Fall 2026

High Performance Computing for Applications in Engineering

Covers hardware and software solutions for advanced computing in engineering, with hands-on parallel programming assignments.

offering recorded3 credits
Recorded instructors · Fall 2026 Tsung-Wei Huang

Summary

1 / 6

Historical reviews of Dan Negrut: Reviewers note the class involves significant work, particularly weekly programming assignments, but consider them doable with effort.

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
COMPSCI/ECE/EMA/EP/ME 759

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Graduate/professional standing

Professors

Fall 2026

Recent recorded grades — Fall 2024: 3.99 GPA, 100.0% A/AB (n=69 letter grades); Spring 2025: 3.98 GPA, 100.0% A/AB (n=112 letter grades).

Historical instructors & teaching patterns

Historical reviews of Dan Negrut: Tsung-Wei Huang is the current instructor, but all provided reviews describe Dan Negrut. Negrut is praised for his engaging lectures, deep knowledge of parallel computing, and lenient grading. Reviewers highlight his care for students and well-designed assignments.

DAN NEGRUT is recorded teaching in Fall 2013, Fall 2015, Fall 2017, Spring 2019, Spring 2020, Fall 2022, Fall 2023, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

TSUNG-WEI HUANG is recorded teaching in Fall 2024, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction55 / 1106
LEC 001Classroom Instruction54 / 1301
LEC 001Classroom Instruction1 / 1100
LEC 001Classroom Instruction5 / 1100
LEC 001Classroom Instruction4 / 1101

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

Student experience

the class

Historical reviews for Dan Negrut describe a well-made curriculum focused on High Performance Computing with CUDA and OpenMP, offering a great learning experience.

Recent recorded grades — Fall 2024: 3.99 GPA, 100.0% A/AB (n=69 letter grades); Spring 2025: 3.98 GPA, 100.0% A/AB (n=112 letter grades); Spring 2026: 3.97 GPA, 100.0% A/AB (n=102 letter grades).

difficulty & workload

Historical reviews of Dan Negrut: Reviewers note the class involves significant work, particularly weekly programming assignments, but consider them doable with effort.

Historical reviews of Dan Negrut: Students value the well-designed lectures and homework, noting the availability of recordings and opportunities to explore parallel computing in the final project.

Topics

  • Hardware and software solutions for advanced computing
  • Parallel computing programming languages
  • Emerging hardware architectures

Skills

  • Parallel computing programming using emerging hardware architectures.
  • Applying advanced computing solutions to engineering problems.

Grades

Latest available · Spring 2026— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1283 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
COMPSCI/ECE/EMA/EP/ME 759 · Fall 2026

High Performance Computing for Applications in Engineering

Recorded 2026-09-07
COMPSCI/ECE/EMA/EP/ME 759 · Fall 2026

High Performance Computing for Applications in Engineering

Recorded 2026-09-07
COMPSCI/ECE/EMA/EP/ME 759 · Fall 2026

High Performance Computing for Applications in Engineering

Recorded 2026-09-07
COMPSCI/ECE/EMA/EP/ME 759 · Fall 2026

High Performance Computing for Applications in Engineering

Recorded 2026-09-07
COMPSCI/ECE/EMA/EP/ME 759 · Fall 2026

High Performance Computing for Applications in Engineering

Recorded 2026-09-07
Raw records 
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Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1Download part 2Download part 3
Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "71ad01e1772a5dc2d550a582317fe78ccaee20c37738e54a5484648f6ab85df7",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
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}