Oregon State CS 475: Introduction to Parallel Programming
CS 475 covers parallel programming (multicore concepts, OpenMP, SIMD, GPU programming, and the measurement discipline of speedup and efficiency) through a steady drumbeat of hands-on projects. It's a popular postbacc elective with a structure students consistently praise: clear projects, weekly rhythm, real performance numbers.
Fennie is independent and not affiliated with Oregon State University. This is an unofficial study guide.
What makes it hard
Parallel thinking inverts sequential habits: race conditions, false sharing, and synchronization bugs don't crash; they produce wrong numbers or quiet slowdowns, which makes correctness a measurement problem. The weekly project cadence is forgiving per project but relentless in aggregate, and benchmark write-ups demand that you interpret performance data rather than merely generate it.
What you'll cover
- • Multicore and threading concepts
- • OpenMP
- • Race conditions and synchronization
- • SIMD and vectorization
- • GPU programming basics
- • Speedup, efficiency, and Amdahl's law
The CS 475 study guide
How to study for Oregon State CS 475, step by step.
- 1
Keep the weekly project rhythm sacred
CS 475 is a cadence course: each project is modest, but missing one week means two due at once on a quarter clock. Protect a fixed project block in your week.
- 2
Graph every benchmark and explain its shape
The write-ups grade interpretation: why speedup flattens, where Amdahl bites, what the cache is doing. Practice reading your own performance curves, not only producing them.
- 3
Hunt race conditions by reasoning, not reruns
Parallel bugs are nondeterministic, so test-until-it-passes proves nothing. For every shared variable, write down who reads, who writes, and what protects it.
- 4
Master Amdahl's law numerically
Speedup and efficiency calculations are exam reliables and intuition anchors. Work them until predicting a program's ceiling is quick arithmetic.
Today
Today's CS 475 plan
What a Fennie Daily Plan looks like for CS 475. Yours is built from your own syllabus and adapts every day to your deadlines and progress.
First plan free, no card required. Fennie is independent and unaffiliated with your school.
FAQ
Is CS 475 a good elective in the OSU postbacc?
It's one of the most consistently recommended: structured weekly projects, concrete performance results, and skills that read well for systems and backend roles. The catch is pure cadence. It rewards steady weeks and punishes catch-up attempts.
How much C/C++ does CS 475 need?
Comfortable C-level programming (arrays, pointers, compilation flags), since OpenMP work lives there. Students fresh from CS 374's C find the transition smooth; anyone rusty should warm up before the first project week.
What's the workload like in CS 475?
A project nearly every week, each individually modest, plus benchmark write-ups. Around a full-time job the aggregate is real but predictable; the students who struggle are the ones who let one quiet week become a two-project pileup.
More Oregon State courses
CS 161: Introduction to Computer Science I
CS 161 is Oregon State's first programming course: variables, control flow, functions, basic data structures, and program design, currently taught in Python after the curriculum moved away from C++. It opens both the Corvallis CS degree and the Ecampus postbacc, where for many students it's the first code they've ever written.
CS 162: Introduction to Computer Science II
CS 162 continues the intro sequence with object-oriented programming, recursion, basic data structures, and significantly larger programs, taught in Python like CS 161. It carries a reputation as the Ecampus program's first real filter: the course where assignment scope jumps and time management becomes the curriculum.
CS 225: Discrete Structures in Computer Science
CS 225 is the CS department's discrete math course, covering logic, proofs, sets, functions, combinatorics, and graphs. It's required in the Ecampus postbacc and a prerequisite mindset for CS 325. For career changers from non-quantitative fields, it's often the first proof-based math they've ever faced.
CS 261: Data Structures
CS 261 covers the core data structures (dynamic arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs) with implementation assignments and complexity analysis throughout. In the Ecampus postbacc it's taught in Python (older blog posts reference the earlier C version), and it's the technical-interview foundation for the whole program.