This year ORCD will be offering three IAP classes: Introduction to Parallel Programming, Practical High Performance Computing, and Practical Programming with Data.
For more information and how to sign up visit our IAP web page.
We look forward to seeing you there!
Introduction to Parallel Programming
Parallel computing has been an important research topic in science and technology for decades. Thanks to the fast-developing field of deep learning in recent years, parallel computing is being used for more broad interests. In this class, concepts of parallel computing will be introduced. Attendees will learn not only the basics of high-performance computing (HPC) clusters and GPU accelerators but also programming skills with OpenMP, MPI, CUDA, Pytorch, and Deepspeed. Examples and hands-on exercises will be provided in several programming languages including C, Fortran, and Python. These parallel programming skill sets are useful for researchers to accelerate their computer programs and helpful for students to be prepared for a career in information technology.
Practical High Performance Computing
As part of the Practical Computational Thinking IAP course series, the focus of this workshop is to introduce the role of High Performance Computing (HPC, aka supercomputing) in research. We will discuss the fields where HPC is used and provide concrete examples where we describe the strategies used to scale applications to hundreds of processors. Students will learn when to scale from their laptops to HPC, what challenges that introduces, and how to address those challenges with efficient HPC workflows. Engaging will be used for hands-on examples using C/C++, Julia, Matlab, and/or Python. We will also demonstrate applications using other computing resources on campus, such as the Satori and SuperCloud clusters. Students should bring an existing research problem/application that they would like to scale as a project.
Practical Programming with Data
AI this, AI that - but how can you use AI to manage your own data as a researcher? Join us this IAP, learn how to set up a RAG system and build a chat interface over your data! From how to structure your experimental results for efficient access, to how to preprocess academic papers for retrieval by LLMs, we will demystify all the steps along the way. Introductory (very basic, really) python knowledge assumed. This class is part of the Practical Computational Thinking IAP series, taught by students from the Data Systems Group at CSAIL and ORCD.