Introduction/subMIT Project Overview

• Joshua Bendavid (Massachusetts Institute of Technology)

Room: Kolker Room (26-414)

Getting started on subMIT: Available Resources

• Zhangqier Wang

Room: Kolker Room (26-414) * Computational Resources * Documentation: Users Guide, GitHub Examples * Support: Help Desk & Chat Bot

Getting started on subMIT: How to Interact with subMIT

• Matthew Heine (Massachusetts Institute of Technology)

Room: Kolker Room (26-414) * ssh / terminal * JupyterHub * X2Go * Visual Studio Code (Remote Development) * 1-minute summary of batch jobs (see also later tutorial)

Getting started on subMIT: Installing / Managing Software

• Chad Freer (Massachusetts Institute of Technology)

Room: Kolker Room (26-414) * native environment * installing software with conda * software environment management tools * containers overview

Navigating the recent Linux Upgrade

• Marianne Moore (MIT)

Room: Kolker Room (26-414) In this session we will outline key changes in the migration to the new version of Linux as well as how to access the older version via containers. We are also setting aside a portion of time to answer user questions about the upgrade, so please bring any questions you have, general or specific.

Experience thus far running a multi-experiment OpenShift Cluster at FNAL

• Lindsey Gray (Fermi National Accelerator Laboratory)

Room: Kolker Room (26-414)

Tutorial: Batch Job / Workflow Management: SLURM & HTCondor

• Chad Freer (Massachusetts Institute of Technology)

Room: Kolker Room (26-414) This session will show how to use the batch schedulers/resource-managers SLURM & HTCondor to manage your computational tasks, distributing them across the shared resources. Depending on your workflow, this may provide an easy way to run your calculations in parallel, shortening time-to-result and eliminating some manual tasks.

Tutorial: GPUs: Introduction to Likelihood-Free Inference with PyTorch Lightning

• Deep Chatterjee (Massachusetts Institute of Technology)

Room: Kolker Room (26-414) This tutorial will give an overview of using pytorch lightning for building and training neural networks. A simple problem in inference: measuring the parameters of a line will be presented. The tutorial will also introduce distributed training with pytorch lightning

Parameter Estimation of Un-modeled Gravitational-wave Signals using Likelihood-free Inference

• Deep Chatterjee (Massachusetts Institute of Technology)

Room: Kolker Room (26-414)

DM21cm: a GPU-accelerated simulation of dark matter energy injection in 21cm cosmology

• Yitian Sun (Massachusetts Institute of Technology)

Room: Kolker Room (26-414)

Embarrassingly parallel ray-tracing for the Arcus X-ray spectrometer

• Hans Guenther (Massachusetts Institute of Technology)

Room: Kolker Room (26-414)

Employing snakemake to perform measurements with the LHCb experiment at CERN

• Blaise Delaney (Massachusetts Institute of Technology)

Room: Kolker Room (26-414)

Tutorial: Introduction to the Snakemake workflow manager

• Blaise Delaney (Massachusetts Institute of Technology)

Room: Kolker Room (26-414)

CMS Analysis on subMIT with crab and dask jobqueue

• Simon Rothman (Massachusetts Institute of Technology)

Room: Kolker Room (26-414)

Adaptive Brillouin-zone integration for optical conductivity

• Lorenzo Van Munoz (Massachusetts Institute of Technology)

Room: Kolker Room (26-414)

Axion and high-frequency gravitational wave searches with ABRACADABRA and DMRadio

• Kaliroe Pappas (MIT laboratory for nuclear science)

Room: Kolker Room (26-414)

Training spiking neural networks via adjoint sensitivity analysis

• Enrique Toloza (Massachusetts Institute of Technology)

Room: Kolker Room (26-414)

ML for Science: Best Practices

• Denis Boyda (Massachusetts Institute of Technology)

Room: Kolker Room (26-414)

Open Discussion & Closing Remarks Room: Kolker Room (26-414)