Training Materials Repository

NCSA, Research IT, and the Illinois Campus Cluster Program will offer this 2-hours workshop with hands-on exercises. We will explore how to build new parallel applications and transform serial applications into parallel ones incrementally in a shared memory environment. Exercises will use OpenMP, a standardized API for parallelizing Fortran, C, and C++ programs on shared-memory architectures. After completing this training, attendees will be prepared for more advanced or different parallel computing tools and techniques.

OpenACC is the accepted standard using compiler directives to allow quick development of GPU-capable codes using standard languages and compilers. It has been used with great success to accelerate real applications within very short development periods. This workshop assumes knowledge of either C or Fortran programming. It will have a hands-on component using the Bridges computing platform at PSC.

The NCSA Delta team and NVIDIA are hosting a workshop that will give you hands-on experience accelerating Python codes with NVIDIA GPUs. We will utilize code samples in three main categories to introduce you to Python GPU accelerated computing: drop-in replacements for SciPy and NumPy code through the CuPy library, GPU acceleration for end-to-end data science workloads using NVIDIA RAPIDS, and custom accelerated code without leaving the Python language using Numba.

NCSA and Research IT will offer the Illinois Campus Cluster: Basics of Access and Usage workshop next April 6th at 11 AM CT. The content is suitable for researchers and students who already have experience working with clusters and want to know more about the ICC and those who have little to no experience working with clusters and want to learn how to use one.

NCSA, Research IT, and the Illinois Campus Cluster Program will offer this 2-hours workshop with hands-on exercises. We will explore how to build new parallel applications and transform serial applications into parallel ones incrementally in a shared memory environment. Exercises will use OpenMP, a standardized API for parallelizing Fortran, C, and C++ programs on shared-memory architectures. After completing this training, attendees will be prepared for more advanced or different parallel computing tools and techniques.

This is a 2-hour workshop where participants will work on exercises to parallelize model scientific applications using OpenMP, a shared-memory application programming interface. It will build on the previous Introduction to Parallel Computing on High-Performance Systems workshop that explored concepts and tools such as parallel loop scheduling, explicit data declarations, reduction clauses, and OpenMPc, dynamic, guided) and tune your loop parallelization for optimal performance

• Explicitly declare data contexts (private, shared) to avoid race conditions and improve the quality of your code, facilitating collaborations
• Use reduction clauses (additions, maximum and minimum values) to calculate properties without thread concurrency
• Use OpenMP library functions to span teams of threads, mix data and task parallelism, insert barriers, and much more

Pre-requisites:

• Basic C/C++ or Fortran programming skills
• Basic parallel programming knowledge
• Basic Linux skills (e.g., compiling code, navigating the file system)
• Familiarity with a remote Linux server text editor: vi, nano, or emacs

NCSA will host the in-person Shared Memory Programming Using OpenMP workshop offered by the Pittsburgh Supercomputing Center (PSC). The workshop is intended to give C and Fortran programmers a hands-on introduction to OpenMP programming. Attendees will leave with a working knowledge of how to write scalable codes using OpenMP. 

The NCSA Delta team and NVIDIA are hosting a workshop that will give you hands-on experience accelerating Python codes with NVIDIA GPUs. We will utilize code samples in three main categories to introduce you to Python GPU accelerated computing: drop-in replacements for SciPy and NumPy code through the CuPy library, GPU acceleration for end-to-end data science workloads using NVIDIA RAPIDS, and custom accelerated code without leaving the Python language using Numba.

Learn how to use the Message Passing Interface (MPI), the standard framework for parallel computing in distributed-memory systems, to parallelize your scientific applications. You will learn the basic concepts of message passing, including domain decomposition, collective communications, and several MPI library functions. A hands-on exercise parallelizing a machine learning model application will be used so participants can practice these concepts. Follow-up sessions will be offered to further help participants with the exercise.

You will learn:

• The message-passing interface paradigm
• Core components of an MPI message: body, envelope
• MPI processes and communicators
• Collective communications: broadcast messages and reductions

Pre-requisites:

• Basic C/C++ or Fortran programming skills
• Basic knowledge of parallel computing concepts
• Familiarity with a remote Linux server text editor: vi, nano, or emacs

NCSA was a satellite site for this PSC/ACCESS training event on Machine Learning and BIG DATA. This workshop focused on big data analytics and machine learning with Spark and deep learning using TensorFlow.