5449: Introduction to High-Performance Deep/Machine Learning

Instructors: Prof. Dhabaleswar K. (DK) Panda and Prof. Hari Subramoni
Autumn 2023

Course Number: 5449

Class Number: 36643 (Grad) and 38406 (Undergrad)

Pre-Requisite: 2431 or 3430; and 3521 or 5521; or Grad standing.

Credits: 3

Course Time: TuTh 11:10 am - 12:30 pm

Classroom: Baker Systems 198

Course Description:

Recent advancements in Artificial Intelligence (AI), including Large Language Models (LLMs) and Chat GPT, have been fueled by the resurgence of Deep Neural Networks (DNNs); various Deep Learning (DL) frameworks like PyTorch, Tensorflow, and Chainer; various Machine Learning (ML) frameworks like K-means; and various data science frameworks like Dask. DNNs have found widespread applications in classical areas like Image Recognition, Speech Processing, Textual Analysis, as well as areas like Cancer Detection, Medical Imaging, Physics, Materials Science, and even Autonomous Vehicle systems. However, scaling distributed training with scale-up and scale-out approaches are still challenging. This is leading to the emergence of a new field called "High-Performance Deep/Machine Learning".

The objectives of this course are to understand the principles and the practice of this emerging trend, the open set of challenges, how modern HPC technologies can be used to accelerate DL/ML training and inferencing and apply these benefits to the real-world applications.

Topics to be Covered


Selected papers from the literature including papers focusing on past and on-going research activities in the group.

Laboratory Exercises:

The course will involve laboratory expercises for students to experiment with Deep/Machine Learning Frameworks. These exercises will be carried out on OSC (Ohio Supercomputing Center) clusters using CPUs and GPUs. This will provide hands-on knowledge to the students in the area of high-performance deep/machine learning.
Last Updated: June 17, 2023