ORCD Classes

The ORCD team regularly offers classes on subjects such as high-performance computing, parallel programming, and using the Engaging cluster.

  • Course Description

    The MIT Office of Research Computing's Engaging Cluster is available to the MIT community for running computational workloads that don't run well on your own computer. This hands-on tutorial walks you through the basics of using Engaging for your research. We will cover:

    • How to access Engaging
    • Transferring files
    • Using and installing software
    • Running jobs, including batch and interactive jobs requesting a variety of resources

    Prerequisites/Requirements

    • Attendees must have an Engaging account (instructions here).
    • We ask that you read through our Getting Started Tutorial to familiarize yourself with the concepts beforehand.
    • Attendees should also bring a laptop for the hands-on component. 

    Learning Objectives

    After this tutorial attendees will

    • Be able to log in and navigate the cluster
    • Know how to use modules and have a plan for installing any additional software needed
    • Be familiar with running jobs and requesting different types of resources

    Schedule

    This course is being run regularly this Summer; the same material will be covered in each instance of the class. We will update this page as dates are added.

    • Thursday, June 11: 1-3pm
    • Tuesday, June 23: 2-4pm
    • Thursday June 11: 1-3pm
    • Tuesday June 23: 2-4pm
    • Wednesday July 08: 10am-12pm
    • Wednesday July 22: 10am-12pm
    • Tuesday August 04: 2-4pm
    • Wednesday August 19: 10am-12pm

    Location

    On-campus at ORCD's offices in NE36

    How to Sign Up

    Sign up for any instance of the class by reserving a spot using this Calendly link. Only sessions offered within the next 30 days will be visible, if signing up for a later date please wait until closer to 30 days before the date to sign up.

  • Course Description

    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.

    Prerequisites/Requirements

    • Attendees should have minor programming experience in one of these languages: C, Fortran, Python, or Julia
    • Attendees should also bring a laptop.

    Learning Objectives

    • Concepts of parallel computing and knowledge of accelerating computer programs on an HPC cluster.
    • Parallel programming skills for CPU (OpenMP, MPI).
    • Parallel programming skills for GPU (CUDA, Pytorch, Deepspeed).

    Schedule

    The next session of this will run on Wednesday, July 22 and Thursday, July 23, 2026, from 10AM - 4PM.

    The topics on the two days are independent. Attendees can choose which session(s) they would like to attend.

    • Day One (Wednesday, July 22, 10AM - 4PM):
      • Parallel Programming with OpenMP
      • Distributed Computing with MPI
    • Day Two (Thursday, July 23, 10AM - 4PM):
      • GPU Programming with CUDA
      • Distributed Deep Learning

    Location

    On campus; room for upcoming sessions TBD.

    How to Sign Up

    To sign up, email the instructor, Shaohao Chen.

  • Course Description

    Retrieval-augmented generation (RAG) is a framework for enhancing the knowledge capabilities of a pre-trained large language model (LLM) by providing it with a set of documents to use as a ground source of truth. RAG allows people to utilize the abstract reasoning and summarizing capabilities of LLMs to gain insights on the information provided in a given set of documents. In this workshop, we will learn how to run RAG using the GPUs on the Engaging cluster, as well as tailor the pipeline to work with any set of documents.

    Prerequisities

    Required

    Recommended

    • Basic understanding of LLMs
    • Basic understanding of shell/bash commands
    • Experience submitting jobs on an HPC system

    Feel free to review the following before the workshop:

    Learning Objectives

    • Learning how to convert a set of documents into a vector store
    • Learning how to implement a RAG pipeline using provided code and make changes based on individual needs
    • Learning how to run LLM workloads on Engaging GPUs

    Schedule

    • Wednesday, June 24, 2-4PM

    Location

    On-campus at ORCD's offices in NE36

    How to Sign Up

    Register using this form.