CS 6240 — Large-Scale Parallel Data Processing
4 semester hoursGraduateLectureusually offered: fall, springBostonSeattle, WATraditional
Covers big-data analysis techniques that scale out with increasing number of compute nodes, e.g., for cloud computing. Emphasizes approaches for problem and data partitioning that distribute work effectively, while keeping total cost for computation and data transfer low. Studies and analyzes deterministic and random algorithms from a variety of domains, including graphs, data mining, linear algebra, and information retrieval in terms of their cost, scalability, and robustness against skew. Course work emphasizes hands-on programming experience with modern state-of-the-art big-data processing technology. Students who do not meet course prerequisites may seek permission of instructor.
Prerequisites
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 1 | 7 | 30 | 23% | 23.0 |
| Spring 2024 | 3 | 83 | 120 | 69% | 12.3 |
| Fall 2024 | 2 | 54 | 79 | 68% | 12.5 |
| Spring 2025 | 1 | 27 | 30 | 90% | 3.0 |
| Fall 2025 | 1 | 28 | 28 | 100% | 0.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 15% · T 43% · W 28% · Th 26% · F 4%
Common patterns: T (43% of sections), W (28% of sections), MR (15% of sections), R (11% of sections), F (4% of sections) — in patterns, R means Thursday
Professors
Fall
- Divya Chaudhary (56% of students) · reviews
- Mirek Riedewald (36% of students) · reviews
- Vishal Rajpal (8% of students) · reviews
Spring
- Mirek Riedewald (55% of students) · reviews
- Divya Chaudhary (45% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (CS course descriptions) · Student reviews on RateMyHusky · All CS courses · Plan it at numap.app