Distributed Systems
COMPSCI 739 explores designing and implementing distributed systems, covering fault tolerance, scalability, replication, distributed storage, consensus, reliability, performance, and correctness.
Summary
Historical reviews of Michael Swift: Students must write numerous paper reviews and synthesize exam answers beyond lecture material, requiring substantial preparation in databases, networks, and operating systems.
Grade history
↗letter grades
All recorded terms · compare terms & instructors
Prerequisites
Course mapCOMP SCI 736, 744, 764, or 774
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
Professors
Fall 2026No instructors recorded for this selection.
Historical instructors & teaching patterns
Historical reviews of Michael Swift: Michael Swift is an energetic instructor who makes students contrast systems and write many paper reviews. His grading is described as tough and meticulous, requiring students to draw independent conclusions beyond lecture material.
MICHAEL SWIFT is recorded teaching in Spring 2010, Spring 2011, Spring 2012, Fall 2014, Fall 2019, Fall 2024. 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 2026Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Historical reviews of Michael Swift: Michael Swift delivers engaging lectures on distributed systems but imposes a significantly higher workload than other graduate courses, making the class unsuitable for undergraduates without strong prior knowledge.
Recent recorded grades — Fall 2024: 3.90 GPA, 100.0% A/AB (n=35 letter grades); Spring 2025: 3.88 GPA, 95.8% A/AB (n=24 letter grades); Spring 2026: 3.80 GPA, 95.0% A/AB (n=40 letter grades).
difficulty & workload
Historical reviews of Michael Swift: Students must write numerous paper reviews and synthesize exam answers beyond lecture material, requiring substantial preparation in databases, networks, and operating systems.
Historical reviews of Michael Swift: Reviewers describe the grading as tough and meticulous, requiring careful attention to detail in submissions.
Topics
Skills
Grades
Latest available · Spring 2026— not enough history to project Fall 2026.
Grade distribution · % of letter grades
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
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.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "aa584f73992c0d5d1aaeff32fe826dc135cc1b6dcaccd7abfd5e72feb635533a",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}