Bridges-2 Webinar Series

2024 Webinars

Upcoming webinars

Utilizing Bridges-2 for State-of-the-art Open Source Large Language Models

Date: May 24, 2024, 1 pm – 2 pm Eastern time

This webinar will demonstrate how to deploy DL applications with popular large language models, including creating environments, running Jupyter notebooks, and enabling fine-tuning/inference with single or multiple GPUs.

Past webinars

A Hands-On Introduction to Quantum Computing with NVIDIA’s CUDA Quantum

Date: April 19, 2024, 1 pm – 2 pm Eastern time

In this hands-on workshop, we will explore the fundamentals of quantum computing and apply them to construct quantum applications. The workshop will also introduce tools for interfacing quantum and classical components through a commonly used machine learning workflow.

2023 Webinars

Parallel Physics-informed Neural Networks via Domain Decomposition

Date: January 25, 2023, 1 pm – 2 pm Eastern time

During this webinar, a team from Brown University presented a parallel algorithm for conservative PINNs and extended PINNs constructed with a hybrid programming model described by MPI + X, where X ∈{CPUs ,GPUs}.

Multi-GPU MuST

Date: April 19, 2023, 1 pm – 2 pm Eastern time

The MuST package is computational software designed for ab-initio electronic structure calculations for solids. This webinar will  describe how to accelerate electronic structure calculations using MuST on multiple GPUs on Bridges-2.

GROMACS on Bridges-2: Optimizing Job Scripts for Performance and Efficiency

Date: June 9, 2 pm – 3 pm Eastern time

This webinar will discuss the run-time options that can affect the performance of a GROMACS job on Bridges-2.

Data-driven computational pipelines

Date: June 26, 2023, 2 pm – 3 pm Eastern time

This webinar reviews frameworks which can empower researchers to build robust and scalable data-driven computational pipelines on Bridges-2.

Utilizing Bridges-2 for Deep Learning Distributed Training

Date: September 26, 2023, 1 pm – 2 pm Eastern time

We will show how to deploy multi-GPU training for deep learning applications, such as with Pytorch and Tensorflow frameworks, using Bridges-2. Topics include how to modify code to run training on a single node or multiple nodes, how to set up an environment, how to run jobs in either interactive or batch mode or with a Jupyter notebook, and factors that affect performance.