explain the features and functions of OnCommand Balance
• Use OnCommand management tools to view performance data
Module 6: Storage QoS
• Discuss how the Storage Quality of Service (QoS) feature works in a clustered Data ONTAP environment
• Identify the commands that are used to manage policy groups
• Monitor workload performance
Oct 1, 2015 · 2:00 p.m.– 4:00 p.m. ·
This free mini-course is an introduction to Python for those with little or no programming experience.
Python is a programming language used for a wide variety of applications including scientific computation, text processing, file handling, graphics, database, and web interfaces. It is designed to be elegant, concise, and easy to learn, while offering many advanced features. This course will introduce you to Python programming, and to the resources you need to start learning and using Python. Participants will use the free Anaconda Python distribution on their own laptops. The course will include in-class exercises so participants can begin to experience Python for themselves.
Matthew Cahn is a programmer and Linux system administrator in the Department of Molecular Biology. He has been programming in Python for over 15 years in the fields of scientific instrumentation, drug discovery, and molecular biology.
Oct 2, 2015 · 9:00 p.m.– 5:00 p.m. ·
Python is a free, open-source, general-purpose programming language that is widely used in all fields of scientific computing. This six-hour workshop will be broken into three sections, each finishing in a set of hands-on exercises. The first section’s objective is to provide a fast refresher on Python syntax, variables and control structures. We'll then move onto demonstrating features from Python’s scientific computing stack : Numpy, Scipy, Matplotlib, and Pandas. To finish, we will run through demos of a number of other packages for model-fitting, MCMC, machine learning, and image processing.
Quentin Caudron is a postdoctoral researcher in the Department of Ecology and Evolutionary Biology, with a background in physics and computer science. Quentin's research interests include mathematical modelling, time se-ries analysis, image processing, statistical inference, and, recently, embedded computing. Quentin uses Python on a daily basis for the vast majority of his computational work.
Oct 21, 2015 · 2:00 p.m.– 3:30 p.m. · 130 Lewis Library, New Media Center
Python is a popular programming language for analyzing numerical and textual data.The mini-course will present elements and features of Python and how it can be used within a workflow. The course includes hands-on exercises so participants can become familiar with programming techniques. We will use the
workstations in the New Media Center. Emphasis will be on getting started with Python and understanding its fundamentals so attendees can continue on
their own. The course is intended for researchers working with data generated by simulations, acquired from experiments, or collected from other sources.
Experience in another programming language is recommended background for the session.
Eliot Feibush is a Computational Scientist in the Computational Plasma Physics Group at the Princeton Plasma Physics Laboratory. He specializes in developing scientific visualizations and graphics software.
He has written many python programs to select, analyze, convert, and display data from various applications and disciplines. Prior to PPPL, he has worked in medical imaging, architectural design, and geo-spatial analysis.
Nov 2, 2015 · 10:00 p.m.– 4:00 p.m. ·
This two-day workshop is intended to provide a practical introduction to the broad topic of parallel computing for scientific/numerical codes. Both MPI and OpenMP, two of the most popular tools for compiled-language parallel programming will be covered. The primary focus of the workshop will be features and topics most useful for beginning parallel programmers.
The first day will cover MPI, including environment management, point-to-point communication, and collective communication routines. The second day will cover OpenMP constructs for specifying parallel regions, work sharing, synchronization, and environment management. Covered throughout the workshop will be general strategies for writing and scaling parallel code as well as tips for optimizing and debugging. Examples will be provided in C and C++ and multiple lab exercises will allow participants to hands-on experience designing, writing, compiling, and executing parallel code on a Princeton Research Computing High Performance Computing (HPC) Cluster.
Level/Prerequisites: This workshop assumes no prior experience with parallel programming but does require some familiarity with C/C++ and basic programming concepts. Some experience working in a Linux command line environment is strongly encouraged but not required. Registrants must have a Princeton University netID, as this is required to gain access to the HPC cluster. Participants should bring their own computers with an ssh client already installed.
Because of hands-on nature of this workshop please bring:
1. A laptop with a wireless connection and an ssh client already installed. Linux and mac should already have one. Windows users will need a client, e.g. putty, cygwin, etc.
2. An account on adroit. If you do not already have one you can register here: https://www.princeton.edu/researchcomputing/computational-hardware/adroit/registration. In the “specify reason for use” write “Intro to Parallel Computing Workshop”. This is not automated so please do not wait until the last minute to register!
Ian Cosden is a Research Computing Software & Programming Analyst at PICSciE where he works with researchers to help build, develop, and optimize serial/parallel scientific codes. He has a Ph.D. in Mechanical Engineering from the University of Pennsylvania where he developed the first highly-parallel hybrid atomistic-continuum model for liquid-vapor phase change.
Dec 2, 2015 · 2:00 p.m.– 4:00 p.m. · 130 Lewis Library, New Media Center
This free mini-course will introduce participants to scientific visualization techniques in the VisIt software package.
Scientific visualization combines computer graphics with data generated by simulations or acquired from experiments. It enables insight, verification, and is very valuable for presentations and publications. VisIt has a graphical user interface for exploring and displaying data. It also connects to a rendering engine that can run in parallel to handle large amounts of data. In addition, VisIt can produce animation to represent complex behavior of variables over time. The software is freely available for Mac, Windows, and Linux platforms and is installed on the Princeton University Research Computing System and in the PICSciE Visualization Laboratory. We will also describe the remote visualization capability within the Research Computing environment.
The mini-course assumes no prior experience with visualization tools. It is intended for researchers interested in developing visual representations of their data. The session features hands-on lab exercises in the New Media Center.
Eliot Feibush is a Visualization Scientist. His role is to facilitate visualization within the computational community and integrate visualization into the scientific workflow. He has written many python programs to select, analyze, convert, and display data from various applications and disciplines. Prior to Princeton, he has worked in medical imaging, architectural design, and geospatial analysis