CSE 5442
Transcript Abbreviation:
SYS/AI: HiDL
Course Description:
This course combines high performance computing (HPC) and artificial intelligence (AI). This emerging trend combines the principles and practices of distributed training, which is critical for the success of both deep learning and machine learning disciplines.
Course Levels:
Undergraduate (1000-5000 level)
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
(N/A)
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Pre-req: 2431 or 3430; and 3521 or 5521; or Grad standing.
Electronically Enforced:
Yes
Exclusions:
(N/A)
Course Goals / Objectives:
Master the principles of deep/machine learning
Master the implications of different ways of using high-performance computing (HPC) systems for scale-up and scale-out of deep/machine learning algorithms
Master the different methods of performing distributed deep/machine learning parallelism techniques (data, model, spatial, layer, hybrid etc)
Be familiar with the architectural designs of past and present (state-of-the-art) high-performance computer systems
Be familiar with analyzing and solving AI problems using deep/machine learning algorithms
Be exposed to emerging trends in high-performance computing architectures for deep/machine learning
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Overview | 3 | 0 | 0 | 0 | 0 |
| Deep Learning Frameworks | 3 | 0 | 0 | 0 | 0 |
| Introduction to HPC Technologies | 6 | 0 | 0 | 0 | 0 |
| Overview of the state-of-the-art DL Models | 3 | 0 | 0 | 0 | 0 |
| Data Parallel DNN Training using HPC Environment | 11 | 0 | 0 | 0 | 0 |
| Model Parallel DNN Training using HPC Environments | 5 | 0 | 0 | 0 | 0 |
| Advanced Parallelization Strategies | 6 | 0 | 0 | 0 | 0 |
| Total | 37 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework (1-2) | 10% |
| Labs (2-3) | 30% |
| Midterm | 25% |
| Final Exam | 35% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| No Textbooks and Other Course Materials Entered. | ||
ABET-CAC Criterion 3 Outcomes:
| Significant contribution (7+ hours) | 1 | Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions. |
| Significant contribution (7+ hours) | 2 | Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline. |
| Some contribution (1-2 hours) | 3 | Communicate effectively in a variety of professional contexts. |
| Significant contribution (7+ hours) | 4 | Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles |
| Substantial contribution (3-6 hours) | 5 | Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline |
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Significant contribution (7+ hours) | 1 | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| Significant contribution (7+ hours) | 2 | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
| Some contribution (1-2 hours) | 3 | an ability to communicate effectively with a range of audiences - pre-2019 EAC SLO (g) |
| Significant contribution (7+ hours) | 4 | an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
| Substantial contribution (3-6 hours) | 5 | an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
| Substantial contribution (3-6 hours) | 6 | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
| Significant contribution (7+ hours) | 7 | an ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Embedded Literacies Info: